STATE OF ALASKA BEFORE THE REGULATORY COMMISSION OF ALASKA · 15 o RHB-1, my resume. 16 o RHB-2, a...

PREFILED DIRECT TESTIMONY OF RONALD H. BROWN Docket No. U-14-_____: September 5, 2014 of 17

STATE OF ALASKA

BEFORE THE REGULATORY COMMISSION OF ALASKA Before Commissioners: Robert M. Pickett, Chairman Paul F. Lisankie T.W. Patch Norman Rokeberg Janis W. Wilson In the Matter of the Consideration of the Revenue Requirement Designated as TA 262-4 Filed by ENSTAR NATURAL GAS COMPANY, A DIVISION OF SEMCO ENERGY, INC.

) ) ) ) )

Docket No. U-14-____

PREFILED DIRECT TESTIMONY OF

RONALD H. BROWN

Q. Please state your name, business address, and present position. 1

A. My full name is Ronald Howard Brown, Ph.D. I am an Associate Professor of Electrical 2

and Computer Engineering and Director of The GasDay Project, both at Marquette 3

University (“Marquette”). My business address is Olin Engineering 534, 1515 W. 4

Wisconsin Avenue, Milwaukee, Wisconsin, 53233 or Olin Engineering 534, P.O. Box 5

1881, Milwaukee, Wisconsin, 53201-1881. I am doing this work as an independent 6

consultant; Marquette has authorized my participation as a witness in this proceeding. 7

Q. On whose behalf are you submitting testimony? 8

A. I am appearing on behalf of ENSTAR Natural Gas Company and Alaska Pipeline 9

Company (to which I will refer collectively as “ENSTAR” or the Company”). 10

Q. Please describe your educational background. 11

A. I have a B.S. in Electrical Engineering from the University of Illinois at Urbana-12


Champaign (May 1976) and a M.S. in Electrical Engineering from the University of 1

Wisconsin-Madison (December 1977). I received my Ph.D. in Electrical Engineering 2

from the University of Illinois at Urbana-Champaign in the area of systems and controls 3

(May 1986). 4

Q. Please describe your professional experience and credentials. 5

A. I have been on the faculty at Marquette since August 1985. I was promoted to 6

Associate Professor with tenure in 1993. Also in 1993, I founded a research project that 7

has come to be known as The GasDay Project. Please see my resume, attached as 8

RHB-1, for additional details on my publications and presentations. 9

Q. What is The GasDay Project? What services does it provide for companies like 10

ENSTAR? 11

A. The GasDay Project is a research project, a student centered learning activity, and the 12

largest technology transfer center at Marquette. The GasDay Project has grown 13

considerably since 1993 and now comprises a total of five faculty members, including 14

myself, two from Computer Engineering, and two from Applied Economics. The 15

GasDay Project also has three full-time employees, nine graduate students, and 16

approximately 20 undergraduates from Marquette’s Colleges of Engineering, Business 17

Administration, and Arts and Sciences. 18

GasDay is used daily at 30+ natural gas utilities in the United States to forecast 19

daily natural gas demand for 150+ different customer bases, accounting for about 20% 20

of the nation’s residential, commercial, and industrial natural gas usage. GasDay works 21

with local distribution companies (“LDC”s), like ENSTAR, providing products and 22

services. GasDay’s flagship service is a rolling eight-day natural gas demand forecast; 23


GasDay can also provide hourly, monthly, and annual forecasts. In addition to 1

GasDay’s demand forecasting service, GasDay also performs Load Growth and Design 2

Day Studies, offers a measurement outlier detection service, and can provide other 3

services. We also perform custom studies and research. 4

Below is a list of just some of our partner LDCs: Pacific Gas & Electric in San 5

Francisco, CA; Oklahoma Natural Gas in Oklahoma City, OK; WeEnergies in 6

Milwaukee, WI; SEMCO Energy Gas Company in Port Huron, MI; Central Hudson 7

Gas & Electric in Poughkeepsie, NY; New Mexico Gas Company in Albuquerque, NM; 8

Piedmont Natural Gas in Charlotte, NC; City Utilities of Springfield in Springfield, 9

MO; Texas Gas Service in Austin, TX; and MidAmerican Energy in Des Moines, IA. 10

Q. Please describe GasDay’s work with ENSTAR. 11

A. ENSTAR has licensed technologies and services from The GasDay Project since 2009. 12

It began by licensing the GasDay product, our rolling eight-day natural gas demand 13

forecast service, and ENSTAR relies on this product as an indicator of its customers’ 14

gas demand every day. The GasDay Project retrains GasDay’s mathematical models 15

and upgrades the GasDay software for ENSTAR every fall; we will upgrade ENSTAR’s 16

GasDay installation for the sixth time in fall 2014. The weather station weighting 17

currently in practice discussed below was updated as the result of a Weather Station 18

Optimization study we performed in 2010. We performed Load Growth and Design 19

Day Studies for ENSTAR twice, in 2010 and in 2012. ENSTAR licensed GasYear in 20

2013; at ENSTAR’s request, we extended GasYear’s mathematical engine to forecast 21

out six years. We have met with ENSTAR personnel in Anchorage multiple times, 22

Daniel Dieckgraeff met with us in Milwaukee, and ENSTAR personnel have attended 23


our GasDay Users Group Meetings. We have also met and talked at conferences. 1

Q. Have you testified before the Commission in other dockets? 2

A. I have not testified before any regulatory commissions. However, I have assisted 3

natural gas utilities in preparing testimony, and my work has been incorporated into 4

testimony before state regulatory authorities in California, Michigan, Illinois, 5

Oklahoma, New York, New Mexico, South Carolina, and Iowa. Additionally, I assisted 6

natural gas utilities in New Mexico and Texas in preparing testimony before the Federal 7

Energy Regulatory Commission following the interruption of service during the 8

extreme cold event of February 2011. 9

Q. What is the purpose of your prefiled testimony? 10

A. I review and analyze ENSTAR’s past customer gas consumption (called “use-per-11

customer”) in order to identify trends and predict use-per-customer levels going 12

forward. 13

Q. Why is this important? 14

A. As described in Mr. Dieckgraeff’s testimony, my data and findings are foundational to 15

the Company’s proposed adjustments to its billing determinants. 16

Q. How is your testimony organized? 17

A. First, I will present ENSTAR’s historical and forecasted weather-normalized use-per-18

customer amounts by customer class. Second, I will discuss the identified trends 19

inherent in the data. Third, I will explain the methods used to derive these weather-20

normalized use-per-customer amounts and identify trends. Fourth, I will describe why 21

these findings are reasonable. These calculations are based on methodologies that we 22


have developed, which has been published, and which are now accepted best practices 1

by the natural gas forecasting community. 2

Q. Please summarize your key findings. 3

A. ENSTAR has been experiencing weather-normalized use per customer declines in all of 4

its general service rate classifications and this trend is exepcted to continue over the 5

period ENSTAR’s rates fromt his proceeding will be in effect. This continued declining 6

use-per-customer supports making certain adjustments to ENSTAR’s billing 7

determinants as disucssed below and in Mr. Dieckgraeff’s testimony. 8

Q. Which exhibits are you sponsoring? 9

A. My analyses and conclusions are supported by the data in Exhibits RHB-1-6, which 10

were prepared by me or under my direction. 11

Q. Describe the source of the information contained on these exhibits. 12

A. I provide detailed descriptions of these exhibits in my testimony below. Here I describe 13

them briefly: 14

o RHB-1, my resume. 15

o RHB-2, a table showing the annual weather-normalized use-per-customer 16

amounts by year and the percentage changes for all seven customer classes. 17

o RHB-3, charts showing the actual annual use-per-customer amounts, the 18

historical weather-normalized use-per-customer amounts, and the forecasted 19

weather-normalized use-per-customer amounts. 20

o RHB-4, charts illustrating the rational for not use the data prior to November 21

2005 on Residential Customer Class Code R3. 22


o RHB-5, charts illustrating the rational for not use the data prior to November 1

2005 on Commercial Customer Class Code C4. 2

o RHB-6, charts showing historical daily temperatures and annual heating degree 3

days. 4

ENSTAR’S HISTORICAL AND FORECASTED WEATHER-NORMALIZED 5 USE-PER-CUSTOMER AMOUNTS 6

7 Q. Which customer classes did you use to calculate the historical and forecasted 8

weather-normalized use-per-customer amounts? 9

A. I have prepared historical and forecasted weather-normalized use-per-customer amounts 10

on seven customer classes; three residential customer class codes, R1, R2, and R3, and 11

four commercial class codes, C1, C2, C3, and C4.1 By “weather normalized” amounts, 12

for forecasting purposes, I mean the amount of natural gas that will be consumed, 13

assuming normal weather conditions occur. Natural gas is primarily used for space 14

heating; as a result, ENSTAR’s customers’ actual use is highly weather-dependent. 15

Since actual annual weather is inevitably colder or warmer than a “normal” year, 16

customers’ actual use will be higher or lower than what we would expect had “normal” 17

weather occurred. For historical purposes, to remove the impact of annual weather 18

aberrations on natural gas usage, we build mathematical models and calculate what the 19

natural gas demand would have been should normal weather have occurred. This is 20

known as the weather-normalized use. To forecast future weather-normalized use, we 21

1 ENSTAR’s tariff includes rates for G1-G4 customers, and does not differentiate between commercial

and residential customers. For internal customer service reasons, ENSTAR distinguishes between these two. The classes of customers listed herein are consistent with the G1-G4 rate classes. In other words, both “R1” and “C1” customers are “G1” customers under ENSTAR’s tariff.


build another set of models that capture the historical trends and extrapolate these 1

characteristics into the future using normal weather. 2

Q. How are the results for the historical and forecasted weather-normalized use-per-3

customer amounts presented? 4

A. The use-per-customer amounts on the seven customer classes are all shown in tabular 5

form in Exhibit RHB-2 and individually shown in graphical form in Exhibit RHB-3. In 6

Exhibit RHB-3, the historical actual (not weather normalized) use-per-customer 7

amounts are shown in blue. We calculated the historical weather-normalized use-per-8

customer amounts. These are shown in Exhibit RHB-2 for 2004 through 2013 (10 9

years) and with the green trace in each of the charts in Exhibit RHB-3. Next, we 10

forecasted weather-normalized use-per-customer. These are shown in tabular form in 11

Exhibit RHB-2 for 2014 through 2019 (6 years) and with the red trace in each of the 12

charts in Exhibit RHB-3. 13

Exhibit RHB-2 also shows the year-over-year percent change in the weather-14

normalized use-per-customer amounts for each customer class. 15

IDENTIFIED TRENDS INHERENT IN THE DATA 16

Q. What are the general trends inherent in the data? 17

A. Consistent with what we and others have observed nationally over the past 30 or 40 18

years, each of ENSTAR’s customer classes are showing declining weather-normalized 19

use-per-customer amounts.2 This is due primarily to higher efficiency equipment, such 20

as furnaces and hot water heaters; higher market penetration of higher efficiency 21

equipment; newer homes and other buildings being constructed with higher energy 22

2 American Gas Association, “Natural Gas Utilities and Their Customers: Efficient. Naturally.”

http://www.aga.org/our-issues/energyefficiency/Natural-Gas-Utilities-Customers/Pages/default.aspx


efficiency; and existing homes and other buildings being retrofitted for higher energy 1

efficiency. 2

In the past two years, I have performed Load Growth and Design Day Studies 3

for local distribution companies in California, New Mexico, Oklahoma, South Dakoda, 4

Nebraska, Iowa, Illinois, Michigan, Texas, Tennessee, Louisiana, and Pennsylvania. I 5

have seen a generally declining usage pattern in all of these states. 6

Q. What are historical and forecasted weather-normalized use-per-customer amounts 7

and what are the identified trends inherent in the data for each of the customer 8

classes? 9

A. Residential Customer Class R1: As shown in Exhibits RHB-2 and RHB-3 page 1, the 10

declining weather-normalized use-per-customer amount for Residential Customer Class 11

R1 has been fairly consistent for the past 17 years. The decline did slow in the 2001-12

2004 time frame, then accelerated from 2005 to 2011, then slowed in 2012, with a more 13

typical decline in 2013. The average decline since 2004 is −1.7% per year with a 14

smaller average decline of −1.4% per year the last five years, for a total of −6.7% over 15

the last five years. The modeled forecasted weather-normalized use-per-customer 16

amounts over the next six years shows an average decline of −1.2% per year. The five-17

year forecast shows a decline of −5.9%. 18

Residential Customer Class R2: Exhibits RHB-2 and RHB-3 page 2 show the 19

weather-normalized use-per-customer amount for Residential Customer Class R2. The 20

declining weather-normalized use-per-customer rate is more constant than for R1. The 21

average decline since 2004 is −1.4% per year with an average decline of −1.4% per year 22

for the last five years, or −6.8% over the last five years. The modeled forecasted 23


weather-normalized use-per-customer amounts over the next six years show an average 1

decline of −1.0% per year. The five-year forecast has a decline of −5.0% 2

Residential Customer Class R3: Exhibits RHB-2 and RHB-3 page 3 show the 3

weather-normalized use-per-customer amount for Residential Customer Class R3. The 4

first full year of this dataset is 2006. The declining weather-normalized use-per-5

customer rate was greater in the first few years and slowed over the last four years. The 6

average decline is −0.8% per year for the last five years, or −4.0% over the last five 7

years. The modeled forecasted weather-normalized use-per-customer amounts over the 8

next six years show an average decline of −0.5% per year. The five-year forecast shows 9

a decline of −2.4% 10

Commercial Customer Class C1: Exhibits RHB-2 and RHB-3 page 4 show the 11

weather-normalized use-per-customer amount for Commercial Customer Class C1. C1 12

has been consistently declining since 2001, with the highest rate of decline in the 2005 13

to 2009 time frame. It is still declining, but at a slower rate over the last four years. 14

The average decline is −1.2% per year over the last five years, or −5.7% over the last 15

five years. The modeled forecasted weather-normalized use-per-customer amounts over 16

the next six years show an average decline of −1.1% per year. The five-year forecast 17

shows a decline of −5.4%. 18


weather-normalized use-per-customer amount for Commercial Customer Class C2. The 20

historical weather-normalized use-per-customer amounts for Commercial Customer 21

Class C2 have been variable with a general declining trend, but increasing some years; 22

in particular, usage increased in 2000, 2004, and 2012. The amount in 2013 is the same 23


as 2012. The average decline is −0.8% per year over the last five years, or −3.8% over 1

the last five years. The modeled forecasted weather-normalized use-per-customer 2

amounts over the next six years show an average decline of −0.5% per year. The five-3

year forecast shows a decline of −2.4% 4



weather-normalized use-per-customer was relatively flat until 2005, when it started 7

decreasing, with a slight uptick in 2012. The rate of decline was greater in 2007 to 8

2009 than in recent years. The average decline is −0.9% per year over the last five 9

years, or −4.5% over the last five years. The modeled forecasted weather-normalized 10

use-per-customer amounts over the next six years show an average decline of −0.5% per 11

year. The five-year forecast shows a decline of −2.4% 12



first full year of this dataset is 2006. The rate of decline was greater in 2007 to 2009 15

than in recent years. The average decline is −0.7% per year over the last five years, or 16

−3.5% over the last five years. The modeled forecasted weather-normalized use-per-17

customer amounts over the next six years show an average decline of −0.8% per year. 18

The five-year forecast has a decline of −3.8% 19

20

21

22

METHODS TO DERIVE WEATHER-NORMALIZED USE-PER-CUSTOMER 23 AMOUNTS AND IDENTIFIED TRENDS 24


1

Q. How does GasDay ascertain these weather-normalized use-per-customer amounts 2

and the identified trends? Describe the Forecast Methodology, the processes used, 3

and the work done. 4

A. To calculate these amounts, I first analyzed historical consumption data and historical 5

weather data. From the historical weather, I calculated normal weather. Then, to 6

validate the historical consumption and weather data, I built models to calculate the 7

historical weather-normalized use-per-customer amounts. Using this information, I 8

built models to forecast future weather-normalized use-per-customer amounts. 9

Q. Please describe the historical data used to estimate these amounts. 10

A. The data used for building models to estimate the weather-normalized use-per-customer 11

amounts was gathered from historical billing data. ENSTAR provided us with 12

approximately 24 million individual meter reading records. Each of these records 13

contains a customer identifier, the date of the meter reading, the number of days since 14

the last reading, and the amount of gas consumed since the last reading. The times 15

between meter readings are typically about a month. The dates of these records start in 16

1996 and continue through the first few months of 2014. Efforts were made to identify 17

and correct anomalous records, such as records with overlapping dates or gaps in the 18

billing data for individual billing locations, incorrect dates, trailing 0 readings for 19

locations where the account is closed but the meter is still being read (inflates number 20

of customers if not removed), imputed apparently missing single month billing records, 21

etc. From these 24 million records, we built daily datasets by customer class that 22

included the dates, the disaggregated daily amounts of gas consumed, and the daily 23

number of customers. To produce historical (actual) use-per-customer data by customer 24


class, we divided the daily customer class usage by the daily number of customers. We 1

then used our standard GasDay outlier detection and replacement algorithm to replace 2

obvious outliers. 3

Q. Did you take further steps to prepare these datasets? 4

A. Yes, as follows: 5

1. There were very few Residential Customer Class Code R4 customers. We 6

combined these customers into Residential Customer Class Code R3. 7

2. The Residential Customer Class Code R3 dataset had unusual characteristics. 8

During 2005, the number of R3 customers jumped from 170 to 270. The R3 9

weather-normalized use-per-customer amounts also jumped at that time with the 10

additional customers, meaning the added customers on average were higher volume 11

customers than the average R3 customer prior to 2005. Daniel Dieckgraeff 12

explained this was due to changes at the Elmendorf and Fort Richardson military 13

bases. To use a consistent dataset going forward, I recommended eliminating data 14

prior to November 2005. Charts showing these trends are in Exhibit RHB-4. 15

3. The Commercial Customer Class Code C4 dataset had unusual characteristics. Prior 16

to 2005, the number of customers increased at a rate of about 17 customers per year. 17

In 2005 and 2006, this rate increased at a much higher rate, 69 customers were 18

added in 2005 and 41 new customers were added in 2006. Following 2006, the rate 19

of increase in the number of C4 customers returned to pre-2005 levels. The 20

customers added in 2005 greatly increased the weather-normalized use-per-21

customer amounts. Mr. Dieckgraeff attributed this, as well, to changes at the 22

Elmendorf and Fort Richardson military bases. The increase in the number of 23


customers in 2006 did not adversely impact the weather-normalized use-per-1

customer amounts. To use a consistent dataset going forward, I recommended 2

eliminating the data prior to November 2005. Charts showing these trends are in 3

Exhibit RHB-5. 4

Q. Please describe the weather data used to estimate these amounts. 5

A. When identifying variables to model natural gas usage, weather is the most important 6

factor. When GasDay first started working with ENSTAR in 2009, we looked at using 7

several weather stations in and around ENSTAR’s service territory. 8

We typically try to acquire hourly weather data from NOAA (Integrated Surface 9

Hourly (“ISH”) data) for all weather stations used by the LDC. NOAA has data for 10

many stations beginning January 1, 1973. 11

We compute a daily weather data set from this history of hourly data aligned to 12

the gas day local to the regions being analyzed—in ENSTAR’s case, midnight to 13

midnight. We have found that the average of the 24 hourly temperatures produces more 14

accurate natural gas forecasting models than the average of the high and low 15

temperatures. 16

Using numerical optimization techniques, we determined that the best weather 17

station for ENSTAR is a weighted combination of 34% Anchorage (“PANC”), 10% 18

Kenai (“PAEN”), 12% Palmer (“PAAQ”), and 43% Merrill Field (“PAMR”). 19

We compute daily wind-adjusted heating degree days (“HDDW”) using the 20

following formula: 21

7280

, 8


152160

, 8

We find that adjusting heating degree days (“HDD”) for wind produces superior 1

models. Exhibit RHB-6 contains two charts. The first chart shows the wind-adjusted 2

daily average temperature since 1973, with the years superimposed. The 20-year 3

normal wind adjusted daily average temperature is also shown (the green line). 4

Q. Please discuss how you determined “normal weather”. 5

A. The “normal weather” used in my analysis was calculated from the 20 years of daily 6

weather data—from 1994 to 2013—from the four weather stations. The historical 7

weather was combined using the weighting described above, and then averaged. This is 8

how I calculated the 20-year normal wind-adjusted daily average temperature shown on 9

the first chart of Exhibit RHB-6. The second chart of Exhibit RHB-6 shows the annual 10

heating degree days by year as blue bars. The green line with circles represents the 20-11

year normal annual heating degree days. Normal annual heating degree days is 10,096 12

HDDs, increasing to 10,145 HDDs on leap years. The third page of Exhibit RHB-6 13

contains a table of the data in the second chart, along with HDDWs and percent colder 14

than normal (positive percentages) or percent warmer than normal (negative 15

percentages). 16

Q. Please discuss the method used to calculate the historical weather-normalized use-17

per-customer amounts. 18


A. To calculate the historical weather-normalized use-per-customer amounts, we used a 1

methodology that we presented at the 2010 International Symposium on Forecasting.3 2

Briefly, this approach builds a fourth order linear regression model on a year-long 3

window of daily data, which yields the four coefficients for the model. This model 4

estimates the daily natural gas demand. The daily mathematical model is 5

65 55 ∆

where represents the baseload, models the heatload with a 65°F reference 6

temperature, models the heatload with a 55°F reference temperature, and accounts 7

for impact on gas consumption based on the change in temperature from the prior day. 8

We use two heating degree day inputs in the model because this allows the model to 9

select the optimal reference temperature and the scatter plot of daily consumption 10

versus temperature does not have a sharp kink at 65°F but a gradual transition from 11

non-heating days to heating days, typically between 65°F and 55°F. We have shown 12

that this produces a more accurate model. 13

Briefly, we calculate the coefficients of this model multiple times, once for each 14

year of the historical data. Each year has different coefficients. The results is a fourth 15

order model with coefficients that change each year. This model identifies the changing 16

baseload and heatload components of the total load. 17

To obtain the historical weather-normalized use-per-customer amounts, we 18

evaluate this model with coefficients that vary with time on normal weather. This 19

produces the daily estimates of natural gas demand using normal weather. Since the 20

3 Ronald H. Brown, Y. Li, B. Pang, S. Vitullo, and G. Corliss, “Detrending Daily Natural Gas Demand

Data Using Domain Knowledge,” 30th International Symposium on Forecasting (San Diego, CA), June 20-23, 2010. http://forecasters.org/pdfs/isf/ISF10_Proceedings.pdf, pg. 10.


model is modeling a customer class use-per-customer amount, this is the weather-1

normalized use-per-customer. This model is weather-normalizing only the historical 2

heatloads; the historical baseloads, which also vary over time, are not changed by this 3

weather-normalizing process. 4

We repeat this process for each customer class to calculate the weather-5

normalized use-per-customer demand of each class. 6

Q. Please discuss the method used to forecast future weather-normalized use-per-7

customer amounts. 8

A. To forecast future weather-normalized use-per-customer amounts, I use a different 9

process than the one used to determine the historical weather-normalized use-per-10

customer amounts. To calculate what happened, we build mathematical models and 11

then look at its coefficients to determine the changes in the load. To forecast future 12

weather-normalized use-per-customer amounts, we need to model the changing load 13

and extrapolate these characteristics into the future. We rely on mathematical models 14

that can both account for the trends in the historical data and can extrapolate these 15

trends into the future. 16

I build a daily regression model that includes a linear trend term, which models 17

the historical change in the baseload, and a linear trend term crossed with heating 18

degree days adjusted for wind, which models the historical change in the heatload. 19

Since we want to evaluate this model to get estimated demand with forecast horizons 20

out six years, I choose a model without autoregressive terms. As a result, the model 21

does not require a forecast to generate a forecast. 22


For each of the customer classes, I parameterize two different regression 1

models. I evaluate both models on normal weather out through the end of 2019. I 2

average the forecast of the two models and aggregate the daily load forecasts up to 3

annual load forecasts. 4

REASONABLENESS OF FINDINGS 5

Q. Why are these weather-normalized use-per-customer amounts and the identified 6

trends findings reasonable? 7

A. The methods I used are based on well-established modeling practices.4 I validated the 8

models using diagnostic statistics such as the R-square Statistic, the Adjusted R-square 9

Statistic, the Root Mean Square Error, the t Statistics, etc. The forecasted weather-10

normalized use-per-customer amounts are similar to what we have seen elsewhere and 11

are consistent with the ENSTAR calculated historical weather-normalized use-per-12

customer amounts. 13

The American Gas Association states “Natural Gas use per customer has 14

decreased by about 1 percent per year for the last 38 years, which means that the 15

average residential customer today uses 39 percent less than they did 38 years ago.” 16

The findings presented in this testimony are consistent with the statement from The 17

American Gas Association. 18

Q. Does this conclude your prepared testimony? 19

A. Yes, it does. 20

4 See R. H. Brown, B. M. Marx, G. F. Corliss and T. F. Quinn, “Mathematical Models for Short Term

Natural Gas Demand Forecasting,” in American Gas Association Operating Section Proceedings 2007, Dallas, TX, April 2007; see also S. R. Vitullo, R. H. Brown, G. F. Corliss and B. M. Marx, “Mathematical Models for Natural Gas Forecasting,” The Canadian Applied Mathematics Quarterly, vol. 17, no. 4, pp. 807-822, Winter 2009. http://www.math.ualberta.ca/ami/CAMQ/table_of_content/vol_17/17_4i.htm.

Ronald H. Brown, Ph.D. 1

MarquetteUniversityCurriculumVitae

Name: Ronald H. Brown, Ph.D.

Address: Department of Electrical and Computer Engineering Marquette University 1515 West Wisconsin Avenue, Milwaukee, WI 53233 P.O. Box 1881, Milwaukee, WI 53201‐1881

DateandPlaceofBirth: February 9, 1954, Chicago, Illinois

Specialization: Systems and Controls; Intelligent Control; Adaptive Controls, Prediction, and Filtering; Artificial Neural Networks, Genetic Algorithms, Time‐series Modeling, Ensemble Modeling in Identification, Controls, and Predictions. Applications of systems and controls in distribution and transmission of natural gas and energy forecasting.

Education:

B.S. in Electrical Engineering, University of Illinois at Urbana‐Champaign, May 1976.

M.S. in Electrical Engineering, University of Wisconsin‐Madison, Thesis: “Electronic

Instrumentation for Bird Control,” Thesis advisor: Allan K. Scidmore, December 1977.

Ph.D. in Electrical Engineering, University of Illinois at Urbana‐Champaign, Dissertation: “Robust

Optimal Control of Step Motors by Lead Angle Selection,” Dissertation Advisor: Benjamin C.

Kuo, May 1986.

AcademicandScholarlyExperience:

Associate Chairperson, Department of Electrical and Computer Engineering, Marquette

University, Milwaukee, Wisconsin, May 1996‐August 1998.

Associate Professor of Electrical, Computer, and Biomedical Engineering, Marquette University,

Milwaukee, Wisconsin, August 1993‐present.

Director, The GasDay Project at Marquette University, 1993‐present.

Director, Center for Intelligent Systems, Marquette University, Milwaukee, Wisconsin, 1993‐

1998. Director, Center for Intelligent Systems, Controls, and Signal Processing, 1998‐present

Director, Intelligent Systems and Controls Laboratory, Marquette University, Milwaukee,

Wisconsin, 1987‐present.


Assistant Professor of Electrical, Computer, and Biomedical Engineering, Marquette University,

Milwaukee, Wisconsin, August 1985‐August 1993.

Teaching Assistant, Electrical Engineering, University of Illinois at Urbana‐Champaign, Sept.

1983‐May 1985.

Research Assistant, Control Systems Research Laboratory, University of Illinois at Urbana‐

Champaign, Nov. 1982‐Aug. 1985.

Project Engineer, Advanced Technology Development Department, Allen‐Bradley Co.,

Milwaukee, Wisconsin, 1978‐82.

Member Technical Staff, Bell Telephone Laboratories, Indianapolis, Indiana, 1978.

Teaching Assistant, Electrical and Computer Engineering, University of Wisconsin‐Madison, Sept.

1977‐Dec. 1977.

Research Assistant, Electrical and Computer Engineering, University of Wisconsin‐Madison, Sept.

1976‐Dec. 1977.

Consulting:

Warner Electric Clutch and Brake Company, Marengo, IL, 1983‐86.

Sporlan Valve Company, St. Louis, MO, 1985.

Rexnord, Corporate Research and Development Group, Milwaukee, WI, 1986.

Johnson Controls, Inc., Milwaukee, WI, 1986‐87, 90, 92, 95‐96.

Milwaukee Heart Research Foundation, Milwaukee, WI, 1986‐89.

Stone & Webster Management Consultants, Inc., Houston, TX, 1997‐2001.

Thirty plus natural gas utilities, 1993‐present.

ProfessionalSocietyMembershipsandOfficesHeld:

Institute of Electrical and Electronics Engineers (IEEE), 1972‐present.

IEEE Milwaukee Section Executive Committee, 1985‐1987.

IEEE Milwaukee Chapter of the IEEE Control Systems/Systems, Man, and Cybernetics Societies

Chair 1985‐1987, Past Chair, 1987‐1988.

IEEE Power Engineering Society, DC, PM and Special Machines Subcommittee of the Electric

Machinery Committee member, 1995‐2002.


Publications Chair and member of Program Committee, IEEE Power Electronics Specialists

Conference, 1989.

Tutorial Chair, member of Program Committee, and Technical Paper Reviewer for the IEEE

International Electric Machines and Drives Conference, May 1997.

International Society for Artificial Organs (ISAO), 1987‐1990.

Organizing Committee, Southern Gas Association's Gas Forecaster's forum, September 2004,

October 2005.

FellowshipsandAwards:

Eastman Kodak Fellowship, 1984‐85.

Marquette University Distinguished Scholar; Fall 1987, Spring 1988, Fall 1988, Spring 1989, Fall

1989, 1993‐1995, 1997‐2011.

Wisconsin Governor’s Business Plan Contest, “GasDay™, Natural Gas Demand Forecasting,” with

Brian Boyd, June 2, 2004. Won honorable mention, $1,000.

CoursesTaughtatMarquetteUniversity:

COEN 030; Introduction to Computer Hardware and Software.

EECE 111; Analog Electronics.

EECE 113; Linear Systems Analysis.

EECE 121; Electromagnetic Fields I.

EECE 123; Electromechanical Energy Conversion.

EECE 144; Experimental Problem‐Solving 4.

EECE 150/ELEN 4310 & EECE 5310; Control Systems.

EECE 151; Topics in Computers and Control; Digital and Computer Control.

EECE 153/ELEN 4320 & EECE 5320; Digital Control Systems.

EECE 169; Design with Analog Integrated Circuits (substitute lecturer, 4 weeks).

EECE 195; Independent Study; Incremental Motion Control, Design of Step Motor Drive

Circuits, Writing DLLs in C++, Senior Design Project, Software Design.

EECE 220/6310; Modern Control Theory.

EECE 225/6320; Optimal Control.

EECE 227/6330; Nonlinear and Adaptive Control.

EECE 229; Advanced Topics in Computers and Control; Adaptive Control.

EECE 295; Readings and Research: Control of Step Motors; Adaptive Systems and Control;

Neural Networks and Control Systems; Simulations and Numerical Methods in Control

Systems; Optimal Control; and Intelligent Control.

EECE 396; Seminar; Artificial Hearts.

EECE 396; Seminar; Advanced and Intelligent Systems/Controls.


MEEN 142; Design of Machine Elements, guest lecturer.

CoursesDevelopedatMarquetteUniversity:

EECE 153/5320; Digital Control Systems.

EECE 227/6330; Nonlinear and Adaptive Control.

ResearchFundingandLicensingRevenue:

Marquette career external funding as principal investigator: $7,746,006.

1. Summer Faculty Fellowship, Marquette University Committee on Research, “A Study on the

Capacitor‐Dump Drive Circuit for Brushless DC and Stepper Motors,” Summer 1987, $2700.

Resigned due to other awards.

2. National Science Foundation, Engineering Initiation Award, “Improvements in the Performance

and Efficiency of Small Electric Machines in Motion Control Applications,” Sept. 1987‐Aug. 1989,

$66,688. Requested reduced award of $37,904 due to other funding. (ECS‐8708678)

3. Whitaker Foundation, Biomedical Engineering Research Grants, “Performance and Efficiency

Improvements of Electric Motors in Artificial Hearts,” July 1987‐June 1990, $149,603.

4. Milwaukee Heart Research Foundation, “Control, Performance, and Efficiency Improvements of

Electrical Motors in Artificial Hearts.” July 1987‐June 1988, $78,531.

5. National Science Foundation, Research Experiences for Undergraduates Supplement, “REU

Supplement Request for NSF Grant ECS‐8708678, Improvements in the Performance and

Efficiency of Small Electric Machines in Motion Control Applications,” Dec. 1987‐Aug. 1989,

$14,000.


Electrical Motors in Artificial Hearts.” July 1988‐June 1989, $66,450.


Electrical Motors in Artificial Hearts,” July‐Aug. 1989, $23,840.

8. Prime/Computervision, “Request for Personal Designer Software.” Received software packages

valued at $8600, June 1989.

9. National Science Foundation/Electric Power Research Institute, Program Initiate on Intelligent

Control, “Incorporating Known Characteristics into Artificial Neural Networks for Dynamic

System Identification and Control with Electrically Commutated Machine Applications,” Feb.

1993‐Jan 1997 with co‐principal investigators Abd A. Arkadan, Xin Feng, and James A. Heinen,

$200,000 from NSF (MSS‐9216462), $100,000 from EPRI (RP8030‐11).

10. Johnson Controls, Inc., Controls Research, “Analysis of the Simulations and Controls of a Package

for Modeling of Air Handling Units,” July‐Dec. 1992, $13,558.

11. Summer Faculty Fellowship, Marquette University Committee on Research, for Summer 1993,

“Conceptual Studies of Intelligent Controls: Adaptive Neural Controllers for Motion Control

Applications,” Summer 1993, $4,000. Resigned due to other awards.


12. Wisconsin Center for Demand‐Side Research, “A Feasibility Study for Using Neural Networks to

Forecast Gas Sendout,” with co‐principal investigator Xin Feng, July‐Aug, 1993, $9,958.

13. Wisconsin Center for Demand‐Side Research, “Development of Models to Predict and Analyze

Gas Consumption,” with co‐principal investigator Xin Feng, Sept.1993‐Aug. 1994, $38,313.

14. Wisconsin Gas Company, “Development of User Friendly Computer Applications for Demand

Forecasting,” Feb.‐May 1994, $1,500.

15. Wisconsin Center for Demand‐Side Research, “Supplement to Development of Models to Predict

and Analyze Gas Consumption,” with co‐principal investigator Xin Feng, May 1994, $5,533.

16. Wisconsin Gas Company, “Development of User Friendly Computer Applications for Demand

Forecasting,” Aug.‐Dec. 1994, $9,810.


Gas Consumption‐Year 2, Fall 1994,” Sept.‐Dec.1994, $11,495.


Gas Consumption‐Year 2, Spring 1995.” Jan.‐Aug. 1995, $10,501.

19. Gas Research Institute, “Development of Models to Predict and Analyze Gas Consumption,”

Sept. 1995‐Aug. 1996, $112,378.

20. Energy Center of Wisconsin, “Development of Models to Predict and Analyze Gas Consumption‐

Year 3,” Sept. 1995‐Aug. 1996, $9,950.

21. Wisconsin Gas Company, “Development of User Friendly Computer Applications for Gas Load

Estimation,” Sept.‐Dec. 1995, $9,959.

22. Energy Center of Wisconsin, “Development of Models to Predict and Analyze Gas Consumption‐

Year 4,” Sept. 1996‐Aug. 1997, $13,420.

23. Gas Research Institute, “Development of Models to Predict and Analyze Gas Consumption,”

Sept. 1996‐Aug. 1997, $49,095.

24. United States Department of Education, Graduate Assistance in Areas of National Need

Program, “Graduate Assistance in the Development of Electrical Engineering Faculty,” with

principal investigator James A. Heinen, Sept. 1997‐Aug. 1998 (Year 1), $122,255; Sept. 1998‐Aug.

1999 (Year 2), $126,110; Sept. 1999‐Aug. 2000 (Year 3), $126,110.

25. West Florida Natural Gas, “Models to Predict Gas Consumption for West Florida Natural Gas,”

April 1997, $5,000.

26. Stone & Webster Management Consultants, Inc., “Models to Predict Gas Consumption for

Arkansas Western Gas,” May 1997, $17,500.

27. Stone & Webster Management Consultants, Inc., “Commercialization Agreement for Models to

Predict Gas Consumption,” August 1997‐September 1998, $120,000.

28. Stone & Webster Management Consultants, Inc., “Small Projects,” August 1997‐July 1998, total

funded projects $24,150.

29. Stone & Webster Management Consultants, Inc., “Small Projects,” August. 1998‐July 1999,

Funded projects: $18,170

30. Stone & Webster Management Consultants, Inc., “Commercialization Agreement for Models to

Predict Gas Consumption,” September1999‐August 2000, $25,000.

31. Stone & Webster Management Consultants, Inc., “Small Projects,” September 1999‐August

2000, Funded projects: $53,050


32. United States Department of Education, Graduate Assistance in Areas of National Need

Program, “Graduate Assistance in the Development of Electrical Engineering Faculty,” with co‐

investigators James A. Heinen and Nabeel A. O. Demerdash, August 2000‐August 2001 (Year 1),

$127,500; August 2001‐August 2002 (Year 2), $127,500; August 2002‐August 2003 (Year 3),

$127,500.

33. Stone & Webster Management Consultants, Inc., “Small Projects,” September 2000‐August

2001: $19,290.

34. GasDay™ Responsibility Center, October 2001‐June 2002, funding from licenses, royalties, and

related projects: $87,463.

35. GasDay™ Responsibility Center, July 2002‐June 2003, funding from licenses, royalties, and


36. National Collegiate Inventors and Innovators Alliance, “Development and Implementation of a

Web‐Based Demand Forecasting Service,” July 2003‐June 2004, $16,900.

37. American Public Gas Association, “Development and Implementation of a Public Gas Company

Web‐based Demand Forecasting Service ‐‐ Phase 1,” July 2003‐June 2004, $34,752 (decision

pending).





40. GasMarq™ Responsibility Center, December 2004‐June 2005: $173,730.



42. GasMarq™ Responsibility Center, July 2005‐June 2006: $223,998.


related projects: $346,251

44. Burns & McBride Energy, “Heating Oil Demand Forecasting,” with principal investigator George

F. Corliss, April 2007‐December 2008, $65,000.





47. Southeast Wisconsin Energy Technology Research Center, “Short Term Wind Speed Prediction

from a Network of Inexpensive Weather Stations,” as co‐investigator with principal investigator

Joseph Bockhorst (UWM), co‐investigators Paul Roebber (UWM), and George F. Corliss (MU),

requested $14,208 (Marquette portion), submitted April 2009, decision: funded at reduced

level, Marquette portion $11,000.

48. Southeast Wisconsin Energy Technology Research Center, “Developing a Business Case for

Sustainable Asset Renewal of Existing Buildings,” as co‐investigator with co‐principal

investigators Carol Diggelman (MSOE) and George Corliss (MU) and co‐investigators Jeong Woo

(MSOE), Bass Abusharka (MSOE), Mike Emmer (MSOE), Mukul Goyal (UWM), Joe Bockhorst


(UWM), and Seyed Hosseini (UWM), requested $10,000 (Marquette portion), submitted April

2009, decision: funded at $9,528











54. Undergraduate Summer Research Program, Marquette University, “Daily Natural Gas Demand

Models: Design Day Studies from Monthly Data,” with co‐principal investigator George Corliss

and undergraduate student Calvin Jay, $5,000.


related projects: $782,844 (projected).

Groups,Committees,andActivitiesatMarquetteUniversity: Graduate Committee, Electrical and Computer Engineering, 1987‐1991, 2009‐present.

Department Chair Search Committee, Electrical and Computer Engineering, 1987.

Faculty Search Committee, Electrical and Computer Engineering, 1987‐1991, 1999, 2005‐2008.

Faculty Advisor, InterVarsity Christian Fellowship, 1987‐2000.

Computer Software Coordination Committee, Electrical and Computer Engineering, 1988‐

present.

Ad Hoc Committee on Peer Review of Teaching, Electrical and Computer Engineering, 1989‐

1990.

Ad Hoc Committee on Power Engineering, Electrical and Computer Engineering, 1990‐1991.

Undergraduate Committee, Electrical and Computer Engineering, 1991‐2005.

Web Page Supervisor, Electrical and Computer Engineering, 1996‐1998.

College of Engineering Task Force on Enrollment Management, 1996‐1997.

Goals Committee, Electrical and Computer Engineering, 1996‐2002.

College of Engineering Computer Resource Committee, 1997‐2003.

College of Engineering Dean Search Committee, 1998‐1999.

GAANN Advisory Board, Electrical and Computer Engineering, 2000‐2004

Department Chair Search Committee, Electrical and Computer Engineering, 2000‐2001.


College Curriculum Committee, College of Engineering, 2002‐2004

Enrollment Task Force, College of Engineering, 2003‐2005.

Marquette University Intellectual Property Review Board, 2006‐present.

Executive/Goals Committee, Electrical and Computer Engineering, 2010‐present.

OtherProfessionalGroups,Committees,andActivities:

Instructor and Course Originator, two day short course titled “Microprocessor Control of Step

Motors,” Champaign, IL, June 1986.

Session Chair, Fifteenth Annual Symposium on Incremental Motion Control Systems and

Devices, Champaign, IL, June 1986.

Session Chair, Sixteenth Annual Symposium on Incremental Motion Control Systems and


Session Chair, Seventeenth Annual Symposium on Incremental Motion Control Systems and


Chair, Missions Committee, Redeemer Evangelical Free Church, Milwaukee, WI, 1988‐1994, Vice

Chair 1994‐1999.

Volunteer Computer Instructor, Wauwatosa Public School System, 1988‐1989.

Instructor, three‐day seminar titled “Computer Control of Machines and Processes,” with G.

Perdikaris, at University of Wisconsin‐Parkside, Kenosha, WI, June 1989, June 1990, and May

1993.

Session Chair, “Motors and Drives 5: Modern Systems Theory for Converters and Drives,” PSEC

'89, Power Electronics Specialists Conference, Milwaukee, WI, June 1989.

Session Co‐Chair, “Adaptive Control I,” The First IEEE Conference on Control Applications,

Dayton, OH, September 1992.

Session Chair, “Neuro‐Control II,” and Session Co‐Chair, “Learning Control Systems,” 8th IEEE

International Symposium on Intelligent Control, Chicago, IL, August 1993

Chaperon, Jefferson Elementary School, Wauwatosa, WI, 5th grade overnight camp‐out at Camp

Whitcomb, May 1996, May 1997 and May 1999.

Session Chair, “Control on Induction Machines III,” 1st IEEE International Electric Machines and

Drives Conference, May 1997.

Youth Sponsor, Redeemer Evangelical Free Church, Milwaukee, WI, 1997‐2002.

Instructor, short course “Digital Controls, Analysis & Design,” with G. Perdikaris, at Eaton

Corporation, Milwaukee, WI, Spring 1998.

Elder, Redeemer Evangelical Free Church, Milwaukee, WI, 2004‐2008, 2010‐present.


Gas Forecaster's Forum, helped organize the conference and plan the program, October 26‐28,

2005.

Organized the Marquette University MATLAB Total Academic Headcount License meeting,

September 8, 2006, results ‐ Marquette has campus wide site license for MATLAB, January 2008.

Gas Forecaster's Forum, session organizer, moderated the Peak Day Round Table discussion,

October 18, 2006.

Ph.D.DissertationDirection:

1. Timothy L. Ruchti (GRA, Schmitt Fellow, GAANN Fellow) “Adaptive Control of Nonlinear Dynamic

Systems Using Artificial Neural Networks with Electromechanical Applications,” May 1995.

2. Jeffrey J. Garside (GTA, GRA, Schmitt Fellow, GAANN Fellow) “Identification and Control of

Switched Reluctance Motors Using Artificial Neural Networks and A Priori Knowledge,” August

1996.

3. Hardev Singh (GTA, Bacon Fellow), “Unified Approach to Singularly Perturbed Control Systems,”

March 2001.

4. Richard Lukas (GAANN Fellow, Schmitt Fellow), “Development of Adaptive Online Fuzzy

Arbitrator for Forecasting Short Term Natural Gas Usage,” April 2001.

5. Everton Walters (GTA, Schmitt Fellow, GAANN Fellow), “Continuous‐Time System Identification

from Discrete‐Time Measurements with Application To Natural Gas Pipeline Modeling,” co‐

advisor with James A. Heinen, March 2002.

6. Brian M. Marx (GTA, GAANN Fellow, Schmitt Fellow, GRA, Bacon Fellow), “The Hourly Profile:

Time Series Data Disaggregation,” May 2007.

7. Steven R. Vitullo (GTA, GRA), “Disaggregating Time Series Data for Energy Consumption by

Aggregate and Individual Customer,” November 2011.

Ph.D.Candidates:

1. Maral Fakoor (GRA)

M.S.ThesisDirection:

1. Brian John Seibel, “Stability Analysis of a Voltage Controlled Current Regulated PWM Induction

Motor Drive with respect to Load Variations,” July 1988.

2. Krishna Srinivas (GRA), “Closed Loop Control for Point‐To‐Point Moves Using Step Motors,”

January 1990.

3. Maher Jaroudi (GRA), “Torque Prediction for the Efficient Commutation Strategy of Bifilar Hybrid

Step Motor Closed‐loop Systems,” March 1990.

4. John C. Yunger (GRA), “A Closed‐Loop Fault Tolerant Microprocessor Based Step Motor Control,”

June 1990.

5. Yan Zhu (GRA), “Time‐Optimal Control for Hybrid Step Motors,” June 1990.

6. Timothy L. Ruchti (GRA, Schmidt Fellow), “Identification of Systemic Arterial Parameters for

Control of an Electrically Actuated Total Artificial Heart,” August 1990.


7. Jeffrey J. Garside (GTA), “The Application of Kohonen Topology‐Preserving Artificial Neural

Networks to Nonlinear System Identification,” October 1992.

8. Bryan S. Behun (GRA), “Incorporating a priori Information into Artificial Neural Network

Architectures with Applications to Switch Reluctance Motors,” May 1994.

9. Paul S. Carpenter (GTA), “An Adaptive Approach to Encoder‐Based Velocity Estimators Using

Recursive Least Squares,” December 1994.

10. Iftekhar Matin (GRA), “Artificial Neural Network Models to Predict Gas Consumption,”

November 1995.

11. Avinash Taware (GRA), “Forecasting and Identification Methods Applied to Gas Load Estimation

Problems,” December 1998.

12. Lance Hilbelink (GRA), “A Tale of Ten Cities: Consolidating Weather Information in Gas Load

Forecasting,” December 1999.

13. Hui Li Esther Lim, (GRA, Bacon Fellow), “Computational Intelligence Models for Short Term

Natural Gas Demand Forecasting,” May 2002.

14. Brian M. Marx (GTA, Bacon Fellow, GAANN Fellow), “Forecasting Daily Processing Tomato

Harvest Tonnage in California,” August 2004.

15. Susanto Halim (GTA, GRA), “Selection of Inputs for Generating Combinatorial Daily Natural Gas

Demand Forecasts,” November 2004.

16. Rohan O. Kennedy (GRA), “Detecting Outliers and Meter Anomalies in Natural Gas Customer

Flow Data,” November 2006.

17. Steven R. Vitullo (GTA, GRA), “Disaggregating Interval Time‐series Data Applied to Natural Gas

Flow Estimation,” April 2007.

18. Meng He (GRA), “Annual Growth Algorithm for hourly Data in Natural Gas Demand,” April 2007.

19. Sidhartha Tenneti (GTA, GRA), “Identification of Non‐temperature‐sensitive Natural Gas

Customers and Forecasting Their Demand,” March 2009.

20. Bo Pang (GRA, GTA) “The Impact of Additional Weather Inputs on Gas Load Forecasting,” July

2012.

21. Tian Gao (GRA), “Blending as a Multi‐Horizon Time Series Forecasting Tool,” May 2014.

22. Anisha D'Silva (GRA), “Estimating Unusually Cold Temperatures Using Statistical Methods,” (in

progress).

23. Paul E. Kaefer (GRA), “Transforming Analogous Time Series Data for Improving Natural Gas

Demand Forecast Accuracy,” (in progress).

M.S.EssayDirection:

1. Mark Anthony Figurski, “Reliability Testing of Electronic Subsystems,” January 1990.

2. Haiying Yu (GRA), “Artificial Neural Network Models to Predict Natural Gas Consumption” (failed

first exam, May 1998, switched to course option).

M.S.Candidates:

1. William Castedo (GTA)

2. Babatunde Ishola (GTA, GRA)


GraduateStudentswhoworkedwithmewhodidnotdoathesiswithme:

1. Donald F. Shelley (GRA), Thesis under George Corliss, May 2005.

2. Subatharshini Ganeshan (GRA), M.S. in Engineering Management, December 2005.

3. Meeta Oberio (GRA), M.S. in Bioinfomatics, December 2006.

4. Clifford Johnson (GRA), M.B.A. (did not graduate, left December 2005).

5. Priyanka Kennedy (GRA), M.S. in Biomedical Engineering, January 2009?

6. Cassandra Polansky (GRA), M.B.A., May 2008.

7. Scott Hopfensperger (GRA), M.S. in Computer Science (did not graduate, left December 2006).

8. Samson Kiware (GRA), “Detection of Outliers in Time Series Data,” M.S. in Computer Science,

Thesis director: George F. Corliss, May 2010; Ph.D. (in progress).

9. Tsuginosuke Sakauchi (GRA), “Applied Baysian Forecasting to Predict New Customers' Heating

Oil Demand” M.S. in Electrical and Computer Engineering, Thesis director: George F. Corliss (in

progress).

10. Navneet Dhillon (GTA, GRA), switched to course option, left December 2009).

11. James Lubow (GRA), M.B.A., (December 2013).

12. Catherine Twetten (GRA), M.S. A.E., (May 2013).

13. Yifan Li (GRA) “Forecasting Natural Gas Demand using Surrogate Data,” (left before finishing).

14. Hermine Akouemo Kengmo Kenfack (GRA), Dissertation advisor: Richard Povinelli (in progress)

15. Nazruba Islam (GTA), withdrew.

16. Shangsi Zhou, switched to course option.

17. Wenyan Min (GTA), left GasDay Project

18. Nicholas Winninger (GRA), COBA

JournalPublications:

1. Xin Feng, H. Mukai, and Ronald H. Brown, “A New Decomposition and Convexification Algorithm

for Nonconvex Large‐Scale Primal‐Dual Optimizations,” Journal of Optimization Theory and

Applications, vol. 67, no. 2, pp. 279‐296, November 1990.

2. Ronald H. Brown, Susan C. Schneider, and M. G. Mulligan, “Analysis of Algorithms for Velocity

Estimation from Discrete Position Versus Time Data,” IEEE Transactions on Industrial Electronics,

vol. 39, no. 1, pp. 11‐19, February 1992.

3. Ronald H. Brown and Mahar Jaroudi, “Torque Prediction and Maximization Strategies of Bifilar‐

Wound Hybrid Step Motors,” IEEE Transactions on Power Electronics, vol. 7, no. 3, pp. 535‐541,

July 1992.

4. Timothy L. Ruchti, Ronald H. Brown, Dean C. Jeutter, and Xin Feng, “An Identification Algorithm

for Systemic Arterial Parameters with Application to Total Artificial Heart Control,” Annals of

Biomedical Engineering, vol. 21, no. 3, pp. 221‐236, May/June 1993.

5. Ronald H. Brown, Paul Kharouf, Iftekhar Matin, and Luc P. Piessens, “Development of Artificial

Neural Network Models to Predict Daily Gas Consumption,” American Gas Association

Forecasting Review, vol. 5, pp. 1‐22, March 1996.


6. Abd A. Arkadan, H. H. Shehadeh, Ronald H. Brown, and N. A. Demerdash, “Effects of Chopping

on Core Losses and Inductance Profiles of SRM Drives,” IEEE Transactions on Magnetics, vol. 5,

no. 2, pp. 2105‐2108, March 1997.

7. Hardev Singh, Ronald H. Brown, and D. S. Naidu, “Unified Approach to Linear Quadratic

Regulator with Time‐Scale Property,” Optimal Control Applications and Methods, vol. 22, pp. 1‐

16, 2001.

8. Hardev Singh, Ronald H. Brown, and D. S. Naidu, “Robust Stability of Singularly Perturbed State

Feedback Systems Using Unified Approach,” IEE Proceedings‐Control Theory and Applications,

vol. 148, no. 5, pp. 391‐396, September 2001.

9. Richard. J. Povinelli, John. F. Bangura, Nabeel A. O. Demerdash, and Ronald H. Brown,

“Diagnostics of Bar and End‐Ring Connector Breakage Faults in Polyphase Induction Motors

Through a Novel Dual Track of Time‐Series Data Mining and Time‐Stepping Coupled FE‐State

Space Modeling,” IEEE Transactions on Energy Conversion, vol 17, no. 1, pp. 39‐46, March 2002.

10. Hardev Singh, Ronald H. Brown, and D. S. Naidu, “Discrete‐Time Time Scale Analysis via a New

Separation Ratio and the Unified Approach,” International Journal of System Science, vol. 34, no.

6, 15 May 2003.

11. John. F. Bangura, Richard. J. Povinelli, Nabeel. A. O. Demerdash, and Ronald H. Brown,

“Diagnostics of Eccentricities and Bar/End‐Ring Connector Breakages in Polyphase Induction

Motors through a Combination of Time‐Series Data Mining and Time‐Stepping Coupled FE‐State

Space Techniques,” IEEE Transactions On Industry Applications, vol. 39, no. 4, pp. 1005 ‐ 1013,

Jul‐Aug 2003.

12. Steven R. Vitullo, Ronald H. Brown, George F. Corliss, and Brian M. Marx, “Mathematical Models

for Natural Gas Forecasting,” The Canadian Applied Mathematics Quarterly, vol. 17, no. 4, pp.

807‐822, Winter 2009,

http://www.math.ualberta.ca/ami/CAMQ/table_of_content/vol_17/17_4i.htm.

13. Tsuginosuke Sakauchi, George F. Corliss, Steven R. Vitullo, and Ronald H. Brown, “Exploiting

Domain Knowledge and Bayesian Inference to Forecast Heating Oil Consumption,” Journal of the

Operations Research Society (submitted February 2012, in revision).

14. Steven R. Vitullo, George F. Corliss, Monica Adya, Farrokh Nourzad, and Ronald H. Brown,

“Disaggregation of Energy Consumption Data Using Correlated Variables,” Canadian Applied

Mathematics Quarterly (submitted November 2012, accepted May 2013).

15. Ronald H. Brown, Yifan Li, Bo Pang, Steven R. Vitullo, George F. Corliss, and Monica Adya,

“Detrending Daily Natural Gas Consumption Series Using Domain Knowledge to Improve

Forecasts,” International Journal of Forecasting (submitted December 2013).

16. George F. Corliss, Richard J. Povinelli, Paul Kaefer, William Castedo, and Ronald H. Brown. “Gas

demand forecasts with confidence intervals,” International Journal of Forecasting (submitted

January 2014).


TradeJournalPublications:

1. Ronald H. Brown, “Near Time‐Optimal Control of Step Motors with a Velocity Restriction,”

PowerConversion & Intelligent Motion, vol. 13, no. 11, pp. 58‐64, March 1987.

2. Ronald H. Brown, T, M. Richardson, and J. E. Buchanan, “Forecasting Daily Sendout Demand with

Artificial Neural Networks,” Gas Industries, February 2000.

3. (Ronald H. Brown), “Marquette Model Forecasts Daily Gas Loads,” Gas Utility Manager, February

2001.

BookChapters1. Ronald H. Brown, “Step Motor Drives” in The Industrial Electronics Handbook, J. D. Erwin, Ed.,

CRC Press LLC, Boca Raton, FL, in cooperation with IEEE Press, Piscataway, NJ, 1997, pp. 331‐

341.

RegisteredCopyrights:

1. "Marquette University Gas Load Estimation Software,” registered June 1997.

ConferencePublications:

1. B. C. Kuo and Ronald H. Brown, “The Step Motor Time‐Optimal Control Problem,” Proceedings:

Fourteenth Annual Symposium on Incremental Motion Control Systems and Devices

(Champaign, IL), June 1985, pp. 311‐317.

2. B. C. Kuo and Ronald H. Brown, “Determining Torque‐Speed Characteristics for Variable‐

Reluctance Step Motors,” Proceedings: Fourteenth Annual Symposium on Incremental Motion

Control Systems and Devices (Champaign, IL), June 1985, pp. 347‐352.

3. Ronald H. Brown, “Near Time‐Optimal Control of Step Motors,” Proceedings: Fifteenth Annual

Symposium on Incremental Motion Control Systems and Devices (Champaign, IL), June 1986, pp.

191‐200.

4. Ronald H. Brown, “Determining the Cross‐Over Point in Step Motor Point‐To‐Point Moves,”

Proceedings: Fifteenth Annual Symposium on Incremental Motion Control Systems and Devices


5. Ronald H. Brown, “Step Motor Time‐Optimal Point‐To‐Point Moves,” Proceedings: Conference

on Applied Motion Control'86 (Minneapolis, MN), June 1986, pp. 267‐273.

6. Ronald H. Brown and D. C. Hanselman, “Near‐Optimal Acceleration of Step Motors,”

Proceedings: IECON'86, Twelfth Annual IEEE Industrial Electronics Society Conference

(Milwaukee, WI), September 1986, pp. 17‐22.


7. Ronald H. Brown, “Near Time‐Optimal Control of Step Motors with a Velocity Restriction,”

Proceedings: Ninth International Motor‐Con'86 Conference (Boston, MA), October 1986, pp. 62‐

74.

8. Ronald H. Brown and George F. Corliss, “Optimal Acceleration of the Bifilar Hybrid Step Motor

with a Chopper Drive,” Proceedings: Sixteenth Annual Symposium on Incremental Motion

Control Systems and Devices (Champaign, IL), June 1987, pp. 205‐214.

9. Ronald H. Brown and Susan C. Schneider, “Velocity Observations from Discrete Position

Encoders,” Proceedings: IECON'87, Thirteenth Annual IEEE Industrial Electronics Society

Conference, SPIE vol. 858 (Cambridge, MA), November 1987, pp. 1111‐1118 (invited paper).

10. Ronald H. Brown and Susan C. Schneider, “Analysis of Algorithms for Velocity Observers from

Discrete Position Encoders,” Proceedings: Seventeenth Annual Symposium on Incremental

Motion Control Systems and Devices (Champaign, IL), June 1988, pp. 243‐252.

11. Ronald H. Brown, Mahar Jaroudi, and Gary Krenz, “Prediction and Optimization of Average

Torque in Hybrid Step Motors,” Proceedings: Seventeenth Annual Symposium on Incremental

Motion Control Systems and Devices (Champaign, IL), June 1988, pp. 253‐259.

12. Susan C. Schneider and Ronald H. Brown, “Frequency Domain Analysis of Discrete Velocity

Observers,” Proceedings: 31st IEEE Midwest Symposium on Circuits and Systems (St. Louis, MO),

August 1988, pp. 1100‐1103.

13. Mahar Jaroudi and Ronald H. Brown, “Torque Prediction and Optimal Control Strategies of Bifilar

Hybrid Step Motors,” Proceedings: APEC'89, IEEE Applied Power Electronics Conference

(Baltimore, MD), March 1989, pp. 204‐210.

14. Yan Zhu, Ronald H. Brown, and Xin Feng, “The Free End‐Velocity Time‐Optimal Control for Bifilar

Hybrid Step Motors with Inverse‐Diode‐Clamped Drive Circuit,” Proceedings: Eighteenth Annual

Symposium on Incremental Motion Control Systems and Devices (Champaign, IL), June 1989, pp.

59‐64.

15. Mahar Jaroudi and Ronald H. Brown, “Drive Circuit‐Bifilar Hybrid Step Motor System Modeling

and Prediction of Closed Loop Control for Average Torque Optimization,” Proceedings:

Eighteenth Annual Symposium on Incremental Motion Control Systems and Devices


16. Krishna Srinivas, Ronald H. Brown, and Cheryl Ventola, “A Damping Circuit for Bifilar Hybrid Step

Motors using Phase‐Lead Compensation,” Proceedings: Eighteenth Annual Symposium on

Incremental Motion Control Systems and Devices (Champaign, IL), June 1989, pp. 259‐265.

17. Mahar Jaroudi and Ronald H. Brown, “Torque Prediction for Efficient Commutation Strategies of

Bifilar Hybrid Step Motors,” PSEC'89 Conference Record, 20th IEEE Power Electronics Specialists

Conference (Milwaukee, WI), June 1989, vol. 1, pp. 411‐417.


18. Ronald H. Brown and Krishna Srinivas, “A Damping Circuit for Chopper Driven Bifilar Hybrid Step

Motors,” PSEC'89 Conference Record, 20th IEEE Power Electronics Specialists Conference

(Milwaukee, WI), June 1989, vol. 1, pp. 446‐451.

19. Timothy L. Ruchti, Ronald H. Brown, Xin Feng, and Dean C. Jeutter, “Estimation of Systemic

Arterial Parameters for Control of an Electrically Actuated Total Artificial Heart,” Proceedings:

32nd IEEE Midwest Symposium on Circuits and Systems (Champaign, IL), August 1989, pp. 640‐

643.

20. Timothy L. Ruchti, Ronald H. Brown, and Xin Feng, “Parameter Estimation of the Systemic

Arterial Bed for Control of an Electrically Actuated Total Artificial Heart,” Proceedings of the

11th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

(Seattle, WA), November 1989, vol. 11, part 1, pp. 151‐152.

21. Ronald H. Brown, Yan Zhu, and Xin Feng, “A New Relaxation Algorithm for the Time Optimal

Control Problem of Hybrid Step Motors,” Proceedings: 28th IEEE Conference on Decision and

Control (Tampa, FL), December 1989, vol. 1, pp. 907‐908.

22. Timothy L. Ruchti and Ronald H. Brown, “Recursive Estimation of Systemic Arterial Parameters

for Control of an Electrically Actuated Total Artificial Heart,” Proceedings: IEEE Conference on

Decision and Control (Tampa, FL), December 1989, vol. 2, pp. 1798‐1799.

23. Ronald H. Brown, Yan Zhu, and Xin Feng, “Time‐Optimal Acceleration Control and Point‐to‐Point

Control of Step Motors,” Proceedings: IEEE International Conference on Systems Engineering

(Pittsburgh, PA), August 1990, pp. 593‐596 (invited paper).

24. Jeffrey J. Garside, Timothy L. Ruchti, and Ronald H. Brown, “Utilizing Self‐organizing Artificial

Neural Networks to Solve Uncertain Dynamic Nonlinear System Identification Problems,” 1st

Midwest Electro‐Technology Conference (Ames, IA), April 1992, pp. 74‐77.

25. Timothy L. Ruchti, Jeffrey J. Garside, and Ronald H. Brown, “Identification of Dynamic Nonlinear

Systems Utilizing Artificial Neural Networks that Incorporate A Priori Knowledge,” 1st Midwest

Electro‐Technology Conference (Ames, IA), April 1992, pp. 114‐117.

26. Ronald H. Brown and Timothy L. Ruchti, “Gray Layer Technology: Incorporating A Priori

Knowledge into Feedforward Artificial Neural Networks,” IEEE International Joint Conference on

Neural Networks (Baltimore, MD), June 1992, pp. I‐806 ‐ I‐811.

27. Jeffrey J. Garside, Ronald H. Brown, Timothy L. Ruchti, and Xin Feng, “Nonlinear Estimation of

Torque in Switched Reluctance Motors using Grid Locking and Preferential Training Techniques

of Self‐organizing Neural Networks,” IEEE International Joint Conference on Neural Networks

(Baltimore, MD), June 1992, pp. II‐811 ‐ II‐816.

28. Timothy L. Ruchti, Ronald H. Brown, and Jeffrey J. Garside, “A Parameter Estimation Approach to

Artificial Neural Network Weight Selection for Nonlinear System Identification,” Proceedings of

the 1st IEEE Conference on Control Applications (Dayton, OH), September 1992, vol. 1, pp. 565‐

570.


29. Ronald H. Brown and Timothy L. Ruchti, “Identification of Dynamic Nonlinear Systems Using

Artificial Neural Networks Incorporating A Priori Knowledge,” Proceedings of the 1st IEEE

Conference on Control Applications (Dayton, OH), September 1992, vol. 1, pp. 571‐576.

30. Jeffrey J. Garside, Ronald H. Brown, Timothy L. Ruchti, and Xin Feng, “Modeling Torque in a

Switched Reluctance Motor for Adaptive control Purposes Using Self‐organizing Neural

Networks,” Proceedings of the 1st IEEE Conference on Control Applications (Dayton, OH),

September 1992, vol. 2, pp. 944‐949.

31. M. G. Mulligan, Ronald H. Brown, and James A. Heinen, “Using MATLAB in a Linear Systems

Analysis Course,” Proceedings of the 1992 North Midwest Section, American Society of

Engineering Education 54th Annual Meeting (Milwaukee, WI), October 1992, pp. 6.5.1‐6.5.6.

32. Jeffrey J. Garside, Timothy L. Ruchti, and Ronald H. Brown, “Using Self‐Organizing Artificial

Neural Networks for Solving Uncertain Dynamic Nonlinear System Identification and Function

Modeling Problems” Proceedings: 31st IEEE Conference on Decision and Control (Tucson, AZ),

December 1992, pp. 2716‐2721.

33. Timothy L. Ruchti, Ronald H. Brown, and Jeffrey J. Garside, “Estimation of Artificial Neural

Network Parameters for Nonlinear System Identification,” Proceedings: 31st IEEE Conference

on Decision and Control (Tucson, AZ), December 1992, pp. 2728‐2733.

34. Ronald H. Brown, Timothy L. Ruchti, and Xin Feng, “On Identification of Partially Known Dynamic

Nonlinear Systems with Neural Networks,” Proceedings: 8th IEEE International Symposium on

Intelligent Control (Chicago, IL), August 1993, pp. 499‐504.

35. Timothy L. Ruchti, Ronald H. Brown, and Jeffrey J. Garside, “Kalman Based Artificial Neural

Network Training Algorithms for Nonlinear System Identification,” Proceedings: 8th IEEE

International Symposium on Intelligent Control (Chicago, IL), August 1993, pp. 582‐587.

36. Ronald H. Brown, Timothy L. Ruchti, and Xin Feng, “Artificial Neural Network Identification of

Partially Known Dynamic Nonlinear Systems,” Proceedings: 32nd IEEE Conference on Decision

and Control (San Antonio, TX), December 1993, pp. 3694‐3699.

37. Ronald H. Brown, Paul Kharouf, Xin Feng, Luc P. Piessens, R. A. Nestor, “Development of Feed‐

Forward Network Models to Predict Gas Consumption,” Proceedings: IEEE International

Conference on Neural Networks (Orlando, FL), June/July 1994, (paper omitted due to publishing

error).

38. B. S. Behun, Jeffrey J. Garside, and Ronald H. Brown, “On Training Artificial Neural Networks to

Identify Periodic Functions,” Proceedings: IEEE International Conference on Neural Networks

(Orlando, FL), June/July 1994, pp. 3222‐3225.

39. Ronald H. Brown, Iftekhar Matin, and Xin Feng, “Development of Feed‐Forward Neural Network

Models for Gas Short‐Term Load Forecasting,” Proceedings: Adaptive Control Systems

Technology Symposium; theme: High Tech Controls for Energy and Environment (Pittsburgh,

PA), October 1994, pp. 222‐229.


40. Ronald H. Brown, Abd A. Arkadan, Nabeel A. O. Demerdash, and Jeffrey J. Garside, “An Algebraic

Approach for Performance Prediction of Electric Machines During Sustained Fault Conditions,”

Proceedings: IECON'95, 21st IEEE Industrial Electronics Conference (Orlando, FL), November

1995, vol. 1, pp. 719‐724.

41. P. S. Carpenter, Ronald H. Brown, James A. Heinen, and Susan C. Schneider, “On Algorithms for

Velocity Estimation Using Discrete Position Encoders,” Proceedings: IECON'95, 21st IEEE

Industrial Electronics Conference (Orlando, FL), November 1995, vol. 2, pp. 844‐849.

42. Ronald H. Brown and Iftekhar Matin, “Development of Artificial Neural Network Models to

Predict Daily Gas Consumption,” Proceedings: IECON'95, 21st IEEE Industrial Electronics

Conference (Orlando, FL), November 1995, vol. 2, pp. 1389‐1394.

43. Jeffrey J. Garside, Ronald H. Brown, and Abd A. Arkadan, “Identification of Switched Reluctance

Motor States Using Application Specific Artificial Neural Networks,” Proceedings: IECON'95, 21st

IEEE Industrial Electronics Conference (Orlando, FL), November 1995, vol. 2, pp. 1446‐1451.

44. Abd A. Arkadan, H. H. Shehadeh, Ronald H. Brown, and N. A. Demerdash, “Effects of Chopping

on Core Losses and Inductance Profiles of SRM Drives,” Proceedings: IEEE CEFC'96, 7th Biennial

IEEE Conference of Electromagnetic Field Computation (Okayama, Japan), March 1996, p. 223.

45. Ronald H. Brown, Paul Kharouf, Luc P. Piessens, and James M. Fay, “Artificial Neural Network

Based Models for Daily Gas Load Estimation,” Conference Record, Institute of Gas Technology;

Restructuring the Energy Markets (Clearwater, FL), April 1996.

46. Jeffrey J. Garside, Ronald H. Brown, and Abd A. Arkadan, “Switched Reluctance Motor Control

with Artificial Neural Networks,” Proceedings: IEMDC'97, 1st IEEE International Electric

Machines and Drives Conference (Milwaukee, WI), May 1997.

47. Ronald H. Brown, R. J. Lukas, T, M. Richardson, and J. E. Buchanan, “Gas Load Estimation Using a

Hybrid Dynamic Model,” 1998 International Gas Research Conference (San Diego, CA),

November 1998.

48. Hardev Singh, Ronald H. Brown, and D. S. Naidu, “Unified Approach to H4‐Optimal Control of

Singularly Perturbed Systems via Time Scale Analysis: Perfect state measurements,”

Proceedings: 37th IEEE Conference on Decision and Control (Tampa, FL), December 1998.

49. Ronald H. Brown, T, M. Richardson, and J. E. Buchanan, “Forecasting Daily Sendout Demand with

Artificial Neural Networks,” American Gas Association Operating Section Pre‐Print Proceedings

1999 (Cleveland, OH), May 1999, pp. 131‐139.

50. Hardev Singh, Ronald H. Brown, and D. S. Naidu, “Unified Approach to H4‐Optimal Control of

Singularly Perturbed Systems via Time Scale Analysis: Imperfect state measurements,”

Proceedings: 18th American Control Conference (San Diego, CA), June 1999, pp. 2909‐2913.

51. Hardev Singh, Ronald H. Brown, D. S. Naidu, and James A. Heinen, “Robust Stability of Unified

Singularly Perturbed Feedback Systems,” Proceedings, AIAA Guidance, Navigation, and Control

Conference (Portland, OR), August 1999, pp. 2214‐2215.


52. A Taware and Ronald H. Brown, “Dynamic Linear Finite Element Model for Pressure Prediction in

a Gas Pipeline,” Proceedings: 38th IEEE Conference on Decision and Control (Pheonix, AZ),

December 1999.


“Diagnostics of Faults in Induction Motor ASDs Using Time‐Stepping Coupled Finite Element

State‐Space and Time Series Data Mining Techniques,” Proceedings: Third Naval Symposium on

Electric Machines (EM 2000), (Pittsburgh, PA) December 2000.

54. John. F. Bangura, Richard. J. Povinelli, Nabeel A. O. Demerdash, and Ronald H. Brown,

“Diagnostics of Eccentricities and Bar/End‐Ring Connector Breakages in Polyphase Induction

Motors through a Combination of Time‐Series Data Mining and Time‐Stepping Coupled FE‐State

Space Techniques,” IEEE Industrial Applications Society Annual Meeting 2001, pp. 1579‐1586.


“Diagnostics of Bar and End‐Ring Connector Breakage Faults in Polyphase Induction Motors

Through a Novel Dual Track of Time‐Series Data Mining and Time‐Stepping Coupled FE‐State

Space Modeling,” IEMDC 2001, pp. 809‐813.

56. E. St. P. Walters, Ronald H. Brown, and James A. Heinen, “On Direct Offline Discrete‐Time

Identification of the Continuous‐Time Elements of an RC Electric Circuit Network,” Proceedings:

44th IEEE Midwest Symposium on Circuits and Systems (Dayton, OH), August 2001, pp. 37‐40.

57. Hui Li Lim and Ronald H. Brown, “Hourly Gas Load Forecasting Model Input Factor Identification

Using a Genetic Algorithm,” Proceedings: 44th IEEE Midwest Symposium on Circuits and

Systems (Dayton, OH), August 2001, pp. 670‐673.

58. Ronald H. Brown, M. K. Quan, B. Marx, and J. G. Norment, “Weathering the Storm: Successfully

Navigating Short‐Term Forecasting Abnormalities,” Gas Forecaster's Forum (Covington, KY),

September 2004.

59. Ronald H. Brown, Brian M. Marx, George F. Corliss, and Thomas F. Quinn, “Mathematical Models

for Short Term Natural Gas Demand Forecasting,” American Gas Association Operating Section

Proceedings 2007 (Dallas, TX), April 2007.

60. Ronald H. Brown, Yifan Li, Bo Pang, S. Vitullo, and George F. Corliss, “Detrending Daily Natural

Gas Demand Data Using Domain Knowledge,” 30th International Symposium on Forecasting

(San Diego, CA), June 20‐23, 2010. http://forecasters.org/pdfs/isf/ISF10_Proceedings.pdf, pg.

10.

61. S. Vitullo, R. Brown, Monica Adya, and George F. Corliss, “Disaggregating Time Series Energy

Consumption Data,” 30th International Symposium on Forecasting (San Diego, CA), June 2010,

http://forecasters.org/pdfs/isf/ISF10_Proceedings.pdf, pg. 44.

62. S. Vitullo, R. Brown, Monica Adya, and George F. Corliss, “Disaggregating United States Real

Gross Domestic Product,” 30th International Symposium on Forecasting, (San Diego, CA), June

2010, http://forecasters.org/pdfs/isf/ISF10_Proceedings.pdf, pg. 127.


63. Thomas F. Quinn, Ronald H. Brown, and George F. Corliss, “The GasDay Project at Marquette

University: A Laboratory for Real‐world Engineering and Business Experiences”, Proceedings of

the 2010 ASEE North Midwest Sectional Conference (Mankato, MN), October 2010.

64. George F. Corliss, Tsuginosuke Sakauchi, Steven R. Vitullo, and Ronald H. Brown, “Exploiting

Domain Knowledge to Forecast Heating Oil Consumption,” Advances in Mathematical and

Computational Methods: Addressing Modern Challenges of Science, Technology, and Society,

American Institute of Physics | Conference Proceedings No. 1368 (Waterloo, ON, Canada), July

2011, pp. 305‐308.

65. Catherine Twetten, Farrokh Nourzad, David Clark, George F. Corliss, Ronald H. Brown,

“Economic Factors Contributing to Natural Gas Demand Forecast Model Performance,” Midwest

Economics Association 76th Annual Conference (Evanston, IL), March 30‐April 1, 2012.

66. Ronald H. Brown, Thomas F. Quinn, George F. Corliss, J. Goldberg, and M. Nagurka, “The GasDay

Project: A Learning Laboratory in a Functioning Business”, American Society for Engineering

Education 119th Annual Conference & Exposition (San Antonio, TX), June 2012.

67. Steven R. Vitullo, George F. Corliss, and Ronald H. Brown, “Combining Time Series

Disaggregators,” 32th International Symposium on Forecasting, (Boston, MA), June 2012,

http://www.forecasters.org/proceedings12/VitulloStevenISF2012Presentation.pdf, pg. 59.

68. Thomas Quinn, Ronald H. Brown, David E. Clark, Catherine Twetten, Farrokh Nourzad, and

George F. Corliss, “Forecasting Natural Gas Demand: The Role of Physical and Economic

Factors,” 32th International Symposium on Forecasting, (Boston, MA), June 2012,

http://www.forecasters.org/proceedings12/QUINN_THOMAS_ISF2012_2012‐07‐16.pdf, pg. 147.

69. Farrokh Nourzad, Paul Kaefer, Adriatik Hajdari, David Clark, Ronald H. Brown, “A Natural Gas

Consumption Model with Physical, Economic, and Energy Variables,” Midwest Economics

Association 78th Annual Conference (Evanston, IL), March 2014.

70. Ronald H. Brown, Paul E. Kaefer, Calvin R. Jay, and Steven R. Vitullo, “Forecasting Natural Gas

Design Day Demand from Historical Monthly Data,” Pipeline Simulation Interest Group, PSIG

2014, (Baltimore, MD), May 2014

InvitedTalksandPresentations1. Department of Mathematics, Statistics and Computer Science Colloquium, “Mathematical

Problems in Control Systems/ Incremental Motion Control,” February 13, 1986.

2. IEEE Control Systems/Systems, Man, and Cybernetics Societies‐Milwaukee Chapter Meeting,

“Time Optimal Incremental Motion Control,” March 18, 1986.

3. IEEE Magnetic Society‐Milwaukee Chapter Meeting, “An Overview of Stepper Motors,” March

16, 1988.

4. IEEE Industrial Electronics Society‐Milwaukee Chapter Meeting, “Velocity Observations from

Discrete Position Encoders,” with Susan C. Schneider, April 20, 1988.


5. Biomedical Engineering Seminar, “The Milwaukee Heart Project; A Motor Actuated Artificial

Heart,” February 21, 1989.

6. Milwaukee Archdiocesan Education Convention, “The Milwaukee Heart Project ‐‐ A Motor

Actuated Total Artificial Heart.” October 5, 1989.

7. University of Wisconsin‐Milwaukee ITFS Noon Hour Seminars, broadcasted on the UWM

Interactive Television Network, “Engineering Aspects of the Milwaukee Artificial Heart Project.”

October 19, 1989.

8. Department of Electrical and Computer Engineering Colloquium, “Neural Networks in Control

Systems,” October 29, 1991.

9. Johnson Controls Colloquium, “Neural Networks in Control Systems,” January 10, 1992.

10. IEEE Control Systems/Systems, Man, and Cybernetics‐Milwaukee Chapter Meeting, “Neural

Networks in Control Systems,” March 25, 1992.

11. Dean's Advisory Council Meeting, Marquette University, “Center for Intelligent Systems,” April

16, 1993.

12. Young Engineering and Science Scholars (Y.E.S.S.), “University Research/ Development of

Models to Predict and Analyze Gas Consumption,” July 1994.

13. Wisconsin Association for Research Management, “Artificial Neural Networks and Fuzzy Logic ‐‐

what they are and what they're not,” September 28, 1994.

14. NSF/EPRI Intelligent Control Review (Palo Alto, CA), “Incorporating Known Characteristics into

Artificial Neural Networks for Dynamic System Identification and Control with Electrically

Commutated Machine Applications,” October 11, 1994.

15. IEEE Control Systems/Systems, Man, and Cybernetics‐Milwaukee Chapter Meeting,

“Development of Artificial Neural Network Models to Predict Daily Gas Consumption,” March

23, 1995.

16. TOD‐NeT, Marquette University, “Introduction to MATLAB,” April 21, 1995.

17. GRI Program Review Meeting, New Models for Estimating Gas Demand, “Development of

Models to Predict and Analyze Gas Consumption,” July 10, 1996.

18. Industry/University Day, Marquette University “Software to Forecast Natural Gas Demand,”

November 14, 1996.

19. TOD‐NeT, Marquette University, “MATLAB at the EECE 113 Level,” February 7, 1997.

20. Department of Electrical and Computer Engineering Colloquium, Marquette University, “Neural

Networks and Genetic Algorithms Lower Your Gas Bill,” November 17, 1998.

21. Panelist, Marquette University Graduate School, Brown Bag Lunch Series, “The Successful

Marquette Research Center,” September 5, 2000.


22. Department of Electrical and Computer Engineering Freshman Seminar, Marquette University,

“Control, Control, You must Learn Control” September 7, 2000.

23. Panelist, Graduate School, Office of Research and sponsored Programs, “The GAANN Program,”

September 27, 2000.

24. Lynde Bradley Science Club, Allen‐Bradley, Rockwell Automation, Inc. (Milwaukee, WI), “Neural

Networks and Genetic Algorithms Lower Your Gas Bill,” December 14, 2000.


“Control, Control, You must Learn Control,” October, 2001.

26. EECE and BIEN GAANN Seminar, Marquette University, “Industrial Sponsored Research,”

February 25, 2002.


“Control, Control, You must Learn Control,” October, 2003.

28. Marquette University Golden Angles, “GasDay™, Natural Gas Demand Forecasting,” January 21,

2004.

29. College of Engineering Alumni Reception (San Jose, CA), March 16, 2004.

30. Kohler Center for Entrepreneurship Business Plan Competition, “GasDay ™, Natural Gas Demand

Forecasting,” with Brian Boyd, April 7, 2004.

31. College of Engineering National Advisory Board, “Are you ready for the coming heating season?”

April 23, 2004.

32. Wisconsin Governor's Business Plan Contest, “GasDay ™, Natural Gas Demand Forecasting,”

with Brian Boyd, June 2, 2004. Won honorable mention, $1,000.

33. College of Engineering Administrative Council, Marquette University, “GasDay ™ Project at

Marquette University,” October 2004.


“GasDay ™ Project at Marquette University,” November 11, 2004.


“GasDay ™ Project at Marquette University,” September 15, 2005.

36. Department of Electrical and Computer Engineering Colloquium, Marquette University,

“Perspective on Energy Resources Post Katrina, Rita, and Wilma,” November 15, 2005.


“GasDay ™ Project at Marquette University,” September 7, 2006.


“GasDay ™ Project at Marquette University,” January 24, 2008.


“GasDay ™ Project at Marquette University,” January 22, 2009.


40. Department of Electrical and Computer Engineering Colloquium, “Colloquium Lab Expo ‐ The

GasDay Project,” October 20, 2009.


“GasDay™ Project at Marquette University,” March 11, 2010.

OtherPublicationsandPresentations:

1. Yan Zhu, Ronald H. Brown, and Xin Feng, “Point‐to‐Point Move Time‐Optimal Control for Bifilar

Hybrid Step Motors with Inverse‐Diode‐Clamped Drive Circuit,” presented at Marquette

University Sigma Xi Student Poster Competition (Milwaukee, WI), March 1990.

2. Timothy L. Ruchti and Ronald H. Brown, “Estimation of Systemic Arterial Parameters for Control

of an Electrically Actuated Total Artificial Heart,” presented at Marquette University Sigma Xi

Student Poster Competition (Milwaukee, WI), March 1990.

3. H. L. E. Lim and Ronald H. Brown, “Hourly Gas Load Forecasting Model Input Factor

Identification using A Genetic Algorithm,” presented at Marquette University Sigma Xi Poster

Symposium (Milwaukee, WI), October 2000.

4. E. St. P. Walters, James A. Heinen and Ronald H. Brown, “The Effect of Noise and Sampling on

System Identification,” presented at Marquette University Sigma Xi Poster Symposium

(Milwaukee, WI), October 2000.

5. R. Povinelli, J. Bangura, N. Demerdash, and Ronald H. Brown, “Diagnostics of Faults in Induction

Motor ASDS Using Time‐Stepping Coupled Finite Element State‐Space and Time Series Data

Mining Techniques,” presented at Marquette University Sigma Xi Poster Symposium

(Milwaukee, WI), October 2000.

6. Brian M. Marx, Ronald H. Brown, S. Halim, and R. N. Mirell, “Load Growth Estimation for Natural

Gas Forecasting,” presented at Advances in the Sciences Poster Symposium, Sigma Xi,

Marquette University (Milwaukee, WI), October 2002.

7. D. Hughes and Ronald H. Brown, “Analysis of Customer Base Growth over Time,” Gas

Forecaster's Forum, (Greensboro, NC), October 2002.

8. Ronald H. Brown, GasDay ™ Users Group Meeting (Chicago, IL), January 2003. (2 LDCs

represented)

9. Brian M. Marx, D. F. Shelley, and Ronald H. Brown, “Daily Agricultural Natural Gas Load

Forecasting for Processing Tomatoes in California,” presented at Advances in the Sciences Poster

Symposium, Sigma Xi, Marquette University (Milwaukee, WI), October 2003.

10. Ronald H. Brown and Richard M. Mirell, GasDay ™ Users Group Meeting (Albuquerque, NM),

October 2003. (9 LDCs represented)

11. Ronald H. Brown, “OK, Who Is Playing with the Thermostat?” Gas Forecaster's Forum,

(Albuquerque, NM), October 2003.


12. Ronald H. Brown and R. M. Mirell, GasDay ™ Users Group Meeting (Naperville, IL), December

2003. (4 LDCs represented)

13. B. Boyd and Ronald H. Brown, “GasDay ™ Natural Gas Demand Forecasting,” presented at

Marquette University Golden Angles meeting, January 2004.

14. D. F. Shelley, N. Klosinski, K. Kalitowski, W.Y. Lau, K. Shelley, C. Johnson, R.M. Mirell, and Ronald

H. Brown (advisor) “Development and Implementation of a Web‐Based Demand Forecasting

Service,” National Collegiate Inventors and Innovators Alliance 8th Annual Meeting (San Jose,

CA), March 2004.

15. Ronald H. Brown, T. Quinn, and B. Marx, GasDay ™ Users Group Meeting (Covington, KY),

September 2004 (7 LDCs represented).

16. New York Facilities Operating Group 17th Annual Metropolitan Gas Dispatchers Seminar,

“Forecasting Daily Sendout Demand” (Princeton, NJ), May 5, 2005.

17. Ronald H. Brown, T. Quinn, and B. Marx, GasDay™ Users Group Meeting (Hilton Head Island, SC),

October 26, 2005 (5 LDCs represented).

18. B. Marx. T. Quinn, and Ronald H. Brown, “Forecasting Daily Processing Tomato Harvest Tonnage

in California,” Gas Forecaster's Forum, (Covington, KY), September 2004.

19. Ronald H. Brown, “Forecasting Demand in the Shoulder Months,” Gas Forecaster's Forum,

(Hilton Head Island, SC), October 27‐28, 2005.

20. Brian M. Marx and Ronald H. Brown, “Hourly Profile,” Gas Forecaster's Forum, (Hilton Head

Island, SC), October 27‐28, 2005.

21. Steven R. Vitullo, Ronald H. Brown, and George F. Corliss, “Daily Natural Gas National Demand

Modeling,” Proceedings of the Forward Thinking Poster Session and Research Exchange,

(Marquette University, Milwaukee, WI), November 30, 2005, p. 9.

22. R. O. Kennedy, Ronald H. Brown, and George F. Corliss, “Detecting Outliers and Meter

Anomalies in Natural Gas Customer Data,” Proceedings of the Forward Thinking Poster Session

and Research Exchange, (Marquette University, Milwaukee, WI), November 30, 2005, p. 10.

23. Brian M. Marx, Ronald H. Brown, and George F. Corliss, “The Hourly Profile: Hourly Forecasting

When Hourly Data Is Unavailable,” Proceedings of the Forward Thinking Poster Session and

Research Exchange, (Marquette University, Milwaukee, WI), November 30, 2005, p. 11.

24. Ronald H. Brown, “GasDay, an Overview,” Wisconsin Public Service Company, January 20, 2006.

25. Thomas F. Quinn, Ronald H. Brown, and George F. Corliss, “Strategic Startup for the College of

Engineering,” COE NAC, March 10, 2006.

26. Steven R. Vitullo, Ronald H. Brown, and George F. Corliss, “Daily Natural Gas National Demand

Modeling,” 2006 Poster Session, Marquette University Chapter, Sigma Xi, (Marquette University,

Milwaukee, WI), March 24, 2006.


27. R. O. Kennedy, Ronald H. Brown, and George F. Corliss, “Detecting Outliers and Meter

Anomalies in Natural Gas Customer Data,” 2006 Poster Session, Marquette University Chapter,

Sigma Xi, (Marquette University, Milwaukee, WI), March 24, 2006.

28. Brian M. Marx, Ronald H. Brown, and George F. Corliss, “The Hourly Profile: Hourly Forecasting

When Hourly Data Is Unavailable,” 2006 Poster Session, Marquette University Chapter, Sigma Xi,

(Marquette University, Milwaukee, WI), March 24, 2006.

29. Ronald H. Brown, “Math & Modeling & How it Is Applied,” given to about 50 fourth grade

students from Magee Elementary, Genesee Depot, WI, April 7, 2006

30. George F. Corliss, R. O. Kennedy, and Ronald H. Brown, Analysis of Customer Data to Detect

Meter Anomalies, (Oklahoma Natural Gas, Oklahoma City, OK), June 15, 2006.

31. Ronald H. Brown, Marquette University Engineering Summer Camp, Fleck Foundation, July 5,

2006.

32. Ronald H. Brown, Career Services Center employer advisory board meeting tour of the GasDay

laboratory, July 18, 2006.

33. Ronald H. Brown, “Electrical Engineering at Marquette University,” to MSOE students, August

22, 2006.

34. George F. Corliss, R. O. Kennedy, Chris Spriggs, and Ronald H. Brown, “Art and Science of Finding

Lost and Unaccounted‐for Natural Gas,” SGA Gas Forecasters Forum, (Albuquerque, NM),

October 18, 2006.

35. Brian M. Marx, Steven R. Vitullo, George F. Corliss, Monica Adya, and Ronald H. Brown,

“Forecasting Peak Day Weather Using Heuristically Selected Probability Distributions,” SGA Gas

Forecasters Forum, (Albuquerque, NM), October 18, 2006.

36. Meng He, Ronald H. Brown, and George F. Corliss, “Annual Transformation Algorithm for hourly

Data in Natural Gas Consumption,” 2007 Poster Session, Marquette University Chapter, Sigma

Xi, (Marquette University, Milwaukee, WI), April 27, 2007.

37. Ronald H. Brown, Marquette University Engineering Summer Camp, Fleck Foundation, June,

2007.

38. Ronald H. Brown, “Research Results; The Heck‐with‐it Hook and Other Observations,” SGA Gas

Forecasters Forum, (Jacksonville, FL), October 16, 2007.

39. Sidhartha Tenneti, George F. Corliss, and Ronald H. Brown, “Quantitative Classification and

Demand Forecasting of Non Temperature Sensitive Natural Gas Customers,” Proceedings of the

Sigma Xi 2008 Poster Symposium, Marquette University Chapter, Sigma Xi, (Marquette

University, Milwaukee, WI), April 3, 2008.

40. Ronald H. Brown, “Load Growth and Design Day Studies,” SGA Gas Forecasters Forum, (New

Orleans, LA), October 14, 2008.


41. Ronald H. Brown and James M. Fay, “Load Duration Changing over Time,” SGA Gas Forecasters

Forum, (New Orleans, LA), October 14, 2008.

42. Ronald H. Brown and George F. Corliss, “Improved Forecasting by Leveraging External Weather

and Consumption Data,” SGA Gas Forecasters Forum, (New Orleans, LA), October 15, 2008.

43. Ronald H. Brown, Steven R. Vitullo, and George F. Corliss, “The GasDay Project at Marquette

University; Helping utilities save rate payers money and reduce CO2 emissions,” 2009 Wisconsin

Renewal Energy Summit, (Milwaukee, WI), March 25, 2009.

44. Steven R. Vitullo, Ronald H. Brown, and George F. Corliss, “Individual Energy Customer

Forecasting,” 2009 Wisconsin Renewal Energy Summit, (Milwaukee, WI), March 25, 2009.

45. Steven R. Vitullo with Ronald H. Brown and George F. Corliss, “Disaggregating Time Series Data

for Energy Consumption by Individual Customer,” Proceedings of the Sigma Xi 2009 Poster

Symposium, Marquette University Chapter, Sigma Xi, (Marquette University, Milwaukee, WI),

April 2, 2009.

46. Navneet Dhillon with Ronald H. Brown and George F. Corliss, “Ensemble Gas Load Forecasting,”

Proceedings of the Sigma Xi 2009 Poster Symposium, Marquette University Chapter, Sigma Xi,

(Marquette University, Milwaukee, WI), April 2, 2009.

47. S. Kiware with George F. Corliss and Ronald H. Brown, “Detection Of Time Series Outliers with a

Focus in Natural Gas Flow,” Proceedings of the Sigma Xi 2009 Poster Symposium, Marquette

University Chapter, Sigma Xi, (Marquette University, Milwaukee, WI), April 2, 2009.

48. Ronald H. Brown, “January 2009 Midwest Near‐Design‐Day Demand Characteristics,” SGA Gas

Forecasters Forum, (San Antonio, TX), October 13, 2009.

49. Navneet Dhillon, Ronald H. Brown, George F. Corliss, and Monica Adya, “Natural Gas Load

Forecasting Using Ensembles of Multiple Models,” Proceedings of the Sigma Xi 2010 Poster


April 7, 2010.

50. Tsuginosuke Sakauchi, , George F. Corliss and Ronald H. Brown, “Application of Bayesian

Forecasting to Heating Oil Consumption by New Customers,” Proceedings of the Sigma Xi 2010

Poster Symposium, Marquette University Chapter, Sigma Xi, (Marquette University, Milwaukee,

WI), April 7, 2010.

51. Anisha D’Silva, Ronald H. Brown, and George F. Corliss, “Estimating the Unusually Cold

Temperature Using Statistical Methods,” Proceedings of the Sigma Xi 2010 Poster Symposium,

Marquette University Chapter, Sigma Xi, (Marquette University, Milwaukee, WI), April 7, 2010.

52. Yifan Li, Ronald H. Brown, George F. Corliss, and Steven R. Vitullo, “Natural Gas Flow Estimation

Using Data Disaggregation Algorithm,” Proceedings of the Sigma Xi 2010 Poster Symposium,



53. Bo Pang, Ronald H. Brown, and George F. Corliss, “The Impact Of Additional Weather Inputs On

Gas Load Forecasting,” Proceedings of the Sigma Xi 2010 Poster Symposium, Marquette


54. Ronald H. Brown, George F. Corliss, Thomas F. Quinn, J, Fay, Farrokh Nourzad, David Clark, and

Monica Adya, “Detrending Daily Natural Gas Demand Data Using Domain Knowledge,” SGA Gas

Forecasters Forum, (Austin, TX), October 25‐27, 2010.

55. Ronald H. Brown, George F. Corliss, J, Fay, Monica Adya, David Clark, and Farrokh Nourzad,

“Econometric Inputs to Enhance Long‐Term Natural Gas Demand Forecasting,” SGA Gas

Forecasters Forum, (Austin, TX), October 25‐27, 2010.

56. Anisha D’Silva, Ronald H. Brown, and George F. Corliss, “Estimating the Extreme Low‐

Temperature Event Using Nonparametric Methods,” Proceedings of the Sigma Xi 2011 Poster


March 23, 2011.

57. Bo Pang, Ronald H. Brown, and George F. Corliss, “The Impact of Additional Weather Inputs on

Gas Load Forecasting,” Proceedings of the Sigma Xi 2011 Poster Symposium, Marquette

University Chapter, Sigma Xi, (Marquette University, Milwaukee, WI), March 23, 2011.

58. Yifan Li, Ronald H. Brown, George F. Corliss, and Monica Adya, “Forecasting Natural Gas Flow

Using Surrogate Data,” Proceedings of the Sigma Xi 2011 Poster Symposium, Marquette

University Chapter, Sigma Xi, (Marquette University, Milwaukee, WI), March 23, 2011.

59. Ronald H. Brown, George F. Corliss, Thomas F. Quinn, Farrokh Nourzad, Monica Adya, and David

Clark, “Research Update: Determining Design Day Conditions,” SGA Gas Forecasters Forum,

(Nashville, TN), October 18, 2011.

60. Ronald H. Brown, George F. Corliss, Thomas F. Quinn, Farrokh Nourzad, Monica Adya, and David

Clark, “Research Update: Econometric Inputs to Enhance Long‐Term Natural Gas Demand

Forecasting,” SGA Gas Forecasters Forum, (Nashville, TN), October 18, 2011.

61. Ronald H. Brown, George F. Corliss, and Thomas F. Quinn, “Gas Demand Response to February

2011 Cold Events,” SGA Gas Forecasters Forum, (Nashville, TN), October 19, 2011.

62. Tian Gao, Curtis Stochl, Ronald H. Brown, and George F. Corliss, “Blending as a General Purpose

Time Series Forecasting Tool,” Proceedings of the Forward Thinking Poster Session and Colloquy,

(Marquette University, Milwaukee, WI), November 29, 2011, pg. 29.

63. Hermine Akouemo, Ronald H. Brown, and George F. Corliss, “Daily Determination of 1‐in‐N

Temperature Conditions Method,” Proceedings of the Forward Thinking Poster Session and

Colloquy, (Marquette University, Milwaukee, WI), November 29, 2011, pg. 30.

64. Catherine Twetten, Farrokh Nourzad, David Clark, Ronald H. Brown, and George F. Corliss,

“Economic Factors Contributing to Natural Gas Demand Forecast Model,” Proceedings of the

Forward Thinking Poster Session and Colloquy, (Marquette University, Milwaukee, WI),

November 29, 2011, pg. 33.


65. Hermine Akouemo Kengmo Kenfack, Ronald H. Brown, Anisha D'Silva, George F. Corliss, and

Thomas F. Quinn. “A Method to Determine 1‐in‐N Years Extreme Cold Weather Conditions,” 9th

Annual Green Energy Summit (Milwaukee, WI), March 7‐10, 2012.

66. Wenyan Min, Samson Kiware, Ronald Brown, and George Corliss, “Detection Of Time Series

Outliers with w Focus On Machine Learning,” Proceedings of the Sigma Xi 2012 Poster


April 17, 2012.

67. Hermine Akouemo, Anisha D'Silva, Ronald Brown, and George Corliss, “Design Day Conditions:

How Cold Will It Get Next Winter?” Proceedings of the Sigma Xi 2012 Poster Symposium,


68. Tian Gao, Curtis Stochl, Ronald Brown, and George Corliss, “Blending as a General Purpose Time

Series Forecasting Tool” Proceedings of the Sigma Xi 2012 Poster Symposium, Marquette


69. Ronald H. Brown, George F. Corliss, and Thomas F. Quinn, “Research Update: Risks and

Mitigations for Near Design Day Forecasting in the Absence of Recent Historical Events,” SGA

Gas Forecasters Forum, (Clearwater Beach, FL), September 18, 2012.

70. David Clark, Ronald H. Brown, Catherine Twetten Dybicz, Farrokh Nourzad, Thomas F. Quinn,

and George F. Corliss, “Outlook for Economic Drivers of National Gas Demand,” SGA Gas

Forecasters Forum, (Clearwater Beach, FL), September 18, 2012.

71. Tian Gao, Ronald H. Brown, and George F. Corliss, “Blending as a Multi‐Horizon Time Series

Forecasting Tool,” Proceedings of the Forward Thinking Poster Session and Colloquy,

(Marquette University, Milwaukee, WI), November 28, 2012, pg. 21.

72. Hermine Akouemo, Richard Povinelli, and Ronald Brown, “Outlier Detection Technique for Data

Cleaning in the Natural Gas Domain,” Proceedings of the Forward Thinking Poster Session and

Colloquy, (Marquette University, Milwaukee, WI), November 28, 2012, pg. 36.

73. Sigma Xi, April 2013.

74. Ronald H. Brown, “Research Update: Models to Forecast Natural Gas Demand,” SGA Gas

Forecasters Forum, (San Antonio, TX), October 24, 2013.

75. Paul Kaefer, Ronald H. Brown, George F. Corliss, and Richard Povinelli “Forecasting Natural Gas

Demand for Electric Power Generation,” Proceedings of the Forward Thinking Poster Session

and Colloquy, (Marquette University, Milwaukee, WI), December 3, 2013.

PatentDisclosures:

1. Ronald H. Brown and Krishna Srinivas, “Phase Lead Compensation Damping Circuit for Chopper

Driven Step Motors,” Invention Disclosure submitted to Research Corporation Technologies


(RCT), March 1989. RCT took option to continue, April 1989. RCT elected not to accept

technology, May 1989.

2. Ronald H. Brown, “Models for Predicting Short‐Term Natural Gas Demand,” Invention Disclosure

submitted to Research Corporation Technologies (RCT), June 1995. RTC elected not to accept

technology, July 1995.

3. George F. Corliss, R. O. Kennedy, and Ronald H. Brown, “Detection of Unusual Data Events in

Time Series of Natural Gas Meter Readings,” December 2006.

4. Steven R. Vitullo, George F. Corliss, and Ronald H. Brown, “Flow Reconstruction Algorithm for

Disaggregating Interval Time‐Series Data Applied to Natural Gas Flow Estimation,” May 2007.

5. Brian M. Marx, George F. Corliss, and Ronald H. Brown, “Fitting a Continuous Profile to Hourly

Natural Gas Flow Data,” May 2007.

6. Ronald H. Brown and, George F. Corliss, “Surrogate Data for Energy Demand Forecasting,”

September 2007.

28‐Jul‐14in MCF

Rate Class 2013 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

R1 148.5 177.7 174.7 171.2 167.0 163.2 158.5 155.9 154.2 153.8 152.2 150.5 148.7 147.1 145.1 143.3 141.5R2 348.5 403.0 399.1 394.9 388.6 381.9 372.2 366.1 362.2 360.5 356.1 353.6 349.8 346.4 342.1 338.3 334.4R3 793.0 874.0 858.5 842.6 825.8 818.3 815.4 813.3 808.8 807.4 803.0 799.8 794.2 789.8 785.4

C1 177.9 216.6 211.5 206.2 199.6 193.6 187.5 185.5 184.5 184.4 182.4 180.4 178.4 176.6 174.5 172.6 170.6C2 465.1 520.8 519.0 515.3 505.0 493.5 480.4 475.1 472.4 474.5 474.5 472.1 469.9 468.1 465.5 463.3 461.1C3 1264.2 1413.6 1405.0 1396.1 1376.7 1352.5 1319.3 1305.6 1299.0 1300.6 1292.1 1289.3 1282.3 1276.6 1268.2 1261.2 1254.1C4 7811.8 8837.0 8548.3 8288.9 8149.1 8140.3 8113.7 8070.6 7997.8 7933.0 7873.0 7822.6 7751.3 7690.9 7630.3

R1 ‐1.7% ‐2.0% ‐2.5% ‐2.3% ‐2.9% ‐1.7% ‐1.1% ‐0.2% ‐1.0% ‐1.2% ‐1.2% ‐1.1% ‐1.3% ‐1.2% ‐1.2%R2 ‐1.0% ‐1.1% ‐1.6% ‐1.7% ‐2.5% ‐1.6% ‐1.1% ‐0.5% ‐1.2% ‐0.7% ‐1.1% ‐1.0% ‐1.2% ‐1.1% ‐1.1%R3 ‐1.8% ‐1.9% ‐2.0% ‐0.9% ‐0.4% ‐0.3% ‐0.5% ‐0.2% ‐0.5% ‐0.4% ‐0.7% ‐0.6% ‐0.6%

C1 ‐2.3% ‐2.5% ‐3.2% ‐3.0% ‐3.1% ‐1.1% ‐0.5% ‐0.1% ‐1.1% ‐1.1% ‐1.1% ‐1.0% ‐1.2% ‐1.1% ‐1.1%C2 ‐0.3% ‐0.7% ‐2.0% ‐2.3% ‐2.6% ‐1.1% ‐0.6% 0.5% 0.0% ‐0.5% ‐0.5% ‐0.4% ‐0.6% ‐0.5% ‐0.5%C3 ‐0.6% ‐0.6% ‐1.4% ‐1.8% ‐2.5% ‐1.0% ‐0.5% 0.1% ‐0.7% ‐0.2% ‐0.5% ‐0.4% ‐0.7% ‐0.6% ‐0.6%C4 ‐3.3% ‐3.0% ‐1.7% ‐0.1% ‐0.3% ‐0.5% ‐0.9% ‐0.8% ‐0.8% ‐0.6% ‐0.9% ‐0.8% ‐0.8%

% Change Normalized ForecastCalculated from Observed

Marquette University GasDayWeather Normalized Use Per CustomerActual

Observed Calculated from Observed Forecast

Actual use per customer (blue), calculated weather‐normalized use‐per‐customer from observed data (green), and forecasted weather‐normalized use‐per‐customer (red) for Customer Class Code R1.

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191400

1500

1600

1700

1800

1900

2000

Flo

w C

CF

Flow by Year Class Code R1 perCust

Year

1830

1882

18401861

1994

1845

1766

1839

17891813

1689

1794

1707

1780

1718

1777

1660

1747

1790

1712

16671670

1742

1632

1589

1585

1510

1559

1577

1542

1640

1538

1485

15221505

14871471

14511433

1415

28-Jul-2014Actual FlowWx Normalized UseNormal Wx as Wx Fcst


1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20193200

3400

3600

3800

4000

4200

4400

4600

Flo

w C

CF


Year

4094

420841294175

4461

4155

4000

4150

40434093

3834

4051

3864

4025

3909

4030

3801

3991

4114

394938873886

4060

3819

3733

3722

3560

3661

3699

3622

3811

3605

34853561

35363498

34643421

33833344 28-Jul-2014

Actual FlowWx Normalized UseNormal Wx as Wx Fcst


2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20197800

8000

8200

8400

8600

8800

9000

9200

Flo

w C

CF


Year

9010.0

8740.3

8574.98584.9

8867.1

8425.7

8241.48258.2

7995.2

8183.0

8293.8

8154.2

8570.8

8132.8

7930.3

8088.4 8073.78030.2

7997.7

7941.87897.9

7853.8


Actual use per customer (blue), calculated weather‐normalized use‐per‐customer from observed data (green), and forecasted weather‐normalized use‐per‐customer (red) for Customer Class Code C1.

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191700

1800

1900

2000

2100

2200

2300

2400

2500

Flo

w C

CF

Flow by Year Class Code C1 perCust

Year

2202

2266

2175

2235

2444

22402193

2271

22122254

2083

2224

2072

2189

2093

2166

1986

2115

2180

2062

2000

1996

2081

1936

18741875

1786

1855

1902

1845

1981

1844

17791824

18041784

17661745

17261706



1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20194600

4800

5000

5200

5400

5600

5800

Flo

w C

CF


Year

5184

5312

5166

5267

5750

5250

5041

5262

5106

5205

4843

5175

4921

5167

5004

5208

4906

5190

5418

51535093

5050

5283

4935

48024804

4604

4751

4835

4724

5043

4745

4651

47454721

46994681

46554633

4611



1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191250

1300

1350

1400

1450

1500

1550

Flo

w M

CF


Year

1372

1414 14031413

1527

1413

1359

1415

1376

1403

1318

1401

1348

1404

1372

1414

1332

1405

1459

1396 1384

1377

1442

1352

13171319

1267

1306

1333

1299

1377

1301

1264

1292 12891282

12771268

12611254



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20197600

7800

8000

8200

8400

8600

8800

9000

9200

Flo

w M

CF


Year

9159.0

8837.0

8596.0

8548.3

8707.5

8288.9 8256.6

8149.1

7961.5

8140.3

8293.6

8113.7

8540.5

8070.6

7811.8

7997.8

7933.0

7873.0

7822.6

7751.3

7690.9

7630.3


In 2005, we see the number of customer class code R3 customers increases from 171 to 270, an increase of 99 customers.

100

150

200

250

300

350

1-Jan-97

1-Jan-98

1-Jan-99

1-Jan-00

1-Jan-01

1-Jan-02

1-Jan-03

1-Jan-04

1-Jan-05

1-Jan-06

1-Jan-07

1-Jan-08

1-Jan-09

1-Jan-10

1-Jan-11

1-Jan-12

1-Jan-13

1-Jan-14

1-Jan-15

1-Jan-16

1-Jan-17

1-Jan-18

1-Jan-19

1-Jan-20

1-Jan-21

Customer Class R3

Nu

mb

er

of C

ust

om

ers

03-Aug-2014

ObservedRevenue Fcst Totals

There is a significant change in the R3 weather‐normalized use‐per‐customer (heavy blue trace) in 2004 and 2005 with the addition of these 99 customers. This shows the 99 customers had higher usage than the previous 171 customers. Thus we did not use the data prior to November, 2005.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1-Jan-97

1-Jan-98

1-Jan-99

1-Jan-00

1-Jan-01

1-Jan-02

1-Jan-03

1-Jan-04

1-Jan-05

1-Jan-06

1-Jan-07

1-Jan-08

1-Jan-09

1-Jan-10

1-Jan-11

1-Jan-12

1-Jan-13

1-Jan-14

Class Code R3 perCust Annual Weather Normalized Use

CC

F

03-Aug-2014 (9)

Wx Norm TotalWx Norm BaseloadWx Norm HeatloadActual TotalActual BaseloadActual Heatload

In the 2005‐2006 time frame we see a structural change in the rate of increase in the number of customers. In 2005, the number of the number of customer class code C4 customers increases from 735 to 802. In 2006, the number of customers increases to 842.

400

500

600

700

800

900

1000

1100

1-Jan-97

1-Jan-98

1-Jan-99

1-Jan-00

1-Jan-01

1-Jan-02

1-Jan-03

1-Jan-04

1-Jan-05

1-Jan-06

1-Jan-07

1-Jan-08

1-Jan-09

1-Jan-10

1-Jan-11

1-Jan-12

1-Jan-13

1-Jan-14

1-Jan-15

1-Jan-16

1-Jan-17

1-Jan-18

1-Jan-19

1-Jan-20

1-Jan-21

Customer Class C4

Num

ber of C

ust

om

ers

03-Aug-2014

ObservedRevenue Fcst Totals

There is a significant change in the C4 weather‐normalized use‐per‐customer (heavy blue trace) in 2004 and 2005 with the addition of the customers in 2005. This shows these customers had higher usage than the previous customers. The addition of customers in 2006 did not change expected structure of weather‐normalized use‐per‐customer amounts. Thus we did not use the data prior to November, 2005.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1-Jan-97

1-Jan-98

1-Jan-99

1-Jan-00

1-Jan-01

1-Jan-02

1-Jan-03

1-Jan-04

1-Jan-05

1-Jan-06

1-Jan-07

1-Jan-08

1-Jan-09

1-Jan-10

1-Jan-11

1-Jan-12

1-Jan-13

1-Jan-14

Class Code C4 perCust Annual Weather Normalized Use

MC

F

18-Jul-2014 (9)

Wx Norm TotalWx Norm BaseloadWx Norm HeatloadActual TotalActual BaseloadActual Heatload

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

20 y r Norm

-40

-20

0

20

40

60

65

80

01-Jan01-Feb

01-Mar

01-Apr01-May

01-Jun01-Jul

01-Aug01-Sep

01-Oct01-Nov

01-Dec01-Jan

Dai

ly T

empe

ratu

re -

- w

ind

adju

sted

Weighted Historical Weather

105

85

65

45

25

5

0

-15

HD

DW

1998 2000 2002 2004 2006 2008 2010 20120

2000

4000

6000

8000

10000

12000

Year

An

nu

al H

ea

ting

De

gre

e D

ays

Actual Annual HDDsNormal Annual HDDs

Annual HDDs

Year 20‐Year Normal Actual % colder

than normal HDD65 HDDW65 HDD65 HDDW65

1997 10096 9904 9618 9506 ‐4.0%1998 10096 9904 10157 9953 0.5%1999 10096 9904 11029 10818 9.2%2000 10145 9952 9676 9472 ‐4.8%2001 10096 9904 9953 9742 ‐1.6%2002 10096 9904 9339 9145 ‐7.7%2003 10096 9904 9456 9293 ‐6.2%2004 10145 9952 9667 9495 ‐4.6%2005 10096 9904 9390 9192 ‐7.2%2006 10096 9904 10571 10354 4.5%2007 10096 9904 10196 10016 1.1%2008 10145 9952 10938 10721 7.7%2009 10096 9904 10238 10033 1.3%2010 10096 9904 9886 9715 ‐1.9%2011 10096 9904 10054 9930 0.3%2012 10145 9952 11171 10958 10.1%2013 10096 9904 9809 9645 ‐2.6%

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