Weather Normalization
How to Submit Your Questions
Step 1: Type your question
here.
Step 2: Press “Send” to submit your
question.
Weather Normalization
John Penry, CFC
Conducting an Analysis
• What questions are trying to get answered?
• Collecting your data
• Understanding the analytical tools available
• Using the results
What questions are you trying to answer?
• Are you trying to forecast future loads/demand?
– Different customer classes have different “baseload” demand
– Have different sensitivities to weather
• Are you trying to explain variations in kwh to forecast?
– Understand your forecast and historical data
– Did you forecast ranges of consumption?
• Are you getting a better understanding of customer trends?
– Has the “baseload” demand changed?
– Are your customers becoming more efficient?
– Cost of Service Study implications
Data Collection
• Know Thy Data- the missing 11th commandment
• Load Data
– Where is the data measured?
• Busbar, lowside meter, are you accounting for losses
– What is the interval of measurement?
• Is your data contemporaneous?
– Is your data consistent across classes?
• Weather Data
– Where is the data measured?
– What is HDD, CDD, and GDD
• What is the region’s base temperature
• Sources for weather data
http://climate.usurf.usu.edu/products/data.php
http://www.weatherdatadepot.com/
Calculating HDD and CDD
• The Averaging Method
CD= ((Tmax +Tmin)/2) - 65
HD= 65 - (Tmax +Tmin)/2)
CD= ((82 + 61)/2) – 65 = 71.5 – 65 = 6.5 CD
• Hourly Average Integration
CD= (Sum of all temps/# of readings) - 65
HD= 65 - (Sum of all temps/# of readings)
• Sum the CD or HD across the month or cycle to get CDD and HDD
• Problems to Consider
– Defining Average Temp, Base Temp, Other factors (wind, humidity…)
• TMY- NOAA 30-year normal weather
Spot Check your Data
• Graph it out and see if something sticks out
– Weather data is problematic
– Did the temps really reach 9999F?
– Why is the curve flat?
• Use a second source to get the correct data
• Smoothing with averages
• Sum rows or columns to check for completeness
– 8760 hours per year, 8784 in a leap year
– Months with 31 days have 744 hours
– Months with 30 days have 720 hours
– February has 672/696 hours
Actual Energy and Weather – It’s Hot in Texas
Actual Energy and Weather
August 2009 – Loads & Temps
Determine the Variables
• We have an independent variable (IV) that is the weather
• We have a dependent variable (DV) that is our load
– In order to analyze the impact our IV has on our DV, we must
focus on each one in isolation, then together.
– What other ways can we study the variables.
August 2009 – Loads
Load Duration Curve for August
Load Duration Curves by Month for 2009
Daily Weather for 2009
Maximum, Minimum, and Average Temperatures
Temperature Duration Curve
Maximum, Minimum, and Average Temperatures each sorted separately
August 2009 Load Duration Curve with corresponding Temperatures in F
December 2009 Load Duration Curve
December 2009 Load Duration Curve with corresponding Temperatures in F
Regression Analysis
• A simple linear regression analysis is an attempt to create a formula that uses the independent variable (IV) to predict the
expected outcome of a dependent variable (DV).
• CDD and HDD are our IV
• Consumption or load is our DV
• Multiple Regression would take into consideration the affects of humidity, wind, precipitation… other factors
• An R2 (Coefficient of determination) value of .75 or greater is generally accepted as correlated in the Electric Utility Industry.
• In MS Excel, you must download the analysis tool-pack to do Regression Analysis
What is the optimal temperature to use?
MONTH KWh
Integrated Average
Temperature in F
Max Temperature
in F
Min Temperature
in F Simple
Average 55 58 60 63 65
8/1/2009 4,849,034 59.525 69.8 46.4 58.1 4.525 1.525 0 0 0
8/2/2009 5,229,503 64.625 80.6 53.6 67.1 9.625 6.625 4.625 2.125 0
8/3/2009 6,358,255 71.225 80.6 62.6 71.6 16.225 13.225 11.225 8.725 6.225
8/4/2009 6,137,910 68.75 75.2 62.6 68.9 13.75 10.75 8.75 6.25 3.75
8/26/2009 5,625,920 62.825 73.4 51.8 62.6 7.825 4.825 2.825 0.325 0
8/27/2009 5,648,806 63.35 75.2 51.8 63.5 8.35 5.35 3.35 0.85 0
8/28/2009 5,311,553 62.75 75.2 53.6 64.4 7.75 4.75 2.75 0.25 0
8/29/2009 4,480,163 57.2 66.2 48.2 57.2 2.2 0 0 0 0
8/30/2009 4,388,582 54.275 64.4 42.8 53.6 0 0 0 0 0
8/31/2009 4,856,667 54.275 68 41 54.5 0 0 0 0 0
0.84423 0.850016 0.828441 0.746282 0.647958
In Excel =RSQ($C$3:$C$33,J3:J33)
Correlation between the IV and DV
• Using excel, determine the proper temperature for you base study
– The closer the value is to 1, the more highly correlated the data
• Chart and show trend line
• Caution: Correlation is not necessarily causation!
August CDD
•Y = 140,222x + 4,679,287•Y is the predicted load, x is our HDD/CDD, and
4,679,287 is thought to be the base load.
•R2 = 0.85 so the data is highly correlated
December HDD
•Y = 16,829x + 4,757,887•Y is the predicted load, x is our HDD/CDD, and
4,757,887 is thought to be the base load.
•R2 = 0.47 so the data is not very well correlated
Why might the results work for August but not for December?
• What is the mix of consumers in your area?
• What type of heating/cooling do they have?
• Are the members seasonals?
• Can you segregate the customer classes?
• It goes back to your data: gigo
How to Submit Your Questions
Step 1: Type your question
here.
Step 2: Press “Send” to submit your
question.
Top Related