VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS Graphical Data Mining, Visual...

VISUAL ANALYSIS OF ELECTRICITY DEMAND:ENERGY DASHBOARD GRAPHICS

Fatma ÇINAR, MBA Capital Markets Board of Turkey

e-mail: [email protected] [email protected] @fatma_cinar_ftm @DataLabTR

C. Coşkun KÜÇÜKÖZMEN, PhD Izmir University of Economics, School of Business e-mail: [email protected] @ckucukozmen

www.datalabtr.com

The 5th Multinational Energy and Value Conference May 7-9, 2015 İstanbul

Real Time Interactive Data Management for «Effect and Response Analysis»

Technique: Lattice and ggplot2 Graphical Packages using R

Energy Dataset Graphics

Data-Mining

Analysis

VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

Data Source: Republic of Turkey Ministry of Energy and Natural Resources

Period: July 2007 – July 2011Temperature, consumption and year/month factors.

Visualization of electricity demand of Turkey during 2007 - 2011 through Graphical-Datamining analysis.Agenda

VISUAL ANALYSIS OF ELECTRICITY DEMAND:ENERGY DASHBOARD GRAPHICS

• Background information (day, month, year, weekdays, theweekNo1, theweekNo2)

• Temperature information ([HDD, CDD]*, the average temperature, maximum temperature )

• Consumer information (average consumption, peak consumption, daily consumption)

• To minimize the «date problem» the day / month / year data has been converted into separate columns.

• Electricity market is compensated on a per hour basis.

• It requires an unconventional analysis technique to detect which factors exert pressure on the system.

Data Types of

the Dataset


• PTF - consisting of day-ahead prices, market clearing price

• SMF- real time price or balancing power market price. The system operator gives loading and deloding insructions to balancing units in order to stabilize the system according to the bid prices of these balancing units.

• SAM -> system purchase amount • SSM-> system sales amount • KGUP- > final day ahead amount of production • YAL -> take the load • YAT -> dispose the load

The system work as follows


Operations

• The average temperature of each day for selected cities in Turkey, HDD, CDD values were calculated.

• HDD - Heating Degree Days : Indicate the days which the temperature is measured below 17.5 Celsius degree

• CDD - Cooling Degree Days : This is exactly the opposite of the HDD that indicates the difference between the temperature is above a certain temperature of that day

• These variables constitute the whole data set to enable us to observing the fluctuations on a daily, monthly or yearly basis arising from changes in temperature


Actions

Analyzing the demand for Electricity by the Factors affecting the demand with multi-dimensional Matrix Graphics based on Energy Dashboard Software to analyze and visualize

With this technique we can visually observe the effect of temperature on energy consumption, and correlations

We developed an R-based graphics DASHBOARD program with the package ggplot2 for Graphical Data Mining analysis


Log10 Mean Temparature vs Log10 Daily Consumption Explained by Year and Month Factors Grid Graphics

Log10 Mean

Temparature Vs Log10

Daily Consumptio

n Explained

by Year and Month Factors

Grid Graphics

We see the Avg Daily consumption trend against the temperature factor on the basis of 2007-2011 period and yoy basis with the grid graph (Dashboard)There is a significant correlation between the daily consumption and the temperature Avg Temperature increases in 6th 7th and 8th months also implies an increase in daily consumption Each chart type (i.e. baloon, triangels etc) indicates a certain year. The year 2011 indicates that the average temperature displays a seasonal increase in temperature compared to other years and also indicates an increase in the daily consumption.


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Month Factor Density and Violin Graphs

Log10 Mean Temparature Vs Log10

Daily Consumption Explained

by Year Factor

Density and Violin

Graphs

Density and Violin Graphs with logarithmic scale show us that there is a strong positive correlation between temperature and daily consumptionWe also observe that temperature tends to display double peak at some years which is an unexpected movementThere are also double peaked daily consumption related to such years


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Month Factor Power Law Graph

Log10 Mean


Mean Consumption explained by Year and

Month Factors

Grid Graph

Grid Graph of log10Mean Temperature vs. log10 Avg. Consumption explained by the Year-Month Factors We observe seasonality and periodicity of the ratio of point demand to average peak demand. Electricity has an interesting feature which must be balanced with production and consumption at all times. Therefore instantaneous consumption, can be obtained by adding the production of all power plants currently producing. Already we multiply the average consumption of 24h to obtain the daily consumption data.


• When double Log scale applied Power Law analysis is a by product

• LogY = a.LogX + b• a is the Risk Measure • And it is the same for every level

of X and Y• Power Law means that risk is scale

free

Log10 Mean Temparature Vs Log10

Daily Consumption Explained by Year and

Month Factor

Power Law Graphics


Tuesday, May 2, 2023

Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Year and Month Factors Smoothed Grid Graphics

Log10 Mean


Daily Consumpti

on Explained by Year Month Factor

Smoothed Grid

Graphics

•Linear regressions are convenient tools for the analytical world. •In a complex world, more complicated tools are needed for the analysis of data [such as Kernel Regression (Smoothing)]•Smooth option log ggplot2 captures the real trend of sequential data. •In this chart we can get more information than the simple regression analysis•Upper and lower bounds of dashed gray curves determines the 95% confidence interval while outside this range the data displays anomalies. •We need to monitor the effects of factors (year and month) •We observe anomaly during 2007 and 2009•There are points under smooth area for 9th and 10th months which show temperatures remained below normal course of months and thus the daily energy consumption rate showed a similar trend.



Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Year and Month

Factors Baloon Graph

Log10 Mean Temparature vs Log10

Daily Consumption Explained by Year and

Month Factor Baloon Graphh

Bubble Chart indicates log10 Mean Temperature vs. log10 Daily Consumption

So, we can see the effect of year and month factors on mean consumption

The size of the bubbles represents the magnitude of Average Consumption where the shape of the bubbles also implies the concentration with respect to specific dates.


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Month Factor Violin Graph


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Year Factor Violin Graph


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Year Factor Density Graph


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Month Factor Density Graph


Log10 Mean Temparature Vs Log10 Daily Consumption Explained by Years and Month Factor Density Violin Graph


Log10 Peak Temparature Vs Log10 Daily Consumption Explained by Year and Month Factors Grid Graphics


Log10 Peak Temparature Vs Log10 Daily Consumption Explained by Year Factor Violin Graph


Log10 Peak Temparature Vs Log10 Puant Consumption Explained by Year and Month Factors Grid Graphics


Log10 Peak Temparature Vs Log10 Daily Consumption Explained by Years and Month Factor Density Violin Graph


Log10 Peak Temparature Vs Log10 Daily Consumption Explained by Years and Month Factor Matrix Graph


Log10 Mean Temparature Vs Log10 Puant Consumption Explained by Year and Month Factors Grid Graphics


Log10 Mean Temparature Vs Log10 Puant Consumption Explained by Month Factor Violin Graph


Log10 Mean Temparature Vs Log10 Puant Consumption Explained by Year Factor Violin Graph

The electricity demand and consumption and temperature data are used to analyze the effect the average and maximum temperature on the mean and peak demand of electricity.For this purpose we developed software based on R package of ggplot2 which is quite convenient to represent multi-dimensional data and used this application for visual analysis.We hope this will help to arrange and regulate the production of electricity more economically



Contact

@DataLabTR@GeoLabTR@TRUserGroup@CORTEXIEN@Riskonometri@Riskonomi@datanalitik@Riskanalitigi@RiskLabTurkey@fatma_cinar_ftmtr.linkedin.com/in/fatmacinartr.linkedin.com/pub/kutlu-merihtr.linkedin.com/in/coskunkucukozmen

www.datalabtr.com

[email protected]

[email protected]

[email protected]@ieu.edu.trhttp://www.ieu.edu.tr/tr [email protected]://[email protected]@spk.gov.tr

http://www.spk.gov.tr/

http://www.riskonomi.com

Resources• Küçüközmen, C. C. Ve Çınar F., (2014). “Finansal Karar Süreçlerinde Grafik-

Datamining Analizi”, TROUGBI/DW SIG, Nisan 2014 İstanbul, http://www.troug.org/?p=684

• Küçüközmen, C. C. ve Çınar F., (2014). “Görsel Veri Analizinde Devrim” Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-veri-analizinde-devrim-mi.html.

• Küçüközmen, C. C. ve Merih K., (2014). “Görsel Teknikler Çağı" Söyleşi, Ekonomik Çözüm, Temmuz 2014, http://ekonomik-cozum.com.tr/gorsel-teknikler-cagi.html

• Küçüközmen, C. C. and Çınar F., (2014). “Banking Sector Analysis of Izmir Province: A Graphical Data Mining Approach”, Submitted to the 34th National Conference for Operations Research and Industrial Engineering (YAEM 2014), Görükle Campus of Uludağ University in Bursa, Turkey on 25-27 June 2014.

• Küçüközmen, C. C. and Çınar F., (2014). “New Sectoral Incentive System and Credit Defaults: Graphic-Data Mining Analysis”, Submitted to the ICEF 2014 Conference, Yıldız Technical University in İstanbul, Turkey on 08-09 Sep. 2014.

• Küçüközmen, C. C. and Çınar F., (2015). “Visual Anaysis of Electricity Demand Energy Dashboard Graphics” Submitted to the 5th Multinational Energy and Value Conference May 7-9, 2015 Kadir Has University in İstanbul, Turkey

• Merih, K. C. and Çınar F., (2015). “Sectoral Loans Default Chart of Turkey ”, Submitted to 35th National Operations Research and Industrial Engineering Congress (ORIE 2015) 09-11,September, 2015,Middle East Technical University, Ankara, Turkey

http://www.troug.org/?p=684

http://ekonomik-cozum.com.tr/gorsel-veri-analizinde-devrim-mi.html

http://ekonomik-cozum.com.tr/gorsel-teknikler-cagi.html

VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS Graphical Data Mining, Visual...

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