visual analysis of electricity demand: energy dashboard graphics

VISUAL ANALYSIS OF ELECTRICITY DEMAND:ENERGY DASHBOARD GRAPHICS

FATMA ÇINAR, MBA, CAPITAL MARKETS BOARD OF TURKEY

C. COŞKUN KÜÇÜKÖZMEN, PhD, İZMİR UNIVERSITY OF ECONOMICS

An Application of Graphical Data- mining with R

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

EnergyDatasetGraphics

Data-MiningAnalysis

VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS Thursday, May 21, 2015

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

Period: July 2007 – July 2011

Temperature, consumption and year/month factors.

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

Thursday, May 21, 2015VISUAL ANALYSIS OF ELECTRICITY DEMAND:ENERGY DASHBOARD GRAPHICS

Agenda

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 detectwhich factors exert pressure on the system.

Data Types of

theDataset


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

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

SAM -> system purchase amount

SSM-> system sales amount

KGUP- > final day ahead amount of production

YAL -> take the load

YAT -> dispose the load

The systemwork 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

Thursday, May 21, 2015VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

Thursday, May 21, 2015

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, andcorrelations

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

VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS


Log10 MeanTemparature

vs Log10 Daily

Consumptionexplained by

Year andMonthFactors

Grid Graphics


Log10 MeanTemparatureVs

Log10 Daily Consumption

explained byYearand Month Factors

GridGraphics

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 alsoindicates an increase in the daily consumption.




Vs Log10 Daily

Consumptionexplained byMonth FactorDensity and

Violin Graphs


Log10 MeanTemparature Vs

Log10 Daily Consumptionexplained byYear FactorDensity and

Violin Graphs

Density and Violin Graphs with logarithmic scale show us that there is a strong positive correlation between temperature and daily consumption

We also observe that temperature tends to display double peak at some years which is an unexpected movement

There are also double peaked daily consumption related to such years




Vs Log10 Daily Consumptionexplained by

Year andMonth Factors

SmoothedGrid Graphics


Log10 MeanTemparature Vs

Log10 Daily Consumptionexplained by

Year and MonthFactor

Smoothed GridGraphics

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.




Vs Log10 Daily Consumptionexplained by

Year andMonth FactorsBaloon Graph


Log10 MeanTemparature vs

Log10 Daily Consumptionexplained by

Year and MonthFactor Baloon

Graph

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.



Vs Log10 Daily



Violin Graph



Vs Log10 Mean

Temparatureexplained by

MonthFactorViolin

Graph



Vs Log10 Daily

Consumptionexplained byMonth Factor

DensityGraph



Vs Log10 Mean

Temparatureexplained by

MonthFactor

DensityGraph


Log10 Maximum

TemparatureVs Log10 Daily Consumptionexplained by


SmoothedGrid Graphics


Log10 Maximum


Year andMonth FactorsBaloon Graph


Log10 Maximum


Year andMonth FactorsGrid Graphics


Log10 Maximum

TemparatureVsLog10 Daily

Consumptionexplained byMonth FactorViolin Graph


Log10 Maximum

TemparatureVs Log10

Daily Consumption explainedby Month

FactorDensityGraph



Log10 Maximum

Temparature VsLog10 Daily

Consumptionexplained byYear FactorDensity and

Violin Graphics


Vs Log10 Mean



Grid Graph


Log10 MeanTemparatureVs

Log10 MeanConsumptionexplained by


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 balancedwith production and consumption at all times.

Therefore instantaneous consumption, can be obtained obtained by adding the production of all power generating unitsthat are running at that time.

Already we multiply the average consumption of 24h to obtain the daily consumption data.

Thursday, May 21, 2015VISUAL ANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

CO

NC

LU

SIO

NThe electricity demand 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



[email protected]

http://www.ieu.edu.tr/tr

[email protected]

http://www.coskunkucukozmen.com

[email protected]

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

http://www.riskonomi.com

@fatma_cinar_ftm

@ckucukozmen

@Riskonometri

@Riskonomi

@RiskLabTurkey

@datanalitik

@Riskanaltigi

tr.linkedin.com/in/fatmacinar/

tr.linkedin.com/in/coskunkucukozmen

Contact

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

http://www.riskonomi.com/

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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.

Merih, K. ve Çınar, F., (2013). “Modelling of Corporate Performance In Multi-Dimensional Complex Structured Organizations: “Cbbc” Approach”, Submitted to the EconAnadolu 2013: AnadoluInternational Conference in Economics III June 19-21, 2013, Eskişehir. http://www.econanadolu.org/en/index.php/articles2013/3683

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SO

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http://www.bilimsenligi.com/konu/middle-east-technical-university

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

http://www.econanadolu.org/en/index.php/articles2013/3683

visual analysis of electricity demand: energy dashboard graphics

Data & Analytics

energy consumption

energy dashboard software

multinational energy

electricity market

graphicaldatamining

effect of temperature

response analysis technique

certain temperature