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

1. 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

2. 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 Thursday, May 21, 2015

3. 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

4.  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
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS Thursday, May 21, 2015

5.  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
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS Thursday, May 21, 2015

6. 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, 2015VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

7. 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, and
correlations
 We developed an R-based graphics DASHBOARD
program with the package ggplot2 for Graphical
Data Mining analysis
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

8. Thursday, May 21, 2015
Log10 Mean
Temparature
vs Log10
Daily
Consumption
explained by
Year and
Month
Factors
GridGraphics

9. Thursday, May 21, 2015
Log10Mean
TemparatureVs
Log10Daily
Consumption
explainedbyYear
andMonthFactors
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 also
indicates an increase in the daily consumption.
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

10. Thursday, May 21, 2015
Log10 Mean
Temparature
Vs Log10
Daily
Consumption
explained by
Month Factor
Density and
ViolinGraphs

11. Thursday, May 21, 2015
Log10 Mean
TemparatureVs
Log10 Daily
Consumption
explained by
Year Factor
Density and
ViolinGraphs
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
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

12. Thursday, May 21, 2015
Log10 Mean
Temparature
Vs Log10 Daily
Consumption
explained by
Year and
Month Factors
Smoothed
GridGraphics

13. Thursday, May 21, 2015
Log10 Mean
TemparatureVs
Log10 Daily
Consumption
explained by
Year and Month
Factor
SmoothedGrid
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.
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

14. Thursday, May 21, 2015
Log10 Mean
Temparature
Vs Log10 Daily
Consumption
explained by
Year and
Month Factors
BaloonGraph

15. Thursday, May 21, 2015
Log10 Mean
Temparature vs
Log10 Daily
Consumption
explained by
Year and Month
Factor 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.
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

16. Log10 Mean
Temparature
Vs Log10
Daily
Consumption
explained by
Year and
Month
Factors
ViolinGraph
Thursday, May 21, 2015

17. Log10 Daily
Consumption
Vs Log10
Mean
Temparature
explained by
Month
FactorViolin
Graph
Thursday, May 21, 2015

18. Log10 Mean
Temparature
Vs Log10
Daily
Consumption
explained by
Month Factor
Density
Graph
Thursday, May 21, 2015

19. Log10 Daily
Consumption
Vs Log10
Mean
Temparature
explained by
Month
Factor
Density
Graph
Thursday, May 21, 2015

20. Log10
Maximum
Temparature
Vs Log10 Daily
Consumption
explained by
Year and
Month Factors
Smoothed
GridGraphics
Thursday, May 21, 2015

21. Log10
Maximum
Temparature
Vs Log10 Daily
Consumption
explained by
Year and
Month Factors
BaloonGraph
Thursday, May 21, 2015

22. Log10
Maximum
Temparature
Vs Log10 Daily
Consumption
explained by
Year and
Month Factors
GridGraphics
Thursday, May 21, 2015

23. Log10
Maximum
TemparatureVs
Log10 Daily
Consumption
explained by
Month Factor
ViolinGraph
Thursday, May 21, 2015

24. Log10
Maximum
Temparature
Vs Log10
Daily
Consumptio
n explained
by Month
Factor
Density
Graph
Thursday, May 21, 2015

25. Thursday, May 21, 2015
Log10
Maximum
TemparatureVs
Log10 Daily
Consumption
explained by
Year Factor
Density and
ViolinGraphics

26. Log10 Mean
Temparature
Vs Log10
Mean
Consumption
explained by
Year and
Month
Factors
GridGraph
Thursday, May 21, 2015

27. Log10 Mean
TemparatureVs
Log10 Mean
Consumption
explained by
Year and
Month Factors
GridGraph
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
obtained by adding the production of all power generating units
that are running at that time.
 Already we multiply the average consumption of 24h to obtain
the daily consumption data.
Thursday, May 21, 2015
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS

28. CONCLUSION The 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
VISUALANALYSIS OF ELECTRICITY DEMAND: ENERGY DASHBOARD GRAPHICS
Thursday, May 21, 2015

29. coskun.kucukozmen@ieu.edu.tr
http://www.ieu.edu.tr/tr
coskunkucukozmen@gmail.com
http://www.coskunkucukozmen.com
fatma.cinar@spk.gov.tr
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

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Thursday, May 21, 2015