Electricity forecasting of jammu & kashmir a methodological comparison

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
416
ELECTRICITY FORECASTING OF JAMMU & KASHMIR:
A METHODOLOGICAL COMPARISON
AMEESH KUMAR SHARMA
Electrical Site Incharge Schneider Electric Infrastructure Ltd, Jammu, India
ANKUSH GUPTA
Assistant Professor Vaishno Group of Engineering and Technology, Jammu, India
UMESH SHARMA
Instructor in IISD (Indian Institute of Skill Development), Jammu, India
ABSTRACT
The electricity demand forecast is an important input for planning of the power sector to
meet the future power requirement of various sectors of electricity consumption. A planned
load growth in industry, agriculture, domestic and other sectors is necessary to have unified
growth in all sectors of economy and therefore it is necessary that infrastructure is planned in
various sectors of electricity consumption so as to direct the overall growth of economy in
rational manner.
In spite of the large hydroelectric potential available, its exploitation has been very low.
If potential is adequately harnessed, not only would the state’s own demand-supply gap be
narrowed, but the state will also be relieved of the heavy expenditure incurred on Power
Procurement.
To cope up with above problems it is essential to know the future electricity demand. In
this project we have forecasted the future sector wise electricity demand by using two time
series methods (Exponential and ARIMA method) and compared the results for any
discrepancies in mythologies.
As can been seen from the results that for long term forecasting in some cases
Exponential method is more accurate than ARIMA method & in some cases ARIMA method
is more accurate than Exponential method. The actual comparison is done with the help of bar
graph of partial autocorrelation of both ARIMA method and Exponential method.
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING
& TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 2, March – April (2013), pp. 416-426
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI)
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IJEET
© I A E M E

417
KEYWORDS: Forecasting, Electricity requirement, SPSS Soft wear
INTRODUCTION
The electricity demand forecast is an important input for planning of the power sector to
meet the future power requirement of various sectors of electricity consumption. A planned
load growth in industry, agriculture, domestic and other sectors is necessary to have unified
growth in all sectors of economy and therefore it is necessary that infrastructure is planned in
various sectors of electricity consumption so as to direct the overall growth of economy in
rational manner.
The primary objective of the electrical energy forecast is to assess the electricity
demand for States/UTs so that the States/UTs are able to plan and arrange the electrical
infrastructure to meet demand in full and provide electricity to all. The electricity demand
forecast also works as a tool for planning the Demand Side Management (DSM) strategy on
long term basis for optimizing the peak demand and also plan long term tariff policy.
The state of Jammu & Kashmir is located in the extreme north of India and is bound on the
north by China and on the south by Himachal Pradesh and on the west by Pakistan. The state
has a population of 10143700, with 1568159 house hold as per 2001 census.
The state is traversed by three main rivers i.e. Indus, Jehlum and Chenab. The Indus
traverses through Ladakh, while the Jehlum flows through Kashmir and chenab drains Jammu.
The average rainfall is about 10cms.
There are huge Glaciers in the state and the existence of high mountains with glaciers and
rainfall makes it heaven for hydel generation. The State is endowed with a hydro power
potential of 20,000 MWS out of which a mere 11.68 % i.e. 2336.20 MWs has been harnessed
so far.
Despite hydropower being recognized as one of the most economic and preferred
source of electricity with it being the best choice for meeting peak demands, the depleted
capacity of hydro stations during winter months poses serious challenges to the State
Government in providing electric power supply to its people during the period. Due to purchase
of considerable amounts of power from the northern grid and overdraw under UI regime to
meet even the restricted supply gap major expenditure is incurred by the State on this account.
The average generation from State Sector projects is about 3400 units annually, out of which
about 1300 units from Baglihar HEP are traded through PTC. The balance electricity
requirement of the State is met through imports / purchases from the Central Power Generating
Stations through the Northern Grid. It is, therefore, evident that the State is largely dependent
on import of power. The extent of dependence has been increasing every year and is expected to
continue for the time being until the available potential is harnessed.
Inspite of the large hydroelectric potential available, its exploitation has been very low.
If potential is adequately harnessed, not only would the state’s own demand-supply gap be
narrowed, but the state will also be relieved of the heavy expenditure incurred on Power
Procurement. It is, therefore, a matter of great importance that the hydel potential of the state is
harnessed within the shortest possible time.
With huge hydro-potential on one side and increasing power demand adversely
affecting its economy on the other side, the State is continuously losing the opportunity of
reducing huge expenditure possible in the event of development of available potential.

418
Government of Jammu & Kashmir has laid maximum emphasis on the full
development of its hydro potential being clean & renewable source of energy. It has been alive
to the need for encouraging private sector participation in development of Hydro Projects. The
process of exploitation of hydel potential in small hydro sector through private sector
participation began seriously in the State in 2003 through State Hydel Policy issued vide Govt.
Order No. 211-PDD of 2003 dated 9.10.2003, under which 10 small projects were awarded to
various Independent Power Producers(IPPs), which are at various stages of implementation.
The state of Jammu & Kashmir was the second only to the Mysore state in having hydro
power as far back as the 1st
decade of this century.
METHODOLOGY
There is an urgent need for precision in the demand forecasts. In the past, the world
over, an underestimate was usually attended to by setting up turbine generator plants fired by
cheap oil or gas, since they could be set up in a short period of time with relatively small
investment. On the other hand, overestimates were corrected by demand growth. The
underlying notion here was that in the worst case, there would be an excess capacity, which
would be absorbed soon. In the Indian context, the demands were usually overestimated,
notwithstanding which, the capacities fell short of the actual demands on a year to year basis.
The presence of economics of scale, lesser focus on environmental concerns, predictability of
regulation and a favorable public image, all made the process of forecasting demand much
simpler. In contrast, today an underestimate could lead to under capacity, which would result in
poor quality of service including localized brownouts, or even blackouts. An overestimate
could lead to the authorization of a plant that may not be needed for several years. Many
utilities do not earn enough to be able to cover such a cost without offsetting revenues.
Moreover, in view of the ongoing reform process, with associated unbundling of electricity
supply services, tariff reforms and rising role of the private sector, a realistic assessment of
demand assumes ever-greater importance. These are required not merely for ensuring optimal
phasing of investments, a long term consideration, but also rationalizing pricing structures and
designing demand side management programs, which are in the nature of short- or
medium-term needs.
The gestation period for power plants, which are set up to meet consumer demand,
typically varies between 7 to12 years in the case of thermal and hydro plants and 3 to 5 years for
gas-based plants. As a result, utilities must forecast demand for the long run (10 to 20 years),
make plans to construct facilities and begin development well before the indices of forecast
growth reverse or slowdown. In manufacturing institutions and electric utilities there are a
number of factors that drive the forecast, including market share. The forecast further drives
various plans and decisions on investment, construction and conservation. Since electric
utilities are basically dedicated to the objective of serving consumer demands, in general the
consumer can place a reasonable demand on the system in terms of quantity of power. With
some built-in reserve capacity, the utilities may have to configure a system to respond to these
to the extent possible. In the process of making predictions, forecaster bears in mind the
feedback effects of pricing and other policy changes, and therefore, participates in the process
of designing ways and means to meet consumer demands.

419
Another use for demand forecasting models is the assessment of the impact that a new
technology might have on the energy consumption. This helps planners to evaluate the cost
effectiveness of investing in the new technology and the strategy for its propagation. The use of
a straightforward engineering end-use approach that focuses only physical factors can miss the
emergence of new end uses, as well as other effects such as the impact of rising energy prices as
a stimulus to energy efficiency. Also the process of projecting the demand would require
estimating market penetration of various devices, while accounting for fuel substitution,
average capacity and efficiency factors in the future, as well as average utilization rates. The
demand forecasts are also done for each consumer category and voltage level. Charging the
commercial, industrial and large consumers a higher charge, which is used to subsidize social
reform programs, optimizes revenues while keeping social objectives in mind. The forecast
plays an important role in identifying the categories which “can pay” and those that should be
subsidized.
To deal with all of the above many forecasting techniques have been developed,
ranging from very simple extrapolation methods to more complex time series techniques,
extensive accounting frameworks and optimization methods, or even hybrid models that use a
combination of these for purposes of prediction.
TIME SERIES METHODS
A time series is defined to be an ordered set of data values of a certain variable. Time
series models are, essentially, econometric models where the only explanatory variables used
are lagged values of the variable to be explained and predicted. The intuition underlying
time-series processes is that the future behavior of variables is related to its past values, both
actual and predicted, with some adaptation/adjustment built-in to take care of how past
realizations deviated from those expected. Thus, the essential prerequisite for a time series
forecasting technique is data for the last 20 to30 time periods. The difference between
econometric models based on time series data and time series models lies in the explanatory
variables used. It is worthwhile to highlight here that in an econometric model, the explanatory
variables (such as incomes, prices, population etc.) are used as causal factors while in the case
of time series models only lagged (or previous) values of the same variable are used in the
prediction.
In general, the most valuable applications of time series come from developing
short-term forecasts, for example monthly models of demand for three years or less.
Econometric models are usually preferred for long term forecasts. Another advantage of time
series models is their structural simplicity. They do not require collection of data on multiple
variables. Observations on the variable under study are completely sufficient. A disadvantage
of these models, however, is that they do not describe a cause-and-effect relationship. Thus, a
time series does not provide insights into why changes occurred in the variable.
Often in analysis of time series data, either by using econometric methods or time series
models, there do exist technical problems wherein more than one of the variables is highly
correlated with another (multi-co linearity), or with its own past values (auto-correlation). This
sort of a behavior between variables that are being used to arrive at any forecasts demands
careful treatment prior to any further analysis. These, along with other similar methodological
options, need a careful assessment while working out forecasts of demand for any sector.

420
MODELING TOOL AND SOFTWARE
SPSS (originally, Statistical Package for the Social Sciences) was released in its
first version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. Norman
Nie was then a political science postgraduate at Stanford University, and now Research
Professor in the Department of Political Science at Stanford and Professor Emeritus of
Political Science at the University of Chicago. SPSS is among the most widely used
programs for statistical analysis in social science. It is used by market researchers, health
researchers, survey companies, government, education researchers, marketing
organizations and others. The original SPSS manual (Nie, Bent & Hull, 1970) has been
described as one of "sociology's most influential books". In addition to statistical analysis,
data management (case selection, file reshaping, creating derived data) and data
documentation (a metadata dictionary is stored in the datafile) are features of the base
software.Statistics included in the base software:
• Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore,
Descriptive Ratio Statistics
• Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial,
distances), Nonparametric tests
• Prediction for numerical outcomes: Linear regression
• Prediction for identifying groups: Factor analysis, cluster analysis (two-step,
K-means, hierarchical), Discriminant
The many features of SPSS are accessible via pull-down menus or can be
programmed with a proprietary 4GL command syntax language. Command syntax
programming has the benefits of reproducibility; simplifying repetitive tasks; and handling
complex data manipulations and analyses. Additionally, some complex applications can
only be programmed in syntax and are not accessible through the menu structure. The
pull-down menu interface also generates command syntax, this can be displayed in the
output though the default settings have to be changed to make the syntax visible to the user;
or can be paste into a syntax file using the "paste" button present in each menu. Programs
can be run interactively or unattended using the supplied Production Job Facility.
Additionally a "macro" language can be used to write command language subroutines and a
Python programmability extension can access the information in the data dictionary and
data and dynamically build command syntax programs. The Python programmability
extension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" for
most purposes, although SaxBasic remains available. In addition, the Python extension
allows SPSS to run any of the statistics in the free software package R. From version 14
onwards SPSS can be driven externally by a Python or a VB.NET program using supplied
"plug-ins".

421
RESULTS AND DISCUSSION
fig: 4.1 Domestic energy required by Exponential & ARIMA method
0
1000
2000
3000
4000
5000
6000
7000
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
energy(millionKwh)
domestic energy required
by exponential method
energy required by ARIMA
method
We have done the comparison between domestic energy required by Exponential
method and domestic energy required by ARIMA method. Here in the above graph the energy
with in the starting years predicated by Exponential method and ARIMA method have very
short gap between them. As we progress further the gap goes on increasing at a faster rate. In
the year 2040 the energy required by Exponential is about 6328.20Million Kwhr and energy
required by ARIMA method is about 3414.99Million Kwhr. By comparing the partial
autocorrelation of Exponential method and ARIMA method as shown in the Appendix B, we
come to the conclusion that forecasting by ARIMA method is more accurate because in the
graph of partial autocorrelation of ARIMA method the values when averaged almost weight
zero which is actually required for better forecasting.
We have done the comparison between commercial energy required by Exponential
method and commercial energy required by ARIMA method. Here in the above graph the
energy with in the starting years predicated by Exponential method and ARIMA method have
very short gap between them. As we progress further the gap goes on increasing at a faster rate
just like as we discuss in the domestic case. In the year 2040 the energy required by
Exponential is about 1820.73Million Kwhr and energy required by ARIMA method is about
1093.09Million Kwhr. By comparing the partial autocorrelation of Exponential method and
ARIMA method as shown in the Appendix B, we come to the conclusion that forecasting by
Exponential method is more accurate because in the graph of partial autocorrelation of
Exponential method the values when averaged almost weight zero and no specific pattern was
followed by the graph in case of Exponential method which is actually required for better
forecasting.

422
Commercial energy required by Exponential & ARIMA methods
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
Energy(millionKwh)
Commercial energy required
by exponential method
commercial energy required by
ARIMA method
Agriculture Energy required by Exponential & ARIMA method
0
200
400
600
800
1000
1200
1400
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
Energy(MillionKwh)
Agriculture energy required by
exponential method
Agriculture energy required by
ARIMA method
We have done the comparison between Agriculture energy required by Exponential
method and Agriculture energy required by ARIMA method. Here in the above graph the
energy with in the starting years predicated by Exponential method and ARIMA method have
very short gap between them. As we progress further the gap goes on increasing at a faster rate

423
just like as we discuss in the domestic & commercial case but here the forecasted data is
increasing exponentially in case of ARIMA method. In the year 2040 the energy required by
Exponential method is about 381.03Million Kwhr and energy required by ARIMA method is
about 1259.50Million Kwhr. By comparing the partial autocorrelation of Exponential method
and ARIMA method as shown in the Appendix B, we come to the conclusion that forecasting
by ARIMA method is more accurate because in the graph of partial autocorrelation of ARIMA
method the values when averaged almost weight zero which is actually required for better
forecasting and no specific pattern was followed by the graph in case of ARIMA method which
is actually required for better forecasting.
Industrial energy required by Exponential & ARIMA methods
0
2000
4000
6000
8000
10000
12000
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
Energy(MillionKwh)
Industrial energy required by
exponential method
Industrial energy required by
ARIMA method
We have done the comparison between Industrial energy required by Exponential
method and Industrial energy required by ARIMA method. Here in the above graph the energy
with in the starting years predicated by Exponential method and ARIMA method have very
short gap between them. As we progress further the gap goes on increasing at a faster rate just
like as we discuss in the domestic & commercial case but here the forecasted data is increasing
exponentially in case of ARIMA method. In the year 2040 the energy required by Exponential
is about 2444.31Million Kwhr and energy required by ARIMA method is about
Exponential method the values when averaged almost weight zero which is actually required
for better forecasting and no specific pattern was followed by the graph in case of Exponential
method which is actually required for better forecasting.

424
public sector energy requirement by Exponential & ARIMA method
0
1000
2000
3000
4000
5000
6000
7000
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
energy(MillionKwh)
Public energy required by
exponential method
Public energy required by ARIMA
method
We have done the comparison between Public energy required by Exponential method
and Public energy required by ARIMA method. Here in the above graph the energy with in the
starting years predicated by Exponential method and ARIMA method have very short gap
between them. As we progress further the gap goes on increasing but here the forecasted data is
increasing exponentially in both the cases. In the year 2040 the energy required by Exponential
is about 5491.94Million Kwhr and energy required by ARIMA method is about
Exponential method the values when averaged almost weight zero which is actually required
for better forecasting and no specific pattern was followed by the graph in case of Exponential
method which is actually required for better forecasting.
Also have done the comparison between others energy required by Exponential method
and others energy required by ARIMA method. Here in the above graph the energy with in the
starting years predicated by Exponential method and ARIMA method have very short gap
between them. As we progress further the gap goes on increasing at a faster rate, here the
forecasted data is increasing exponentially in case of ARIMA method. In the year 2040 the
energy required by Exponential is about 2020.50Million Kwhr and energy required by ARIMA
method is about 6430.01Million Kwhr. By comparing the partial autocorrelation of
Exponential method and ARIMA method as shown in the Appendix B, we come to the
conclusion that forecasting by Exponential method is more accurate because in the graph of
partial autocorrelation of Exponential method the values when averaged almost weight zero
which is actually required for better forecasting and no specific pattern was followed by the
graph in case of Exponential method which is actually required for better forecasting.

425
Others sector energy requirement by Exponential & ARIMA methods
0
1000
2000
3000
4000
5000
6000
7000
2005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36
years
Energy(MillionKwh)
others energy required by
exponential method
others energy required by ARIMA
method
CONCLUSION AND RECOMMENDATIONS
In our minor project exponential method is more accurate than the ARIMA method
because in that case the data which we are dealing with is not a very fluctuating data. But here
the data with which we are dealing with is highly fluctuating one as we can see in the case of
energy requirement in agriculture sector at the starting the values are increasing but in the
middle years there is a sudden drop in the energy requirement so in this project of electricity
forecasting of Jammu and Kashmir both the methods are accurate. As in some cases ARIMA
method has better forecast than the Exponential method and in some cases exponential method
has better forecast than ARIMA method. Also as the actual data is highly fluctuating one so in
case of exponential method we have taken the log of all the reading in SPSS software in all the
sectors so that the actual data gets smoothen but we find that the actual data is still not so
smoothen than we have taken the square root of all the reading in the SPSS software of all the
sectors so that our actual data gets more smoothen and we can get more accurate results. After
taking the square root we have check the smoothen data with actual data by forming the data
chats with in the SPSS software.
The final result is taken out by simply comparing the partial autocorrelation of
Exponential method and ARIMA method. Finally we can say that the actual data is highly
fluctuating one so for a better or we can say for more accurate forecast we require more precise
reading (e.g. monthly reading or half yearly) so that our forecasted data can come more closer
to the reality.
Long-term electricity demand forecasting in power systems is a complicated task
because it is affected directly or indirectly by various factors primarily associated with the
economy and the population. In this project, two methods have been applied, first is the

426
Exponential smoothing method and second is ARIMA method. The methods provide high
accuracy forecasts for the year 2039-40. This is very useful in planning fuel procurement,
scheduling unit maintenance, and imports.
The forecasts presented in this project suggest that significant growth in electricity
demand can be expected in Jammu & Kashmir until 2039-40. The forecast faster growth in
electricity consumption is consistent with the anticipated, relatively moderate rate of economic
growth in Jammu & Kashmir in the coming decades. In addition, the faster growth in electricity
consumption also reflects the fact that there will be further structural changes in the Indian
economy and that subsequently some energy-intensive sectors in the economy are expected to
grow. Concomitant with the faster growth in electricity demand will be a continuation of the
change in the market shares, with oil, natural gas, and hydroelectricity becoming increasingly
important energy sources at the expense of coal, reflecting government policies towards the use
of cleaner energy in Jammu & Kashmir.
Finally it is therefore being recommended that we should find some other sources of
energy so that we can able to full fill our future requirements. As we all know that the
population of Jammu & Kashmir is increasing at a faster rate so we can use non convention
energy sources such as wind energy, solar energy in solar thermal power generation to full fill
our future energy requirements.
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5. TERI, 2005.TERI Energy Data Directory and Yearbook 2004- 05(TEDDY).TERI (Tata
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Electricity forecasting of jammu & kashmir a methodological comparison