International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 649...
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To forecast the future demand of electrical energy in india by arima

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To forecast the future demand of electrical energy in india by arima

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME197TO FORECAST THE FUTURE DEMAND OF ELECTRICAL ENERGYIN INDIA: BY ARIMA & EXPONENTIAL METHODSANKUSH GUPTAAssistant Professor Vaishno Group of Engineering and Technology, Jammu, IndiaAMEESH KUMAR SHARMAElectrical Site Incharge Schneider Electric Infrastructure Ltd, Jammu, IndiaUMESH SHARMAInstructor in IISD (Indian Institute of Skill Development), Jammu, IndiaABSTRACTIndia faces formidable challenges in meeting its energy needs and in providing adequateenergy of desired quality in various forms in a sustainable manner and at competitive prices.India needs to sustain an 8% to 10% economic growth rate, over the next 25 years. To deliver asustained growth rate of 8% through 2035-2040 and to meet the lifeline energy needs, Indianeeds at the very least , to increase its primary energy supply by 3 to 4 times and its electricitygeneration capacity/supply by 5 to 6 times of their 2005-6 levels.This may put strain on India power sector. There is therefore, need to formulate policyfor future investment in the power sector and allocate energy resources in an optimal way. Inaddition to the problem of adequate energy resource availability, any energy policy has toadequately consider the aspects of environmental emissions and resulting Green House Gases(GHG). Various models have been development all over the world to forecast future energydemand and allocation of energy resources.In this report long term electricity demand and supply have been performed by usingtime series methods that is Exponential and ARIMA methods. Since there will be always amethodological gap in forecasting the electricity. Therefore the comparison of the methods hasbeen implemented.In the first phase of the forecasting the comparison of the original and model data havebeen performed. It has been seen that these two are matches well with great accuracy.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH INENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online)Volume 4, Issue 2 March – April 2013, pp. 197-205© IAEME: www.iaeme.com/ijaret.aspJournal Impact Factor (2013): 5.8376 (Calculated by GISI)www.jifactor.comIJARET© I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME198As can been seen from the results that for long term forecasting Exponential method ismore accurate than ARIMA method. ARIMA method is more accurate in short termforecasting.In the year 2005-06 the electricity availability and requirement is 581.44(Billion KwH)and 629.63(Billion KwH). But at the end of this forecast by ARIMA method the electricityavailability and requirement is 567.75(Billion KwH) and 6057.20(Billion KwH). If wecontinue on this track we will never be able to meet our requirement demands in coming future,where as in Exponential method the growing demand of electricity requirement can be meet bythe equivalent or sub equivalent growth of electricity availability.KEYWORDS: Forecasting, Electricity requirement, SPSS Soft wear.INTRODUCTIONThe electricity demand forecast is an important input for planning of the power sector tomeet the future power requirement of various sectors of electricity consumption. A plannedload growth in industry, agriculture, domestic and other sectors is necessary to have unifiedgrowth in all sectors of economy and therefore it is necessary that infrastructure is planned invarious sectors of electricity consumption so as to direct the overall growth of economy inrational manner.Unbalanced Growth & shortagesAlong with this quantitative growth, the India electricity sector has also achievedqualitative growth. This is reflected in the advanced technological capabilities & largenumber of highly skilled personnel available in the country. While this must beappreciated, it must also be realized that the growth of the sector has been balanced. Theavailability of power has increased but demand has consistently out stripped supply &substantial energy & peak shortages of 7.1 % & 11.2% prevalent in India. The majorproblems in India are:• Lack of optimum utilization of the existing generation capacity.• In adequate inter-regional transmission links.• Inadequate and ageing sub-transmission & distribution network leading to power cuts &local failures / faults.• T&D losses, large scale theft & skewed tariff structure.• Slow pace of rural electrification• Lack of grid discipline.• Inefficient use of electricity by the end consumers.The primary objective of the electrical energy forecast is to assess the electricitydemand for States/UTs so that the States/UTs are able to plan and arrange the electricalinfrastructure to meet demand in full and provide electricity to all. The electricity demandforecast also works as a tool for planning the Demand Side Management (DSM) strategy onlong term basis for optimizing the peak demand and also plan long term tariff policy. In thisproject we have forecasted the total future electricity demand by using two time series methodsz (exponential and ARIMA method) and compared the results for any discrepancies inmythologies. Proper cares have been taken
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME199METHODOLOGYThere is an urgent need for precision in the demand forecasts. In the past, the worldover, an underestimate was usually attended to by setting up turbine generator plants fired bycheap oil or gas, since they could be set up in a short period of time with relatively smallinvestment. On the other hand, overestimates were corrected by demand growth. Theunderlying notion here was that in the worst case, there would be an excess capacity, whichwould 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 ofregulation and a favorable public image, all made the process of forecasting demand muchsimpler. In contrast, today an underestimate could lead to under capacity, which would result inpoor quality of service including localized brownouts, or even blackouts. An overestimatecould lead to the authorization of a plant that may not be needed for several years. Manyutilities 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 electricitysupply services, tariff reforms and rising role of the private sector, a realistic assessment ofdemand assumes ever-greater importance. These are required not merely for ensuring optimalphasing of investments, a long term consideration, but also rationalizing pricing structures anddesigning demand side management programs, which are in the nature of short- ormedium-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 forgas-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 forecastgrowth reverse or slowdown. In manufacturing institutions and electric utilities there are anumber of factors that drive the forecast, including market share. The forecast further drivesvarious plans and decisions on investment, construction and conservation. Since electricutilities are basically dedicated to the objective of serving consumer demands, in general theconsumer can place a reasonable demand on the system in terms of quantity of power. Withsome built-in reserve capacity, the utilities may have to configure a system to respond to theseto the extent possible. In the process of making predictions, forecaster bears in mind thefeedback effects of pricing and other policy changes, and therefore, participates in the processof designing ways and means to meet consumer demands.Another use for demand forecasting models is the assessment of the impact that a newtechnology might have on the energy consumption. This helps planners to evaluate the costeffectiveness of investing in the new technology and the strategy for its propagation. The use ofa straightforward engineering end-use approach that focuses only physical factors can miss theemergence of new end uses, as well as other effects such as the impact of rising energy prices asa stimulus to energy efficiency. Also the process of projecting the demand would requireestimating market penetration of various devices, while accounting for fuel substitution,average capacity and efficiency factors in the future, as well as average utilization rates. Thedemand forecasts are also done for each consumer category and voltage level. Charging thecommercial, industrial and large consumers a higher charge, which is used to subsidize socialreform programs, optimizes revenues while keeping social objectives in mind. The forecast
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME200plays an important role in identifying the categories which “can pay” and those that should besubsidized.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 acombination of these for purposes of prediction.TIME SERIES METHODSA time series is defined to be an ordered set of data values of a certain variable. Timeseries models are, essentially, econometric models where the only explanatory variables usedare lagged values of the variable to be explained and predicted. The intuition underlyingtime-series processes is that the future behavior of variables is related to its past values, bothactual and predicted, with some adaptation/adjustment built-in to take care of how pastrealizations deviated from those expected. Thus, the essential prerequisite for a time seriesforecasting technique is data for the last 20 to30 time periods. The difference betweeneconometric models based on time series data and time series models lies in the explanatoryvariables used. It is worthwhile to highlight here that in an econometric model, the explanatoryvariables (such as incomes, prices, population etc.) are used as causal factors while in the caseof time series models only lagged (or previous) values of the same variable are used in theprediction.In general, the most valuable applications of time series come from developingshort-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 timeseries models is their structural simplicity. They do not require collection of data on multiplevariables. Observations on the variable under study are completely sufficient. A disadvantageof these models, however, is that they do not describe a cause-and-effect relationship. Thus, atime 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 seriesmodels, there do exist technical problems wherein more than one of the variables is highlycorrelated with another (multi-co linearity), or with its own past values (auto-correlation). Thissort of a behavior between variables that are being used to arrive at any forecasts demandscareful treatment prior to any further analysis. These, along with other similar methodologicaloptions, need a careful assessment while working out forecasts of demand for any sector.MODELING TOOL AND SOFTWARESPSS (originally, Statistical Package for the Social Sciences) was released in its firstversion in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. Norman Nie wasthen a political science postgraduate at Stanford University, and now Research Professor in theDepartment of Political Science at Stanford and Professor Emeritus of Political Science at theUniversity of Chicago. SPSS is among the most widely used programs for statistical analysis insocial science. It is used by market researchers, health researchers, survey companies,government, education researchers, marketing organizations and others. The original SPSSmanual (Nie, Bent & Hull, 1970) has been described as one of "sociologys most influentialbooks". In addition to statistical analysis, data management (case selection, file reshaping,
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME201creating 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, DescriptiveRatio 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), DiscriminantThe many features of SPSS are accessible via pull-down menus or can be programmedwith a proprietary 4GL command syntax language. Command syntax programming has thebenefits of reproducibility; simplifying repetitive tasks; and handling complex datamanipulations and analyses. Additionally, some complex applications can only beprogrammed in syntax and are not accessible through the menu structure. The pull-down menuinterface also generates command syntax, this can be displayed in the output though the defaultsettings have to be changed to make the syntax visible to the user; or can be paste into a syntaxfile using the "paste" button present in each menu. Programs can be run interactively orunattended using the supplied Production Job Facility. Additionally a "macro" language can beused to write command language subroutines and a Python programmability extension canaccess the information in the data dictionary and data and dynamically build command syntaxprograms. The Python programmability extension, introduced in SPSS 14, replaced the lessfunctional SAX Basic "scripts" for most purposes, although SaxBasic remains available. Inaddition, the Python extension allows SPSS to run any of the statistics in the free softwarepackage R. From version 14 onwards SPSS can be driven externally by a Python or a VB.NETprogram using supplied "plug-ins".RESULTS AND DISCUSSIONEnergy security and environment problems are two issues which becoming very severein recent years major countries in the world are facing these problems now a days since powersector is one of the major emitter of green house gases in the world and India is not theexception. Most of the power generated in India is due to fossil fuels like coal, gas, and oil.About 40% of the carbon dioxide is only emitted by fossil fuel power plants and if the trendcontinues in the future India will become most polluted country in the world therefore it isnecessary to know the energy requirement in the future so that proper steps should be taken toreduce the green house gases and secure the energy future. In the following section we haveperformed the energy modeling by using SPSS modeling software for long term energyforecasting for India.As seen in the Figure 4.1 of electricity available by ARIMA method the values of theelectricity availability in 2005-06 year is 581.44(Billion KwH). The electricity availability in2039-40 year is 567.74(Billion KwH) as seen the values of electricity availability by thismethod remains constant throughout the forecasted period. From the graph of Exponentialmethod the electricity availability is increasing as we go along the forecasting period from589.88 billion KwH in the year 2005-06 to 2670.86 billion KwH in the year 2039-40.
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME202Fig 4.1 : ENERGY AVILABLE BY EXPONENTIAL & ARIMA methods0.00500.001000.001500.002000.002500.003000.002005-06 2009-10 2013-14 2017-18 2021-22 2025-26 2029-30 2033-34 2037-38yearsenergy(billionKwH)electricity avilable (exp method)electricity avilable (ARIMAMETHOD)fig4.2 : Electricity required by exponential method & ARIMA method010002000300040005000600070002005-06 2009-10 2013-14 2017-18 2021-22 2025-26 2029-30 2033-34 2037-38yearsenergy(billionKwH)ELECTRICITY REQUIRED IN BILLIONKwH (EXP METHOD)ELECTRICITY REQUIRED IN BILLIONKwH(ARIMA METHOD)
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME203As seen in the figure 4.2 electricity requirement by exponential method and ARIMAmethod the initial values from the methods are very much comparable to each other but as wego along the forecasting period the gape between the two values obtained by two methods goeson increasing and as the demand for electricity requirement by the fact information willincrease at a faster rate with in the coming years as shown by the ARIMA method. The year2005-06 the electricity requirement by ARIMA method is 629.82 (Billion KwH) and byExponential method is 618.85(Billion KwH). At the end of the forecast the electricityrequirement by ARIMA method is 6057.19 (Billion KwH) and by exponential method is3652.81 (Billion KwH).As from the graph of electricity availability and requirement byExponential method we are getting the information that in the starting values of electricityrequirement and electricity availability the gape between them is not very large. The electricityavailability also goes on increasing as we go along the forecasting period to cope up withincreasing rate of electricity requirement and from the graph we can see that in the later valuesthere is a gape between electricity availability and requirement which is permissible.fig 4.3 ENERGY AVILABLE & REQUIRED BY EXPONENTIAL METHOD050010001500200025003000350040002005-06 2009-10 2013-14 2017-18 2021-22 2025-26 2029-30 2033-34 2037-38yearsenergy(BILLIONKwH)electricity avilable(exponential method)electricity required(exponential method)
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME204fig 4.4: ENERGY AVILABLE AND REQUIRED BY ARIMA METHOD0.001000.002000.003000.004000.005000.006000.007000.002005-06 2009-10 2013-14 2017-18 2021-22 2025-26 2029-30 2033-34 2037-38YEARSENERGY(BILLIONKwH)ELECTRICITY AVILABLE(ARIMA METHOD)ELECTRICITY REQUIRE(ARIMA METHOD)By comparing energy requirement and energy availability by ARIMA method we aregetting the information that in the starting values the gape between electricity requirement andavailability is not very large. As we go along the forecasting period by ARIMA method thegape between the electricity availability and requirement is getting large. If we continue on thistrack we will never be able to meet our requirement demands in coming future. In the year2005-06 the electricity availability and requirement is 581.44(Billion KwH) and 629.63(BillionKwH). But at the end of this forecast the electricity availability and requirement is567.75(Billion KwH) and 6057.20(Billion KwH).COMPARISON BETWEEN EXPONENTIAL METHOD AND ARIMA METHODBy ARIMA method the gape between electricity availability and requirement is verylarge as availability is very less and requirement is very large, there will be a heavy shortage ofelectricity in the future if we continue our forecast by this method. So this method cannot beapplied for long term forecasting where as in Exponential method the growing demand ofelectricity requirement can be meet by the equivalent or sub equivalent growth of electricityavailability. Therefore, in the end we can say that the Exponential method has a better forecastthan the ARIMA method and Exponential method can be applied for the long term electricityforecasting.
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME205CONCLUSION AND RECOMMENDATIONSLong-term electricity demand forecasting in power systems is a complicated task because itis affected directly or indirectly by various factors primarily associated with the economy and thepopulation. In this project, two methods have been applied, first is the Exponential smoothingmethod and second is ARIMA method. The methods provide high accuracy forecasts for the year2039-40. This is very useful in planning fuel procurement, scheduling unit maintenance, andimports.The forecasts presented in this project suggest that significant growth in electricity demandcan be expected in India until 2039-40. The forecast faster growth in electricity consumption isconsistent with the anticipated, relatively moderate rate of economic growth in India in the comingdecades. In addition, the faster growth in electricity consumption also reflects the fact that therewill be further structural changes in the Indian economy and that subsequently someenergy-intensive sectors in the economy are expected to grow. Concomitant with the faster growthin 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 India.REFERENCES1. Balocco, C., Grazzini, G., 1997.A statistical method to evaluate urban energyneeds.Int.J.Energy Res.21 (14), 1321-1330.2. Mallah, S. , Bansal, N.K.,2008. Sectorial analysis for electricity demand in India, presented inInt. Conference on Issues in Public Policy and Sustainable Development, March 26-28,2008, IGNOU, New Delhi3. TERI, 2005.TERI Energy Data Directory and Yearbook 2004-05(TEDDY).TERI (TataEnergy Research Institute), New Delhi.4. Al-Zayer, J., Al-Ibrahim, A., 1996.Modeling the impact of temperature on electricityconsumption in the eastern province of Saudi Arabia.J.Forecast.15 (2), 97-106.5. Dincer, I., Dost, S., 1997.Energy and GDP.Int.J.Energy Res.21 (2), 153-167.6. Dalh, C., 1994.Asurvey of energy demand elasticities for the developing world.Journal ofEnergy Development, pp1-47.7. Erdogam, M., Dalh, C., 1997.Energy demand in Turkey. Journal of Energy Development ,21(2), 173-187.8. Eltony, M.N., Hosque, A., 1997.A cointegrating relationship in the demand for energy: thecase of electricity in Kuwait.J.Energy Devel.21 (2), 293- 301.9. Ghosh, S., 2002.Electricity consumption and economic growth in India. Energy Policy30,125-129.10. Dr. S. Rajamohan and S. Pasupathi, “Operational Efficiency and Times Series Changes inTaico Bank – Auto Regressive Integrated Moving Average (Arima) Model”, InternationalJournal of Management (IJM), Volume 2, Issue 1, 2011, pp. 79 - 83, ISSN Print: 0976-6502,ISSN Online: 0976-6510.11. M. Nirmala and S. M. Sundaram, “Modeling and Predicting the Monthly Rainfall inTamilnadu as a Seasonal Multivariate Arima Process”, International Journal of ComputerEngineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp. 103 - 111, ISSN Print: 0976– 6367, ISSN Online: 0976 – 6375.

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