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Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
Electricity forecasting of jammu & kashmir a methodological comparison
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Electricity forecasting of jammu & kashmir a methodological comparison

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  • 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME416ELECTRICITY FORECASTING OF JAMMU & KASHMIR:A METHODOLOGICAL COMPARISONAMEESH KUMAR SHARMAElectrical Site Incharge Schneider Electric Infrastructure Ltd, Jammu, IndiaANKUSH GUPTAAssistant Professor Vaishno Group of Engineering and Technology, Jammu, IndiaUMESH SHARMAInstructor in IISD (Indian Institute of Skill Development), Jammu, IndiaABSTRACTThe 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.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 benarrowed, but the state will also be relieved of the heavy expenditure incurred on PowerProcurement.To cope up with above problems it is essential to know the future electricity demand. Inthis project we have forecasted the future sector wise electricity demand by using two timeseries methods (Exponential and ARIMA method) and compared the results for anydiscrepancies in mythologies.As can been seen from the results that for long term forecasting in some casesExponential method is more accurate than ARIMA method & in some cases ARIMA methodis more accurate than Exponential method. The actual comparison is done with the help of bargraph 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.aspJournal Impact Factor (2013): 5.5028 (Calculated by GISI)www.jifactor.comIJEET© I A E M E
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME417KEYWORDS: Forecasting, Electricity requirement, SPSS Soft wearINTRODUCTIONThe 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.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.The state of Jammu & Kashmir is located in the extreme north of India and is bound on thenorth by China and on the south by Himachal Pradesh and on the west by Pakistan. The statehas 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 Industraverses 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 andrainfall makes it heaven for hydel generation. The State is endowed with a hydro powerpotential of 20,000 MWS out of which a mere 11.68 % i.e. 2336.20 MWs has been harnessedso far.Despite hydropower being recognized as one of the most economic and preferredsource of electricity with it being the best choice for meeting peak demands, the depletedcapacity of hydro stations during winter months poses serious challenges to the StateGovernment in providing electric power supply to its people during the period. Due to purchaseof considerable amounts of power from the northern grid and overdraw under UI regime tomeet 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 whichabout 1300 units from Baglihar HEP are traded through PTC. The balance electricityrequirement of the State is met through imports / purchases from the Central Power GeneratingStations through the Northern Grid. It is, therefore, evident that the State is largely dependenton import of power. The extent of dependence has been increasing every year and is expected tocontinue 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 benarrowed, but the state will also be relieved of the heavy expenditure incurred on PowerProcurement. It is, therefore, a matter of great importance that the hydel potential of the state isharnessed within the shortest possible time.With huge hydro-potential on one side and increasing power demand adverselyaffecting its economy on the other side, the State is continuously losing the opportunity ofreducing huge expenditure possible in the event of development of available potential.
  • 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME418Government of Jammu & Kashmir has laid maximum emphasis on the fulldevelopment of its hydro potential being clean & renewable source of energy. It has been aliveto the need for encouraging private sector participation in development of Hydro Projects. Theprocess of exploitation of hydel potential in small hydro sector through private sectorparticipation 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 tovarious 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 hydropower as far back as the 1stdecade of this century.METHODOLOGYThere 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.
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME419Another 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 forecastplays 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.
  • 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME420MODELING TOOL AND SOFTWARESPSS (originally, Statistical Package for the Social Sciences) was released in itsfirst version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull. NormanNie was then a political science postgraduate at Stanford University, and now ResearchProfessor in the Department of Political Science at Stanford and Professor Emeritus ofPolitical Science at the University of Chicago. SPSS is among the most widely usedprograms for statistical analysis in social science. It is used by market researchers, healthresearchers, survey companies, government, education researchers, marketingorganizations and others. The original SPSS manual (Nie, Bent & Hull, 1970) has beendescribed as one of "sociologys most influential books". In addition to statistical analysis,data management (case selection, file reshaping, creating derived data) and datadocumentation (a metadata dictionary is stored in the datafile) are features of the basesoftware.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), DiscriminantThe many features of SPSS are accessible via pull-down menus or can beprogrammed with a proprietary 4GL command syntax language. Command syntaxprogramming has the benefits of reproducibility; simplifying repetitive tasks; and handlingcomplex data manipulations and analyses. Additionally, some complex applications canonly be programmed in syntax and are not accessible through the menu structure. Thepull-down menu interface also generates command syntax, this can be displayed in theoutput 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. Programscan be run interactively or unattended using the supplied Production Job Facility.Additionally a "macro" language can be used to write command language subroutines and aPython programmability extension can access the information in the data dictionary anddata and dynamically build command syntax programs. The Python programmabilityextension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" formost purposes, although SaxBasic remains available. In addition, the Python extensionallows SPSS to run any of the statistics in the free software package R. From version 14onwards SPSS can be driven externally by a Python or a VB.NET program using supplied"plug-ins".
  • 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME421RESULTS AND DISCUSSIONfig: 4.1 Domestic energy required by Exponential & ARIMA method010002000300040005000600070002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsenergy(millionKwh)domestic energy requiredby exponential methodenergy required by ARIMAmethodWe have done the comparison between domestic energy required by Exponentialmethod and domestic energy required by ARIMA method. Here in the above graph the energywith in the starting years predicated by Exponential method and ARIMA method have veryshort gap between them. As we progress further the gap goes on increasing at a faster rate. Inthe year 2040 the energy required by Exponential is about 6328.20Million Kwhr and energyrequired by ARIMA method is about 3414.99Million Kwhr. By comparing the partialautocorrelation of Exponential method and ARIMA method as shown in the Appendix B, wecome to the conclusion that forecasting by ARIMA method is more accurate because in thegraph of partial autocorrelation of ARIMA method the values when averaged almost weightzero which is actually required for better forecasting.We have done the comparison between commercial energy required by Exponentialmethod and commercial energy required by ARIMA method. Here in the above graph theenergy with in the starting years predicated by Exponential method and ARIMA method havevery short gap between them. As we progress further the gap goes on increasing at a faster ratejust like as we discuss in the domestic case. In the year 2040 the energy required byExponential is about 1820.73Million Kwhr and energy required by ARIMA method is about1093.09Million Kwhr. By comparing the partial autocorrelation of Exponential method andARIMA method as shown in the Appendix B, we come to the conclusion that forecasting byExponential method is more accurate because in the graph of partial autocorrelation ofExponential method the values when averaged almost weight zero and no specific pattern wasfollowed by the graph in case of Exponential method which is actually required for betterforecasting.
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME422Commercial energy required by Exponential & ARIMA methods02004006008001000120014001600180020002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsEnergy(millionKwh)Commercial energy requiredby exponential methodcommercial energy required byARIMA methodAgriculture Energy required by Exponential & ARIMA method02004006008001000120014002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsEnergy(MillionKwh)Agriculture energy required byexponential methodAgriculture energy required byARIMA methodWe have done the comparison between Agriculture energy required by Exponentialmethod and Agriculture energy required by ARIMA method. Here in the above graph theenergy with in the starting years predicated by Exponential method and ARIMA method havevery short gap between them. As we progress further the gap goes on increasing at a faster rate
  • 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME423just like as we discuss in the domestic & commercial case but here the forecasted data isincreasing exponentially in case of ARIMA method. In the year 2040 the energy required byExponential method is about 381.03Million Kwhr and energy required by ARIMA method isabout 1259.50Million Kwhr. By comparing the partial autocorrelation of Exponential methodand ARIMA method as shown in the Appendix B, we come to the conclusion that forecastingby ARIMA method is more accurate because in the graph of partial autocorrelation of ARIMAmethod the values when averaged almost weight zero which is actually required for betterforecasting and no specific pattern was followed by the graph in case of ARIMA method whichis actually required for better forecasting.Industrial energy required by Exponential & ARIMA methods0200040006000800010000120002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsEnergy(MillionKwh)Industrial energy required byexponential methodIndustrial energy required byARIMA methodWe have done the comparison between Industrial energy required by Exponentialmethod and Industrial energy required by ARIMA method. Here in the above graph the energywith in the starting years predicated by Exponential method and ARIMA method have veryshort gap between them. As we progress further the gap goes on increasing at a faster rate justlike as we discuss in the domestic & commercial case but here the forecasted data is increasingexponentially in case of ARIMA method. In the year 2040 the energy required by Exponentialis about 2444.31Million Kwhr and energy required by ARIMA method is about9666.78Million Kwhr. By comparing the partial autocorrelation of Exponential method andARIMA method as shown in the Appendix B, we come to the conclusion that forecasting byExponential method is more accurate because in the graph of partial autocorrelation ofExponential method the values when averaged almost weight zero which is actually requiredfor better forecasting and no specific pattern was followed by the graph in case of Exponentialmethod which is actually required for better forecasting.
  • 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME424public sector energy requirement by Exponential & ARIMA method010002000300040005000600070002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsenergy(MillionKwh)Public energy required byexponential methodPublic energy required by ARIMAmethodWe have done the comparison between Public energy required by Exponential methodand Public energy required by ARIMA method. Here in the above graph the energy with in thestarting years predicated by Exponential method and ARIMA method have very short gapbetween them. As we progress further the gap goes on increasing but here the forecasted data isincreasing exponentially in both the cases. In the year 2040 the energy required by Exponentialis about 5491.94Million Kwhr and energy required by ARIMA method is about6078.75Million Kwhr. By comparing the partial autocorrelation of Exponential method andARIMA method as shown in the Appendix B, we come to the conclusion that forecasting byExponential method is more accurate because in the graph of partial autocorrelation ofExponential method the values when averaged almost weight zero which is actually requiredfor better forecasting and no specific pattern was followed by the graph in case of Exponentialmethod which is actually required for better forecasting.Also have done the comparison between others energy required by Exponential methodand others energy required by ARIMA method. Here in the above graph the energy with in thestarting years predicated by Exponential method and ARIMA method have very short gapbetween them. As we progress further the gap goes on increasing at a faster rate, here theforecasted data is increasing exponentially in case of ARIMA method. In the year 2040 theenergy required by Exponential is about 2020.50Million Kwhr and energy required by ARIMAmethod is about 6430.01Million Kwhr. By comparing the partial autocorrelation ofExponential method and ARIMA method as shown in the Appendix B, we come to theconclusion that forecasting by Exponential method is more accurate because in the graph ofpartial autocorrelation of Exponential method the values when averaged almost weight zerowhich is actually required for better forecasting and no specific pattern was followed by thegraph in case of Exponential method which is actually required for better forecasting.
  • 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME425Others sector energy requirement by Exponential & ARIMA methods010002000300040005000600070002005-06 2010-11 2015-16 2020-21 2025-26 2030-31 2035-36yearsEnergy(MillionKwh)others energy required byexponential methodothers energy required by ARIMAmethodCONCLUSION AND RECOMMENDATIONSIn our minor project exponential method is more accurate than the ARIMA methodbecause in that case the data which we are dealing with is not a very fluctuating data. But herethe data with which we are dealing with is highly fluctuating one as we can see in the case ofenergy requirement in agriculture sector at the starting the values are increasing but in themiddle years there is a sudden drop in the energy requirement so in this project of electricityforecasting of Jammu and Kashmir both the methods are accurate. As in some cases ARIMAmethod has better forecast than the Exponential method and in some cases exponential methodhas better forecast than ARIMA method. Also as the actual data is highly fluctuating one so incase of exponential method we have taken the log of all the reading in SPSS software in all thesectors so that the actual data gets smoothen but we find that the actual data is still not sosmoothen than we have taken the square root of all the reading in the SPSS software of all thesectors so that our actual data gets more smoothen and we can get more accurate results. Aftertaking the square root we have check the smoothen data with actual data by forming the datachats with in the SPSS software.The final result is taken out by simply comparing the partial autocorrelation ofExponential method and ARIMA method. Finally we can say that the actual data is highlyfluctuating one so for a better or we can say for more accurate forecast we require more precisereading (e.g. monthly reading or half yearly) so that our forecasted data can come more closerto the reality.Long-term electricity demand forecasting in power systems is a complicated taskbecause it is affected directly or indirectly by various factors primarily associated with theeconomy and the population. In this project, two methods have been applied, first is the
  • 11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME426Exponential smoothing method and second is ARIMA method. The methods provide highaccuracy 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 electricitydemand can be expected in Jammu & Kashmir until 2039-40. The forecast faster growth inelectricity consumption is consistent with the anticipated, relatively moderate rate of economicgrowth in Jammu & Kashmir in the coming decades. In addition, the faster growth in electricityconsumption also reflects the fact that there will be further structural changes in the Indianeconomy and that subsequently some energy-intensive sectors in the economy are expected togrow. Concomitant with the faster growth in electricity demand will be a continuation of thechange in the market shares, with oil, natural gas, and hydroelectricity becoming increasinglyimportant energy sources at the expense of coal, reflecting government policies towards the useof cleaner energy in Jammu & Kashmir.Finally it is therefore being recommended that we should find some other sources ofenergy so that we can able to full fill our future requirements. As we all know that thepopulation of Jammu & Kashmir is increasing at a faster rate so we can use non conventionenergy sources such as wind energy, solar energy in solar thermal power generation to full fillour future energy requirements.REFERENCES1. Eltony, M.N., Hosque, A., 1997.A cointegrating relationship in the demand for energy:the case of electricity in Kuwait.J.Energy Devel.21 (2), 293- 301.2. Ghosh, S., 2002. Electricity consumption and economic growth in India. Energy Policy30,125-129.3. Majumdar, S.,Parikh, J.,1996. Energy demand forecasts with investmentconstraints.J.Forecast.15 (6), 459-476.4. Mallah, S. , Bansal, N.K.,2008. Sectorial analysis for electricity demand in India,presented in Int. Conference on Issues in Public Policy and Sustainable Development,March 26-28, 2008, IGNOU, New Delhi5. TERI, 2005.TERI Energy Data Directory and Yearbook 2004- 05(TEDDY).TERI (TataEnergy Research Institute), New Delhi.6. 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 – 6375.7. D.A.Kapgate and Dr.S.W.Mohod, “Short Term Load Forecasting using Hybrid Neuro-Wavelet Model”, International journal of Electronics and Communication Engineering&Technology (IJECET), Volume 4, Issue 2, 2013, pp. 280 - 289, ISSN Print: 0976-6464, ISSN Online: 0976 –6472.8. Balwant Singh Bisht and Rajesh M Holmukhe, “Electricity Load Forecasting by ArtificialNeural Network Model using Weather Data”, International Journal of ElectricalEngineering & Technology (IJEET), Volume 4, Issue 1, 2013, pp. 91 - 99, ISSN Print :0976-6545, ISSN Online: 0976-6553

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