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\title{A Heuristic Method for Short Term Load Forecasting Using Historical Data Strictly as per the compliance and regulations of}
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\begin{document}

             \author[1]{Dr.  D.V.Rajan}

             \affil[1]{  NATIONAL INSTITUTE OF TECHNOLOGY, DURGAPUR, WB,INDIA}

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\date{\small \em Received: 4 October 2011 Accepted: 29 October 2011 Published: 12 November 2011}

\maketitle


\begin{abstract}
        


 Load  forecasting  plays  an  important  role  in  power  system planning and operation.  In the  present complex power  system network under deregulated  regime, power generating  companies must  be able to forecast their system demand and  the corresponding price  in order  to make appropriate  market  decisions. Therefore, load forecasting, specially the short-term  load  forecasting  (STLF)  plays   an  important  role  for  energy  efficient and  reliable operation of a power system.  It provides  input  data   for many  operational  functions  of  power  systems  such  as  unit  commitment,  economic  dispatch,   and  optimal  power  flow and  security assessment.  This paper proposes  a  new  and  simple  technique   to  calculate  short  term  load  forecasting using historical data and applied it to the Damodar  Valley   Corporation  (DVC)  grid  operating  under  Eastern  Grid  (ERLDC-Eastern Regional Load Despatch  Centre),  India. This  gives  load  forecasts  half  an  hour  in  advance.  The  forecast  error  i.e. difference  between calculated forecast  load and real  time load is a measure of the accuracy of the system, is found   to be  lower  than other existing  techniques  like Holt's Method,  Chow's  Adaptive  Control  Method,   Brown's  One-Parameter Adaptive Method.

\end{abstract}


\keywords{HM-Holt's Method, CACM-Chow's Adaptive  Control Method, BOPAM-Brown's One- Parameter  Adaptive Method, RTL-Real time load  Mean Absolute  Percentage}

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\let\tabcellsep& 	 	 		 \par
A Heuristic Method for Short Term Load Forecasting Using Historical Data D.V.Rajan ? , C.Saravanan ? , S.S.Thakur ? A Abstract-Load forecasting plays an important role in power system planning and operation. In the present complex power system network under deregulated regime, power generating companies must be able to forecast their system demand and the corresponding price in order to make appropriate market decisions. Therefore, load forecasting, specially the short-term load forecasting (STLF) plays an important role for energy efficient and reliable operation of a power system. It provides input data for many operational functions of power systems such as unit commitment, economic dispatch, and optimal power flow and security assessment. This paper proposes a new and simple technique to calculate short term load forecasting using historical data and applied it to the Damodar Valley Corporation (DVC) grid operating under Eastern Grid (ERLDC-Eastern Regional Load Despatch Centre), India. This gives load forecasts half an hour in advance. The forecast error i.e. difference between calculated forecast load and real time load is a measure of the accuracy of the system, is found to be lower than other existing techniques like Holt's Method, Chow's Adaptive Control Method, Brown's One-Parameter Adaptive Method. n the present day, there are many issues and challenges in the deregulated electric power industry worldwide. The Indian power sector is undergoing structural metamorphosis and the various power generations, transmission and distribution companies are getting ready to take their rightful place in this sector to offer efficient service for which load forecasting is an effective tool. 
\section[{Keywords}]{Keywords}\par
Load forecasts with lead times from a few hours to seven days are essential in certain scheduling functions such as unit commitment and interchange evaluation. A wide variety of modeling techniques for STLF have been suggested in the literature in ref.  {\ref 1-6.} Conventional load forecasting methods like regression method in ref. \hyperref[b5]{7}  The short term load forecast plays an important role in economic operation and reliability of power systems. The main objective of the STLF is to advise the dispatcher in making a decision for economic dispatching. Therefore with an accurate model, it could also benefit dispatch systems to: supply load consistently estimate fuel allocation determine operational constraints determine equipment limitations\par
The second objective of the STLF is security assessment and system updation. STLF system requires offline historical data to do predictions. The data helps to run the model in advance, therefore allows dispatcher to provide corrective counter measure to the system.\par
In the proposed technique, historical load data obtained from DVC from the year 2010 to 2011 was used. The inputs used for the proposed method are, load at the particular time in the previous year, and two readings at half hour intervals of the same year along with the load of the half hour intervals in the present year. A mean absolute percentage error of 0.05\% was achieved over the period of data which was tested on 1 week data. This represents on average a high degree of accuracy in the load forecast.\par
Load forecasting is one of the most important inputs for prediction of electricity prices. The vital initiative behind prediction involves increasing number of models that estimate future values of an indicator based on its past values.\par
Load forecasting can be done for different durations i.e. long term forecasts with lead time of more than one year, medium term forecasts with the lead time of one week to one year, short term forecasts with lead A historical data method has been used in this work to develop a model to make predictions of the load half an hour in advance, based on the relationship of processed data of previous year and data available for the current year. In this paper, the proposed short term load forecast using historical data (STLFHD) method has been tested on DVC load data in which the forecast has been made based on load data at a particular time of the previous year in steps of half hour and one hour and corresponding load data of the current year.\par
An assumption has been made that the environment factor of power production system is same on the present day and the same day in the last year. Also, the two real time loads in thirty minutes difference is included in the calculation to make the forecast value more accurate.\par
The equations devised for load forecasting using historical data are as given below: Mean Absolute Deviation (MAD) is the final accuracy measurement. This error measurement is the average of the absolute value of the error without regard to whether the error was an over estimate or underestimate. 
\section[{Eqn (5)}]{Eqn (5)} 
\section[{Where}]{Where}\par
= actual load at particular time instant and = forecast load at that time.\par
The proposed technique was tested on historical data from the period 2010 to 2011. The data of first week February 2011 has been considered here for discussion and plotted graphs show for better understanding. Table \hyperref[tab_1]{1} shows morning peak and Table \hyperref[tab_2]{2} shows evening peak of Load data \& load forecast data respectively. Where FL represents Forecast Load and HD represents Historical data Also, the results obtained clearly demonstrate that the proposed method is simple, fast, reliable, accurate, and effective for short term load forecasting and that this method can perform good prediction with least error.    The results obtained in this work confirm the applicability as well as the efficiency of the proposed method in short-term load forecasting for the DVC grid load pattern located in eastern part of India. The method applied was able to determine the nonlinear relationship that exists between the historical load data supplied and on that basis, to make a prediction of what the load would be in the next half an hour.\par
The forecasting reliability of the proposed method was evaluated by computing the mean absolute error between the real time load and forecasted load. The results have shown that the prediction is more accurate with least error. Finally, we concluded this technique is simple and fast and could be an important tool for short term load forecasting for inter connected grid systems.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{23}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 2 Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{figure8}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.07083333333333333\textwidth}P{0.12142857142857141\textwidth}P{0.2125\textwidth}P{0.2125\textwidth}P{0.23273809523809524\textwidth}}
Time\tabcellsep Year\tabcellsep 01.02.11\tabcellsep 05.02.11\tabcellsep 07.02.11\\
\tabcellsep \tabcellsep \tabcellsep Load (MW)\tabcellsep Load (MW)\\
\tabcellsep 2011\tabcellsep Load (MW)\tabcellsep \tabcellsep \\
600\tabcellsep F FL\tabcellsep 1487\tabcellsep 1837\tabcellsep 2049.5\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.08976897689768977\textwidth}P{0.10099009900990098\textwidth}P{0.22722772277227724\textwidth}P{0.21600660066006602\textwidth}P{0.21600660066006602\textwidth}}
Time\tabcellsep Year\tabcellsep 02.02.11\tabcellsep 04.02.11\tabcellsep 06.02.11\\
\tabcellsep \tabcellsep \tabcellsep Load (MW)\tabcellsep Load (MW)\\
\tabcellsep 2011\tabcellsep Load (MW)\tabcellsep \tabcellsep \\
\tabcellsep FL\tabcellsep 1504\tabcellsep 1744\tabcellsep 1961\\
1800\tabcellsep HD\tabcellsep 1505\tabcellsep 1746\tabcellsep 1964\\
\tabcellsep FL\tabcellsep 1515.5\tabcellsep 1754\tabcellsep 1968.5\\
1830\tabcellsep HD\tabcellsep 1516\tabcellsep 1759\tabcellsep 1967\\
\tabcellsep FL\tabcellsep 1503\tabcellsep 1750.5\tabcellsep 1977.5\\
1900\tabcellsep HD\tabcellsep 1501\tabcellsep 1743\tabcellsep 1976\\
\tabcellsep FL\tabcellsep 1506\tabcellsep 1749\tabcellsep 1978\\
1930\tabcellsep HD\tabcellsep 1508\tabcellsep 1748\tabcellsep 1978\\
\tabcellsep FL\tabcellsep 1504.5\tabcellsep 1761\tabcellsep 1986\\
2000\tabcellsep HD\tabcellsep 1503\tabcellsep 1764\tabcellsep 1988\\
\tabcellsep FL\tabcellsep 1489.5\tabcellsep 1805.5\tabcellsep 1982\\
2030\tabcellsep HD\tabcellsep 1489\tabcellsep 1806\tabcellsep 1981\\
\tabcellsep FL\tabcellsep 1511.5\tabcellsep 1864\tabcellsep 1994\\
2100\tabcellsep HD\tabcellsep 1513\tabcellsep 1869\tabcellsep 1997\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.1608832807570978\textwidth}P{0.1555205047318612\textwidth}P{0.17160883280757097\textwidth}P{0.1635646687697161\textwidth}P{0.19842271293375394\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Methods}\tabcellsep \\
Date\tabcellsep HM\tabcellsep CACM\tabcellsep BOPAM\tabcellsep STLFHD\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep (Proposed\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Method)\\
01.02.11\tabcellsep 0.853771\tabcellsep 0.085133\tabcellsep 0.649826\tabcellsep 0.088758\\
02.02.11\tabcellsep 1.823455\tabcellsep 0.09901\tabcellsep 0.721414\tabcellsep 0.0955\\
03.02.11\tabcellsep 2.181319\tabcellsep 0.381517\tabcellsep 1.265112\tabcellsep 0.054279\\
04.02.11\tabcellsep 1.635235\tabcellsep 0.135338\tabcellsep 0.824362\tabcellsep 0.208704\\
05.02.11\tabcellsep 1.687037\tabcellsep 0.661841\tabcellsep 1.644067\tabcellsep 0.134027\\
06.02.11\tabcellsep 0.682334\tabcellsep 0.224032\tabcellsep 0.664018\tabcellsep 0.16012\\
07.02.11\tabcellsep 1.143343\tabcellsep 0.3451\tabcellsep 0.906755\tabcellsep 0.18286\end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.12921615201900238\textwidth}P{0.12315914489311164\textwidth}P{0.14334916864608077\textwidth}P{0.12921615201900238\textwidth}P{0.16152019002375295\textwidth}P{0.16353919239904988\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 11\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XI Issue v v v v VII Version I\\
Date\tabcellsep HM\tabcellsep Methods CACM\tabcellsep BOPAM\tabcellsep Proposed Method\tabcellsep Researches in Engineering\\
01.02.11 02.02.11 03.02.11 04.02.11 05.02.11\tabcellsep 0.673998 1.122309 1.588924 1.522361 1.18465\tabcellsep 0.120297 0.178835 0.847906 0.260466 1.633852\tabcellsep 0.404542 0.516869 1.126689 1.014949 1.571948\tabcellsep STLFHD 0.08681 0.09902 0.045264 0.284057 0.171404\tabcellsep Global Journal of\\
06.02.11\tabcellsep 0.543446\tabcellsep 0.44989\tabcellsep 0.71449\tabcellsep 0.144841\tabcellsep \\
07.02.11\tabcellsep 1.306622\tabcellsep 0.728371\tabcellsep 1.189224\tabcellsep 0.215153\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 			\footnote{( J ) 2011 December} 			\footnote{( J ) 2011 December} 			\footnote{December} 		 		\backmatter   			 \par
The authors acknowledge Damodar Valley Corporation for their constant support and cooperation in providing real time data to bring this work in this form. 			  			 
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