Electricity price forecasting of deregulated market using Elman Neural Network

Price forecasting is one of the main issues faced in deregulated market because of the dynamic behaviour of the electricity prices. In a day-ahead pool market, market participants need forecasted prices to submit their bids to the market operator. Accurate forecast can provide a risk free environment for the producers and consumers to invest into the market. Participants themselves feel that they can have assured return if the forecasted prices are accurate. This paper presents Elman Neural Network to forecast the dynamics in the electricity prices accurately. The proposed method has been tested on Mainland Spain market to forecast the market clearing prices and found to be an efficient method in comparison with many existing methods.

[1]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[2]  Mohammad Kazem Sheikh-El-Eslami,et al.  Price forecasting of day-ahead electricity markets using a hybrid forecast method , 2011 .

[3]  Maria L. Rizzo,et al.  Measuring and testing dependence by correlation of distances , 2007, 0803.4101.

[4]  J. Contreras,et al.  Forecasting next-day electricity prices by time series models , 2002 .

[5]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[6]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[7]  N. Amjady Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[8]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[9]  Vassilis P. Plagianakos,et al.  An Improved Backpropagation Method with Adaptive Learning Rate , 1998 .

[10]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[11]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[12]  Narayanan Kumarappan,et al.  Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network , 2013, IEEE Systems Journal.