An analysis of short-term price forecasting of power market by using ANN

In deregulated power markets, forecasting electricity parameters are most essential tasks & basis for any decision making. Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. Accurate, short-term price forecasting is an essential instrument which provides crucial information for power producers and consumers to develop accurate bidding strategies in order to maximize their profit. In this paper artificial intelligence (AI) has been applied in short-term price forecasting that is, the day-ahead hourly forecast of the electricity market price. A new artificial neural network (ANN) has been used to compute the forecasted price in ISO New England market using MATLAB R13. The data used in the forecasting are hourly historical data of the temperature, electricity load and natural gas price of ISO New England market. The simulation results have shown highly accurate day-ahead forecasts with very small error in price forecasting.

[1]  Kapil Sharma,et al.  Bayesian spam classification: Time efficient radix encoded fragmented database approach , 2014, 2014 International Conference on Computing for Sustainable Global Development (INDIACom).

[2]  T. Senjyu,et al.  A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method , 2007, IEEE Transactions on Power Systems.

[3]  AN APPROACH TO SHORT TERM LOAD FORECASTING USING MARKET PRICE SIGNAL , 2007 .

[4]  Paras Mandal,et al.  A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting , 2013, IEEE Transactions on Power Systems.

[5]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[6]  Michael Negnevitsky,et al.  An Effort to Optimize Similar Days Parameters for ANN-Based Electricity Price Forecasting , 2008, IEEE Transactions on Industry Applications.

[7]  T. Senjyu,et al.  Several-hours-ahead electricity price and load forecasting using neural networks , 2005, IEEE Power Engineering Society General Meeting.

[8]  Paras Mandal,et al.  An overview of forecasting problems and techniques in power systems , 2009, 2009 IEEE Power & Energy Society General Meeting.

[9]  T. Funabashi,et al.  Forecasting several-hours-ahead electricity demand using neural network , 2004, 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings.

[10]  Jung-Wook Park,et al.  Sensitivity Analysis of Similar Days Parameters for Predicting Short-Term Electricity Price , 2007, 2007 39th North American Power Symposium.