Electricity Price Forecasting Using Generalized Regression Neural Network (GRNN) for PJM Electricity Market

M. M. Tripathi(1*), K. G. Upadhyay(2), S. N. Singh(3)

(1) DOEACC Society Gorakhpur centre, M. M. M. Engineering College campus, India
(2) Electrical Engg. Department, M. M. M. Engineering College campus, India
(3) Electrical Engineering Department, I.I.T. Kanpur, India
(*) Corresponding author


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Abstract


In competitive electricity markets, price forecasting is becoming increasingly relevant to power producers and consumers. Price forecasts provide crucial information for power producers and consumers to develop bidding strategies in order to maximize benefit. This paper provides a method for predicting day-ahead electricity prices in the PJM market using General Regression Neural Network (GRNN) computing technique. Publicly available data acquired from the PJM electricity market were used for training and testing the ANN. The results obtained through the simulation show that the proposed algorithm is efficient, accurate and produce better results.
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Keywords


Generalized Regression Neural Network; Radial Basis Function Network; Day-Ahead Electricity Market; Price Forecasting

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