Application of a new hybrid neuro-evolutionary system for day-ahead price forecasting of electricity markets

Authors:
Nima Amjady;Farshid Keynia
Affiliations:
Department of Electrical Engineering, Semnan University, Semnan, Iran;Department of Electrical Engineering, Semnan University, Semnan, Iran
Venue:
Applied Soft Computing
Year:
2010

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Abstract

In this paper, a new forecast strategy is proposed for day-ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. However, electricity price has a nonlinear, volatile and time dependent behavior owning many outliers. Our forecast strategy is composed of a preprocessor and a Hybrid Neuro-Evolutionary System (HNES). Preprocessor selects the input features of the HNES according to MRMR (Maximum Relevance Minimum Redundancy) principal. The HNES is composed of three Neural Networks (NN) and Evolutionary Algorithms (EA) in a cascaded structure with a new data flow among its building blocks. The effectiveness of the whole proposed method is demonstrated by means of real data of the PJM and Spanish electricity markets. Also, the proposed price forecast strategy is compared with some of the most recent techniques in the area.