ARTIFICIAL INTELLIGENCE SOLUTION TO ELECTRICITY PRICE FORECASTING PROBLEM

Authors:
Pavlos S. Georgilakis
Affiliations:
Department of Production Engineering and Management, Technical University of Crete, University Campus, Chania, Greece
Venue:
Applied Artificial Intelligence
Year:
2007

Citing 2
Cited 1

Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithm - neural network approach

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

GENETIC ALGORITHM MODEL FOR PROFIT MAXIMIZATION OF GENERATING COMPANIES IN DEREGULATED ELECTRICITY MARKETS

Applied Artificial Intelligence

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Abstract

The market-clearing prices in deregulated electricity markets are volatile. Good market-clearing price forecasting will help producers and consumers to prepare their corresponding bidding strategies so as to maximize their profits. Market-clearing price prediction is a difficult task since bidding strategies used by market participants are complicated and various uncertainties interact in an intricate way. This article proposes the use of two artificial neural networks: the first to predict the day-ahead load and the second to forecast the day-ahead market-clearing prices. The methodology is applied to the California power market. After determining the optimal artificial neural network architecture with the minimum mean absolute percentage error on the test set, this architecture is used for price forecasting in periods with price spikes, for price forecasting for weekends, and for week-ahead MCP forecasting during the four seasons of the year. The forecasting accuracy of the artificial neural network model is compared with the accuracy of the persistence method and the results prove the efficiency and practicality of the proposed technique.