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 paper proposes an adaptively trained neural network to forecast the 24 day-ahead market-clearing prices. The adaptive training mechanism includes a feedback process that allows the artificial neural network to learn from its mistakes and correct its output by adjusting its architecture as new data becomes available. The methodology is applied to the California power market and the results prove the efficiency and practicality of the proposed method.
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Georgilakis, P.S. (2006). Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_8
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DOI: https://doi.org/10.1007/11752912_8
Publisher Name: Springer, Berlin, Heidelberg
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