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Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks

  • Pavlos S. Georgilakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)

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.

Keywords

Artificial Neural Network Artificial Neural Network Model Electricity Price Bidding Strategy Artificial Neural Network Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pavlos S. Georgilakis
    • 1
  1. 1.Technical University of CreteKounoupidiana, ChaniaGreece

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