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An enhanced radial basis function network for short-term electricity price forecasting

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  • Lin, Whei-Min
  • Gow, Hong-Jey
  • Tsai, Ming-Tang

Abstract

This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the "spikes" could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment.

Suggested Citation

  • Lin, Whei-Min & Gow, Hong-Jey & Tsai, Ming-Tang, 2010. "An enhanced radial basis function network for short-term electricity price forecasting," Applied Energy, Elsevier, vol. 87(10), pages 3226-3234, October.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:10:p:3226-3234
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    References listed on IDEAS

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    1. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
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    5. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
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