Forecasting electricity market prices: A neural network based approach

Y. Y. Xu*, Rex Hsieh, Y. L. Lu, Y. C. Shen, S. C. Chuang, H. C. Fu, Christoph Bock, Hsiao-Tien Pao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


This paper presents a neural network approach to forecast the Phelix Base (PB) electricity market prices for European Energy Exchange (EEX). Up to now there has been little scientific work on forecasting the price development on the electricity markets. In this study, the Phelix Base moving average (PBMA), the moving difference (PBMD), and multilayer feed-forward neural networks (MLNN) are used to predict various period for 7, 14, 21, 28, 63, 91, 182, and 273 days ahead of electric prices. The experimental results of forecasting by MLNNs and linear methods (autoregressive error model) are compared and discussed. The MLNNs outperform from 11.4% to 64.6% superior to the traditional linear regression method. It seems that the proposed MLNN can be very useful in predicting the electricity market prices of EEX.

Original languageEnglish
Pages (from-to)2789-2794
Number of pages6
JournalIEEE International Conference on Neural Networks - Conference Proceedings
StatePublished - 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004


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