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


研究成果: Conference article同行評審

8 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)2789-2794
期刊IEEE International Conference on Neural Networks - Conference Proceedings
出版狀態Published - 2004
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
持續時間: 25 7月 200429 7月 2004


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