Forecasting electricity market prices: a neural network based approach

Author

Xu, Y.Y. ; Hsieh, Rex ; Lu, Y.L. ; Shen, Y.C. ; Chuang, S.C. ; Fu, H.C. ; Bock, Christoph ; Pao, H.T.

Author_Institution

Dept. of Comput. Eng., Nat. Chiao Tung Univ., Taiwan

Volume

4

fYear

2004

fDate

25-29 July 2004

Firstpage

2789

Abstract

This work 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 feedforward 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.

Keywords

autoregressive moving average processes; economic forecasting; feedforward neural nets; multilayer perceptrons; power markets; power system economics; pricing; European Energy Exchange; Phelix Base moving average; autoregressive error model; forecasting electricity market prices; multilayer feedforward neural networks; price development; Artificial neural networks; Economic forecasting; Educational institutions; Electricity supply industry; Electronic mail; Energy exchange; Load forecasting; Multi-layer neural network; Neural networks; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1381097

Filename

1381097