TY - JOUR
T1 - Forecasting electricity market prices
T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
AU - Xu, Y. Y.
AU - Hsieh, Rex
AU - Lu, Y. L.
AU - Shen, Y. C.
AU - Chuang, S. C.
AU - Fu, H. C.
AU - Bock, Christoph
AU - Pao, Hsiao-Tien
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=10944222255&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2004.1381097
DO - 10.1109/IJCNN.2004.1381097
M3 - Conference article
AN - SCOPUS:10944222255
SN - 1098-7576
VL - 4
SP - 2789
EP - 2794
JO - IEEE International Conference on Neural Networks - Conference Proceedings
JF - IEEE International Conference on Neural Networks - Conference Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -