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dc.contributor.authorXu, YYen_US
dc.contributor.authorHsieh, Ren_US
dc.contributor.authorLu, Yen_US
dc.contributor.authorShen, YCen_US
dc.contributor.authorChuang, SCen_US
dc.contributor.authorFu, HCen_US
dc.contributor.authorBock, Cen_US
dc.contributor.authorPao, HTen_US
dc.date.accessioned2014-12-08T15:25:47Z-
dc.date.available2014-12-08T15:25:47Z-
dc.date.issued2004en_US
dc.identifier.isbn0-7803-8359-1en_US
dc.identifier.issn1098-7576en_US
dc.identifier.urihttp://hdl.handle.net/11536/18209-
dc.description.abstractThis 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 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.en_US
dc.language.isoen_USen_US
dc.titleForecasting electricity market prices: A neural network based approachen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGSen_US
dc.citation.spage2789en_US
dc.citation.epage2794en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000224941900481-
Appears in Collections:Conferences Paper