A neural network approach to m-daily-ahead electricity price prediction

Hsiao-Tien Pao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper proposes an artificial neural network (ANN) model to predict m-daily-ahead electricity price using direct forecasting approach on European Energy Exchange (EEX) market. The most important characteristic of this model is the single output node for m-period-ahead forecasts. The potentials of ANNs are investigated by employing cross-validation schemes. Out-of-sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are more robust multi-step-ahead forecasting method than autoregressive error models (AUTOREG). Moreover, ANN predictions are quite accurate even when the length of forecast horizon is relatively short or long.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings - Part II
PublisherSpringer Verlag
Pages1284-1289
Number of pages6
ISBN (Print)3540344373, 9783540344377
DOIs
StatePublished - 1 Jan 2006
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 28 May 20061 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3972 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
CountryChina
CityChengdu
Period28/05/061/06/06

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