Title: Forecasting electricity market pricing using artificial neural networks
Authors: Pao, Hsiao-Tien
Department of Management Science
Keywords: artificial neural network;European energy exchange;cross validation scherne;autoregressive error model;long-term forecasts
Issue Date: 1-Mar-2007
Abstract: Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long. (c) 2006 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.enconman.2006.08.016
ISSN: 0196-8904
DOI: 10.1016/j.enconman.2006.08.016
Volume: 48
Issue: 3
Begin Page: 907
End Page: 912
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