Forecasting electricity market pricing using artificial neural networks

Hsiao-Tien Pao*

*此作品的通信作者

研究成果: Article同行評審

131 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)907-912
頁數6
期刊Energy Conversion and Management
48
發行號3
DOIs
出版狀態Published - 1 3月 2007

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