Comparisons of machine learning methods for electricity regional reference price forecasting.

Title: Comparisons of machine learning methods for electricity regional reference price forecasting.
Creator: Meng, Ke; Dong, Zhaoyang; Wang, Honggang; Wang, Youyi
Relation: 6th International Symposium on Neural Networks, ISNN 2009. Proceedings of the 6th International Symposium on Neural Networks (Wuhan, China 26-29 May, 2009) p. 827-835
Publisher Link: http://dx.doi.org/10.1007/978-3-642-01507-6_93
Publisher: Springer
Resource Type: conference paper
Date: 2009
Description: Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. In this paper, we investigate two state-of-the-art statistical learning based machine learning techniques for electricity regional reference price forecasting, namely support vector machine (SVM) and relevance vector machine (RVM). The study results achieved show that, the RVM outperforms the SVM in both forecasting accuracy and computational cost.
Subject: electricity reference price forecasting; support vector machine; relevance vector machine
Identifier: http://hdl.handle.net/1959.13/1058100
Identifier: uon:16333
Identifier: ISBN:9783642015069
Language: eng
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