Comparisons of Machine Learning Methods for Electricity Regional Reference Price Forecasting

Authors:
Ke Meng;Zhaoyang Dong;Honggang Wang;Youyi Wang
Affiliations:
School of Information Technology & Electrical Engineering, the University of Queensland, St. Lucia, Australia QLD 4072;Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong,;State Key Laboratory of Chemical Engineering, East China University of Science & Technology, Shanghai, China 200237;School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

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.