Comparisons of Machine Learning Methods for Electricity Regional Reference Price Forecasting

被引：0

作者：

Meng, Ke ^{[1
]}

Dong, Zhaoyang ^{[2
]}

Wang, Honggang ^{[3
]}

Wang, Youyi ^{[4
]}

机构：

[1] Univ Queensland, Sch Informat Technol & Elect Engn, St Lucia, Qld 4072, Australia

[2] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

[3] East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

[4] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS | 2009年 / 5551卷

关键词：

Electricity reference price forecasting; Support vector machine; Relevance vector machine;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in all electricity market. In this paper, we investigate two state-of-the-art statistical learning, based machine learning techniques for electricity regional reference price forecasting, casting, 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.

引用

页码：827 / +

页数：2

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