Comparisons of machine learning methods for electricity regional reference price forecasting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5551 LNCS, Issue PART 1, 2009, Pages 827-835

  Meng, Ke ^a   Dong, Zhaoyang ^b   Wang, Honggang ^c   Wang, Youyi ^d  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

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

Indexed keywords

COMPUTATIONAL COSTS; ELECTRICITY MARKET; ELECTRICITY PRICES; FORECASTING ACCURACY; MACHINE LEARNING METHODS; MACHINE LEARNING TECHNIQUES; MARKET PARTICIPANTS; PRICE FORECASTING; RELEVANCE VECTOR MACHINE; RISK MANAGEMENT PLANS; STATISTICAL LEARNING; TWO-STATE;

COSTS; ELECTRICITY; IMAGE RETRIEVAL; LEARNING ALGORITHMS; NEURAL NETWORKS; RISK ANALYSIS; RISK MANAGEMENT; ROBOT LEARNING; SUPPORT VECTOR MACHINES; VECTORS;

ELECTRIC LOAD FORECASTING;

EID: 69949086026     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01507-6_93     Document Type: Conference Paper

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