A soft computing system for day-ahead electricity price forecasting

Applied Soft Computing Journal

Volumn 10, Issue 3, 2010, Pages 868-875

  Niu, Dongxiao ^a   Liu, Da ^a   Wu, Desheng Dash ^b  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b REYKJAVIK UNIVERSITY   (Iceland)

Author keywords

Electricity price forecasting; Particle Swarm Optimization; Self Organizing Mapping (SOM); Support Vector Machine (SVM)

Indexed keywords

BIDDING STRATEGY; COMPETITIVE ELECTRICITY MARKETS; DAY-AHEAD PRICES; ELECTRICITY MARKET; ELECTRICITY PRICE FORECASTING; ELECTRICITY PRICES; ENERGY PRICES; FORECASTING ACCURACY; HOURLY PRICES; MARYLAND; NEW JERSEY; NOVEL TECHNIQUES; PARTICLE SWARM OPTIMIZATION ALGORITHM; PEAK PRICES; PENNSYLVANIA; SELF-ORGANIZING MAP NEURAL NETWORK; SELF-ORGANIZING MAPPING; SVM MODEL; TRAINING DATA;

COMMERCE; CONFORMAL MAPPING; COSTS; ELECTRIC LOAD FORECASTING; ELECTRICITY; NEURAL NETWORKS; PROFITABILITY; SOFT COMPUTING; STRENGTH OF MATERIALS; SUPPORT VECTOR MACHINES;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77949261563     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.10.004     Document Type: Article

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