Improve the unit commitment scheduling by using the neural-network-based short-term load forecasting

IEEE Transactions on Industry Applications

Volumn 41, Issue 1, 2005, Pages 169-179

  Saksornchai, Titti ^a,b   Lee, Wei Jen ^a   Methaprayoon, Kittipong ^a   Liao, James R ^c   Ross, Richard J ^c  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^b Novatis (Thailand) Ltd   (Thailand)

^c Western Farmers Electric Cooperative   (United States)

Author keywords

Neural networks; Production cost; Short term load forecast; Unit Commitment (UC)

Indexed keywords

COSTS; ELECTRIC LOAD FORECASTING; ELECTRIC POWER GENERATION; ENERGY MANAGEMENT; NEURAL NETWORKS; SCADA SYSTEMS;

PRODUCTION COST; SHORT TERM LOAD FORECAST; UNIT COMMITMENT (UC);

ELECTRIC POWER SYSTEMS;

EID: 13844256574     PISSN: 00939994     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIA.2004.841029     Document Type: Article

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