ELECTRICAL ENGINEERING, vol.85, no.4, pp.229-233, 2003 (SCI-Expanded)
Article / Article
Science Citation Index Expanded (SCI-EXPANDED), Scopus
artificial intelligence, clustering, data forecasting, hybrid learning, neural networks, NEURAL-NETWORKS
Middle East Technical University Affiliated:
Four methods are developed for short-term load forecasting and are tested with the actual data from the Turkish Electrical Authority. The method giving the most successful forecasts is a hybrid neural network model which combines off-line and on-line learning and performs real-time forecasts 24-hours in advance. Loads from all day types are predicted with 1.7273% average error for working days, 1.7506% for Saturdays and 2.0605% for Sundays.