Four methods for short-term load forecasting using the benefits of artificial intelligence
ELECTRICAL ENGINEERING, cilt.85, sa.4, ss.229-233, 2003 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 85 Sayı: 4
- Basım Tarihi: 2003
- Doi Numarası: 10.1007/s00202-003-0163-9
- Dergi Adı: ELECTRICAL ENGINEERING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.229-233
- Anahtar Kelimeler: artificial intelligence, clustering, data forecasting, hybrid learning, neural networks, NEURAL-NETWORKS
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.