Four methods for short-term load forecasting using the benefits of artificial intelligence

Erkmen I., Topalli A.

ELECTRICAL ENGINEERING, cilt.85, sa.4, ss.229-233, 2003 (SCI-Expanded) identifier identifier

  • 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.