Short-term wind power electricity generation forecasting: A four-method combined model

YÜKSEL HALİLOĞLU E., Kazak S., Erdogan M. R., Berument H.

Energy Sources, Part B: Economics, Planning and Policy, cilt.20, sa.1, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/15567249.2025.2585462
  • Dergi Adı: Energy Sources, Part B: Economics, Planning and Policy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Environment Index, Greenfile, INSPEC, Public Affairs Index
  • Anahtar Kelimeler: Extreme learning machine, hybrid model, long short-term memory neural network, variational mode decomposition, wind power forecast
  • Gazi Üniversitesi Adresli: Evet

Özet

We integrate the long short-term memory (LSTM) network into an ensemble model composed of the least squares support vector machine, echo state network, and extreme-learning machine for wind power generation forecasting. This study presents the first unified forecasting framework that combines these four machine learning techniques to evaluate their collective efficacy in improving prediction accuracy for wind power. Empirical analyses demonstrate that incorporating LSTM into the ensemble does not yield performance improvements over the three-method model. These findings indicate that the added complexity from LSTM does not enhance forecasting accuracy. Additionally, the choice of loss function is observed to have a negligible impact on the models’ predictive performance.