Short-term wind power electricity generation forecasting: A four-method combined model
ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, vol.20, no.1, 2025 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 20 Issue: 1
- Publication Date: 2025
- Doi Number: 10.1080/15567249.2025.2585462
- Journal Name: ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Environment Index, Greenfile, INSPEC, Public Affairs Index
- Middle East Technical University Affiliated: Yes
Abstract
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.