Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

• Main Authors: Li, Ling Ling, Zhao, Xue, Tseng, Ming Lang, Tan, Raymond Girard R.
• Format: text
• Published: Animo Repository 2020
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/895; https://animorepository.dlsu.edu.ph/context/faculty_research/article/1894/type/native/viewcontent
• Institution: De La Salle University

© 2019 Elsevier Ltd It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of wind power brings difficult challenges to the operation of the power system with the integration of wind farms into power grid. Accurate wind power prediction is increasingly important for the stable operation of wind farms and the power grid. This study is combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model. The adaptive learning factor and differential evolution strategy are introduced to improve the performance of traditional dragonfly algorithm. The improved dragonfly algorithm is used to choose the optimal parameters of support vector machine. The effectiveness of the proposed model has been confirmed on the real datasets derived from La Haute Borne wind farm in France. The proposed model has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression. The proposed model is suitable for short-term wind power prediction.