Wind power output prediction: a comparative study of extreme learning machine - Wang, Zheng-Chuang; Niu, Jin-Cai (Frontiers in Energy Research, 2023)
This study aims to propose a wind power prediction method that achieves high accuracy in order to minimize the impact of wind power on the power system and reduce scheduling difficulties in systems incorporating wind power. The importance of developing renewable energy has been recognized by society due to the increasing severity of the energy crisis. Wind energy offers advantages such as efficiency, cleanliness, and ease of development. However, the random nature of wind energy poses challenges to power systems and complicates the scheduling process. Therefore, accurate wind power prediction is of utmost importance. A wind power prediction model was constructed based on an improved tunicate swarm algorithm-extreme learning machine (ITSA-ELM). The improved tunicate swarm algorithm (ITSA) optimizes the random parameters of extreme learning machine (ELM), resulting in the best prediction performance. ITSA is an enhancement of the tunicate swarm algorithm (TSA), which introduces a reverse learning mechanism, a non-linear self-learning factor, and a Cauchy mutation strategy to address the drawbacks of poor convergence and susceptibility to local optima in TSA. Two different scenarios were used to verify the effectiveness of the ITSA-ELM. The results showed that the ITSA-ELM has a decrease of 1.20%, and 21.67% in MAPE respectively compared with the TSA-ELM in May and December. This study has significant implications for promoting the development of renewable energy and reducing scheduling difficulties in power systems.