Dynamic ensemble wind speed prediction model based on hybrid deep reinforcement learning |
| |
Affiliation: | 1. Department of Civil & Environmental Engineering, National University of Singapore, Block E1A, #07-03, No.1 Engineering Drive 2, Singapore 117576, Singapore;2. Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, #06-01, Singapore 138602, Singapore;3. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;1. Applied Mechanics and Construction, University of Vigo, Spain;2. Chair of Computational Modelling and Simulation, Technical University of Munich, Germany;1. Faculty of Science, Agriculture, and Engineering, Newcastle University, Singapore 599493, Singapore;2. Xylem Inc, USA;3. Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, TX 78712, USA |
| |
Abstract: | Prediction of wind speed can provide a reference for the reliable utilization of wind energy. This study focuses on 1-hour, 1-step ahead deterministic wind speed prediction with only wind speed as input. To consider the time-varying characteristics of wind speed series, a dynamic ensemble wind speed prediction model based on deep reinforcement learning is proposed. It includes ensemble learning, multi-objective optimization, and deep reinforcement learning to ensure effectiveness. In part A, deep echo state network enhanced by real-time wavelet packet decomposition is used to construct base models with different vanishing moments. The variety of vanishing moments naturally guarantees the diversity of base models. In part B, multi-objective optimization is adopted to determine the combination weights of base models. The bias and variance of ensemble model are synchronously minimized to improve generalization ability. In part C, the non-dominated solutions of combination weights are embedded into a deep reinforcement learning environment to achieve dynamic selection. By reasonably designing the reinforcement learning environment, it can dynamically select non-dominated solution in each prediction according to the time-varying characteristics of wind speed. Four actual wind speed series are used to validate the proposed dynamic ensemble model. The results show that: (a) The proposed dynamic ensemble model is competitive for wind speed prediction. It significantly outperforms five classic intelligent prediction models and six ensemble methods; (b) Every part of the proposed model is indispensable to improve the prediction accuracy. |
| |
Keywords: | Wind speed prediction Dynamic ensemble Multi-objective optimization Deep reinforcement learning |
本文献已被 ScienceDirect 等数据库收录! |
|