Title: Offshore wind power prediction based on chaotic optimisation PSO-SCN-LSTM model

Authors: Xu Li; Lei Kou; Benfa Zhang; Zhen Wang; Jingya Wen; Fangfang Zhang; Jinyan Du; Yan Zhou; Wende Ke

Addresses: Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' State Grid Songyuan Power Supply Company, Songyuan, 138099, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266075, China ' Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

Abstract: Offshore wind power, as a clean energy source, is receiving increasing attention worldwide. To enhance the economic and safety performance of offshore wind power, short-term forecasting of wind power is essential. This paper proposes a model based on chaos optimisation integrated with particle swarm optimisation (PSO), stochastic configuration network (SCN), and long short-term memory (LSTM) algorithm. Firstly, leveraging the randomness and ergodicity of the complex logistic chaos system, the collected power data from wind turbines is utilised as the input data source for the PSO, enhancing the randomness of the data. Subsequently, the SCN is employed to optimise the PSO, increasing the variation in the hidden layer during iterations and mitigating the PSO's tendency to fall into local optima, thereby obtaining initial prediction values. Finally, the mechanism model of the LSTM is utilised for secondary prediction, further improving prediction accuracy. Compared with traditional algorithms, the optimised algorithm significantly reduces errors and enhances prediction precision.

Keywords: chaos theory; particle swarm optimisation algorithm; random configuration network; LSTM prediction model.

DOI: 10.1504/IJCSM.2025.146079

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.1, pp.48 - 63

Received: 12 Aug 2024
Accepted: 28 Nov 2024
Published online: 06 May 2025 *

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