Work

Research on neural network wind speed prediction model based on improved sparrow algorithm optimization

Year: 2022

Type: article

Abstract: The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.

Source: Energy Reports

DOI: 10.1016/j.egyr.2022.09.202

Authors: Liang Zhang, Shan He, Jing Cheng, Zhi Yuan, Xueqing Yan

Institution: Xinjiang University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,240