Genetic least square estimation approach to wind power curve modelling and wind power prediction

• Published in: Scientific reports Vol. 13; no. 1; p. 9188
• Main Authors: Wang, Zhiming, Wang, Xuan, Liu, Weimin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/4077; 639/4077/909; 639/4077/909/4110; Bayesian analysis; Humanities and Social Sciences; Mathematical models; multidisciplinary; Parameter estimation; Predictions; Renewable energy; Science; Science (multidisciplinary); Turbines; Wind farms; Wind power; Wind speed
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-36458-w

Wind power curve (WPC) is an important index of wind turbines, and it plays an important role in wind power prediction and condition monitoring of wind turbines. Motivated by model parameter estimation of logistic function in WPC modelling, aimed at the problem of selecting initial value of model parameter estimation and local optimum result, based on the combination of genetic algorithm and least square estimation method, a genetic least square estimation (GLSE) method of parameter estimation is proposed, and the global optimum estimation result can be obtained. Six evaluation indices including the root mean square error, the coefficient of determination R 2 , the mean absolute error, the mean absolute percentage error, the improved Akaike information criterion and the Bayesian information criterion are used to select the optimal power curve model in the different candidate models, and avoid the model’s over-fitting. Finally, to predict the annual energy production and output power of wind turbines, a two-component Weibull mixture distribution wind speed model and five-parameter logistic function power curve model are applied in a wind farm of Jiangsu Province, China. The results show that the GLSE approach proposed in this paper is feasible and effective in WPC modelling and wind power prediction, which can improve the accuracy of model parameter estimation, and five-parameter logistic function can be preferred compared with high-order polynomial and four-parameter logistic function when the fitting accuracy is close.