Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days - Yilun XU; Binqiao ZHANG; Jing HUANG; Xiao XIE; Ruoxin WANG; Danqing SHEN; Lina HE; Kaifan YANG (Zhongguo dianli)
In order to improve the prediction accuracy of photovoltaic power, the input of the photovoltaic power prediction model is determined according to the characteristics of photovoltaic output power under different weather types. Aiming at the defects of the wolf pack algorithm (WPA), an improved wolf pack algorithm (IWPA) was obtained by improving the walking position and running step of the wolf pack. The least squares support vector machine (lSSVM) was optimized by IWPA, and an IWPA-LSSVM based photovoltaic power prediction model was established considering weather types and similar days. The photovoltaic power generation data under different weather types were used for simulation, and the simulation results show that the proposed method has a higher prediction accuracy and the error fluctuation of regression fitting is smaller whether the weather is sunny, cloudy or rainy.