A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems

Abstract

Accurate model plays an important role in designing, assessing, and controlling photovoltaic (PV) systems. In this work, the least-squares support vector machine (LSSVM) is adopted to model the current–voltage (V–I) characteristic curves of different PV systems. A novel RNA genetic algorithm (bvRNA-GA) is proposed to determine the parameters of LSSVM. The bvRNA-GA is featured by designing the bulge loop crossover operator and the virus-induced mutation operator, they are employed to balance the exploration and exploitation capacities. Different experiments with 10 benchmark functions are conducted to show that the search efficiency of bvRNA-GA is better than the other four state-of-art algorithms. The outputs of bvRNA-GA optimized LSSVM models can better agree with the real outputs of different PV systems, the modeling results demonstrate the effectiveness of bvRNA-GA in solving real-world problems.