A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.