COMPARISON OF VARIOUS ALGORITHMS FOR PV PERFORMANCE

This research focuses on the accurate estimation of photovoltaic (PV) system parameters through the integration of computational intelligence and modern optimization approaches. The proposed framework involves acquiring relevant PV performance data, formulating a suitable mathematical model, and implementing population-based optimization methods to minimize the error between experimentally observed and computationally predicted outputs. Several optimization techniques, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Pufferfish Optimization Algorithm (POA), are investigated to evaluate their effectiveness in solving nonlinear parameter estimation problems. From a computer science perspective, the study concentrates on algorithm design, intelligent optimization mechanisms, data analysis, numerical computation, and performance benchmarking. Particular attention is given to the potential of POA to identify optimal parameter values in complex search spaces characterized by nonlinear relationships and multiple interacting variables. The investigation also explores the relevance of these optimization strategies to artificial intelligence, predictive analytics, computational modeling, and sustainable information technologies. The findings aim to contribute to the development of reliable PV simulation models and data-driven decision-support systems that can assist in renewable energy forecasting, system performance evaluation, and efficient energy resource management.