BA-PSO Based Solar Power Prediction Using Environmental Features | Original Article
Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. Here BA-PSO (Butterfly Algorithm Particle Swarm Optimization) Genetic Algorithm is used to predict the solar power requirement. A set of environmental variables are collected and their effect on solar power generation is evaluated by BA-PSO. This work considers a set of environmental features with their impact ratio solar power prediction. The BA-PSO was applied on dataset of Raipur city in Chhattisgarh state of India and the results show reduction in MAE, RMSE and improvement in power prediction when compared with ground truth values taken from Indian railway top roof solar installation.