Abstract
Extreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radiation is the primary source of renewable energy (RE). With the objective of predicting PV output power with the least amount of error in mind, it is vital to analyse the impact of major environmental parameters on it. The researchers looked at a variety of environmental factors in this study, including irradiance, humidity levels, meteorological conditions, wind velocity, PV global temperature and dust deposition. Countries such as India would gain immensely from this since it will increase the quantity of PV power generated in their national networks. ANN-based prediction models and multiple regression models were used to predict PV system hourly power output. There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. It is possible to reduce bias to enhance accuracy by employing two distinct bias calculation methodologies, which were applied in this study. For example, the ANN model outperforms linear regression, M5P decision trees and GAUSSIAN process regression (GPR) models in terms of performance.