Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for establishing the economic sustainability of a newly installed system. The present study aims to develop a prediction model to forecast an installed PV system’s annual power generation yield and performance ratio (PR) using three environmental input parameters: solar irradiance, wind speed, and ambient air temperature. Three data-based artificial intelligence (AI) techniques, namely, adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM), and artificial neural network (ANN), were employed. The models were developed using three years of data from an operational 2MWp Solar PV Project at Kuzhalmannam, Kerala state, India. Statistical indices such as Pearson’s R, coefficient of determination (R2), root-mean-squared error (RMSE), Nash-Sutcliffe efficiency (NSCE), mean absolute-percentage error (MAPE), Kling-Gupta efficiency (KGE), Taylor’s diagram, and correlation matrix were used to determine the most accurate prediction model. The results demonstrate that ANFIS was the most precise performance ratio prediction model, with an R2 value of 0.9830 and an RMSE of 0.6. It is envisaged that the forecast model would be a valuable tool for policymakers, solar energy researchers, and solar farm developers.

Solar photovoltaics and the associated applications are now considered the most promising technologies for a sustainable future. The performance of the PV power plants is not studied in detail with respect to the influence of various weather parameters like rain, relative humidity, and atmospheric pressure on energy generation. The objective of this research work is to analyze and model the weather impact of a utility-scale PV power plant in a tropical region. The methodology involves the detailed analysis of the PV plant performance for various weather seasons and modeling the energy generation based on important weather parameters obtained from a Solar Radiation Resource Assessment (SRRA) station installed at the PV power plant location itself. Solar generation and its performance are affected during the rainy seasons, and it turns out to be a typical phenomenon in the humid tropical region. A regression model of solar generation for all the weather seasons is generated based on different weather parameters.

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