Abstract
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.
Funder
Universiti Malaysia Pahang
Russian Science Foundation
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference58 articles.
1. Khan, N., Sudhakar, K., and Mamat, R. (2021). Role of Biofuels in Energy Transition, Green Economy and Carbon Neutrality. Sustainability, 13.
2. Modeling and performance simulation of 100 MW LFR based solar thermal power plant in Udaipur India;Bishoyi;Resour. Technol.,2017
3. Solomin, E., Sirotkin, E., Cuce, E., Selvanathan, S., and Kumarasamy, S. (2021). Hybrid Floating Solar Plant Designs: A Review. Energies, 14.
4. Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran;Gholami;Atmos. Pollut. Res.,2020
5. Performance Modeling of the Weather Impact on a Utility-Scale PV Power Plant in a Tropical Region;Gopi;Int. J. Photoenergy,2021
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