Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Ajith Gopi, Prabhakar Sharma, Kumarasamy Sudhakar (), Wai Keng Ngui, Irina Kirpichnikova and Erdem Cuce
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Ajith Gopi: Energy Sustainability Research Group, Automotive Engineering Center, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
Prabhakar Sharma: School of Engineering Sciences, Delhi Skill and Entrepreneurship University, Delhi 110089, India
Kumarasamy Sudhakar: Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
Wai Keng Ngui: Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
Irina Kirpichnikova: Department of Electric Power Stations, Network and Supply Systems, South Ural State University (National Research University), 76 Prospekt Lenina, 454080 Chelyabinsk, Russia
Erdem Cuce: Department of Mechanical Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Zihni Derin Campus, 53100 Rize, Turkey

Sustainability, 2022, vol. 15, issue 1, 1-28

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 (R 2 ), 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 R 2 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.

Keywords: artificial intelligence; forecasting; solar irradiance; energy generation; solar plant; neuro-fuzzy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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