Weather impact on solar farm Performance : A comparative analysis of machine learning techniques

Gopi, Ajith and Sharma, Prabhakar and Sudhakar, Kumarasamy and Ngui, Wai Keng and Kirpichnikova, Irina and Cuce, Erdem (2023) Weather impact on solar farm Performance : A comparative analysis of machine learning techniques. Sustainability (Switzerland), 15 (439). pp. 1-28. ISSN 2071-1050. (Published)

Weather impact on solar farm performance_A comparative analysis of machine learning techniques.pdf
Available under License Creative Commons Attribution.

Download (6MB) | Preview


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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Artificial intelligence; Energy generation; Forecasting; Neuro-fuzzy; Solar irradiance; Solar plant
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: Faculty of Mechanical and Automotive Engineering Technology
Centre of Excellence: Centre of Excellence for Advanced Research in Fluid Flow
Institute of Postgraduate Studies
Centre for Research in Advanced Fluid & Processes (Fluid Centre)
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 23 Aug 2023 01:32
Last Modified: 23 Aug 2023 01:32
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item