Weather impact on solar farm Performance : A comparative analysis of machine learning techniques
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 (...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38196/ http://umpir.ump.edu.my/id/eprint/38196/1/Weather%20impact%20on%20solar%20farm%20performance_A%20comparative%20analysis%20of%20machine%20learning%20techniques.pdf |
| _version_ | 1848825446656901120 |
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| author | Gopi, Ajith Sharma, Prabhakar Sudhakar, Kumarasamy Ngui, Wai Keng Kirpichnikova, Irina Cuce, Erdem |
| author_facet | Gopi, Ajith Sharma, Prabhakar Sudhakar, Kumarasamy Ngui, Wai Keng Kirpichnikova, Irina Cuce, Erdem |
| author_sort | Gopi, Ajith |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:29:03Z |
| format | Article |
| id | ump-38196 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:29:03Z |
| publishDate | 2023 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-381962023-08-23T01:32:59Z http://umpir.ump.edu.my/id/eprint/38196/ Weather impact on solar farm Performance : A comparative analysis of machine learning techniques Gopi, Ajith Sharma, Prabhakar Sudhakar, Kumarasamy Ngui, Wai Keng Kirpichnikova, Irina Cuce, Erdem T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics 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. MDPI 2023-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38196/1/Weather%20impact%20on%20solar%20farm%20performance_A%20comparative%20analysis%20of%20machine%20learning%20techniques.pdf 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) https://doi.org/10.3390/su15010439 https://doi.org/10.3390/su15010439 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Gopi, Ajith Sharma, Prabhakar Sudhakar, Kumarasamy Ngui, Wai Keng Kirpichnikova, Irina Cuce, Erdem Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title | Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title_full | Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title_fullStr | Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title_full_unstemmed | Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title_short | Weather impact on solar farm Performance : A comparative analysis of machine learning techniques |
| title_sort | weather impact on solar farm performance : a comparative analysis of machine learning techniques |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/38196/ http://umpir.ump.edu.my/id/eprint/38196/ http://umpir.ump.edu.my/id/eprint/38196/ http://umpir.ump.edu.my/id/eprint/38196/1/Weather%20impact%20on%20solar%20farm%20performance_A%20comparative%20analysis%20of%20machine%20learning%20techniques.pdf |