Short-Term forecasting of floating photovoltaic power generation using machine learning models
Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42923/ http://umpir.ump.edu.my/id/eprint/42923/1/Short-Term%20forecasting%20of%20floating%20photovoltaic%20power%20generation%20using%20machine%20learning%20models.pdf |
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| author | Mohd Herwan, Sulaiman Mohd Shawal, Jadin Zuriani, Mustaffa Mohd Nurulakla, Mohd Azlan Hamdan, Daniyal |
| author_facet | Mohd Herwan, Sulaiman Mohd Shawal, Jadin Zuriani, Mustaffa Mohd Nurulakla, Mohd Azlan Hamdan, Daniyal |
| author_sort | Mohd Herwan, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations. |
| first_indexed | 2025-11-15T03:49:33Z |
| format | Article |
| id | ump-42923 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:49:33Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-429232024-11-13T07:08:31Z http://umpir.ump.edu.my/id/eprint/42923/ Short-Term forecasting of floating photovoltaic power generation using machine learning models Mohd Herwan, Sulaiman Mohd Shawal, Jadin Zuriani, Mustaffa Mohd Nurulakla, Mohd Azlan Hamdan, Daniyal TK Electrical engineering. Electronics Nuclear engineering Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations. Elsevier B.V. 2024-12 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42923/1/Short-Term%20forecasting%20of%20floating%20photovoltaic%20power%20generation%20using%20machine%20learning%20models.pdf Mohd Herwan, Sulaiman and Mohd Shawal, Jadin and Zuriani, Mustaffa and Mohd Nurulakla, Mohd Azlan and Hamdan, Daniyal (2024) Short-Term forecasting of floating photovoltaic power generation using machine learning models. Cleaner Energy Systems, 9 (100137). pp. 1-15. ISSN 2772-7831. (Published) https://doi.org/10.1016/j.cles.2024.100137 https://doi.org/10.1016/j.cles.2024.100137 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Herwan, Sulaiman Mohd Shawal, Jadin Zuriani, Mustaffa Mohd Nurulakla, Mohd Azlan Hamdan, Daniyal Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title | Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title_full | Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title_fullStr | Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title_full_unstemmed | Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title_short | Short-Term forecasting of floating photovoltaic power generation using machine learning models |
| title_sort | short-term forecasting of floating photovoltaic power generation using machine learning models |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42923/ http://umpir.ump.edu.my/id/eprint/42923/ http://umpir.ump.edu.my/id/eprint/42923/ http://umpir.ump.edu.my/id/eprint/42923/1/Short-Term%20forecasting%20of%20floating%20photovoltaic%20power%20generation%20using%20machine%20learning%20models.pdf |