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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Floating Photovoltaic (FPV); Machine learning; Short-term forecasting |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Computing Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 13 Nov 2024 07:08 |
| Last Modified: | 13 Nov 2024 07:08 |
| URI: | http://umpir.ump.edu.my/id/eprint/42923 |
| Download Statistic: | View Download Statistics |
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