Radzi, Putri Nor Liyana Mohamad and Akhter, Muhammad Naveed and Mekhilef, Saad and Shah, Noraisyah Mohamed (2023) Review on the Application of photovoltaic forecasting using machine learning for very short- to long-term forecasting. Sustainability, 15 (4). ISSN 2071-1050, DOI https://doi.org/10.3390/su15042942.
Full text not available from this repository.Abstract
Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable's technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Machine learning; Forecasting; Renewable energy; Photovoltaic; Artificial neural network; Recurrent neural network; convolutional neural network |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 04 Nov 2024 04:05 |
| Last Modified: | 04 Nov 2024 04:05 |
| URI: | http://eprints.um.edu.my/id/eprint/38731 |
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