Short time solar power forecasting using P-ELM approach
Abstract Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82155-7 |
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| author | Shuqi Shi Boyang Liu Long Ren Yu Liu |
| author_facet | Shuqi Shi Boyang Liu Long Ren Yu Liu |
| author_sort | Shuqi Shi |
| collection | DOAJ |
| description | Abstract Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting. The performance of the P-ELM algorithm is evaluated using mean absolute error (MAE) and root mean square error (RMSE), and it is compared with the extreme learning machine (ELM) algorithm. The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting. |
| format | Article |
| id | doaj-art-e2fe91585aee4a9e81c4ddd9cc1cf94a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e2fe91585aee4a9e81c4ddd9cc1cf94a2024-12-29T12:30:20ZengNature PortfolioScientific Reports2045-23222024-12-011411910.1038/s41598-024-82155-7Short time solar power forecasting using P-ELM approachShuqi Shi0Boyang Liu1Long Ren2Yu Liu3Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang UniversityHunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang UniversityHunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang UniversitySchool of Electrical and Information, Hunan UniversityAbstract Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting. The performance of the P-ELM algorithm is evaluated using mean absolute error (MAE) and root mean square error (RMSE), and it is compared with the extreme learning machine (ELM) algorithm. The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting.https://doi.org/10.1038/s41598-024-82155-7Extreme learning machine (ELM)Pre-trained extreme learning machine (P-ELM)Short-term forecastingSolar power forecasting |
| spellingShingle | Shuqi Shi Boyang Liu Long Ren Yu Liu Short time solar power forecasting using P-ELM approach Scientific Reports Extreme learning machine (ELM) Pre-trained extreme learning machine (P-ELM) Short-term forecasting Solar power forecasting |
| title | Short time solar power forecasting using P-ELM approach |
| title_full | Short time solar power forecasting using P-ELM approach |
| title_fullStr | Short time solar power forecasting using P-ELM approach |
| title_full_unstemmed | Short time solar power forecasting using P-ELM approach |
| title_short | Short time solar power forecasting using P-ELM approach |
| title_sort | short time solar power forecasting using p elm approach |
| topic | Extreme learning machine (ELM) Pre-trained extreme learning machine (P-ELM) Short-term forecasting Solar power forecasting |
| url | https://doi.org/10.1038/s41598-024-82155-7 |
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