Solar PV power forecasting using AI tech
With the development of solar technology, solar power forecasting is essential for optimizing the performance of solar energy systems. Many studies used machine learning methods to create models for predicting solar power. Among the algorithms available, XGBoost is popular and useful. In this projec...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167652 |
| _version_ | 1826127164384215040 |
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| author | Niu, Zhengyi |
| author2 | Xu Yan |
| author_facet | Xu Yan Niu, Zhengyi |
| author_sort | Niu, Zhengyi |
| collection | NTU |
| description | With the development of solar technology, solar power forecasting is essential for optimizing the performance of solar energy systems. Many studies used machine learning methods to create models for predicting solar power. Among the algorithms available, XGBoost is popular and useful. In this project, an XGBoost model was developed by Python to forecast solar irradiance. I used weather variables such as temperature and humidity as input features and trained the model using historical solar irradiance data. I evaluated the model performance using several evaluation metrics, including root mean squared error (RMSE) and coefficient of determination (R^2). Results showed that the developed model can accurately forecast hourly global horizontal irradiance for next four days, with an RMSE of 58.572 W/m2, and R^2 of 0.946. Finally, the model was used to predict the GHI of both Shijiazhuang and New York and compared with the predicted values from the Solcast website. |
| first_indexed | 2024-10-01T07:04:18Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167652 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:04:18Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1676522023-07-07T17:57:37Z Solar PV power forecasting using AI tech Niu, Zhengyi Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering With the development of solar technology, solar power forecasting is essential for optimizing the performance of solar energy systems. Many studies used machine learning methods to create models for predicting solar power. Among the algorithms available, XGBoost is popular and useful. In this project, an XGBoost model was developed by Python to forecast solar irradiance. I used weather variables such as temperature and humidity as input features and trained the model using historical solar irradiance data. I evaluated the model performance using several evaluation metrics, including root mean squared error (RMSE) and coefficient of determination (R^2). Results showed that the developed model can accurately forecast hourly global horizontal irradiance for next four days, with an RMSE of 58.572 W/m2, and R^2 of 0.946. Finally, the model was used to predict the GHI of both Shijiazhuang and New York and compared with the predicted values from the Solcast website. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-31T06:47:30Z 2023-05-31T06:47:30Z 2023 Final Year Project (FYP) Niu, Z. (2023). Solar PV power forecasting using AI tech. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167652 https://hdl.handle.net/10356/167652 en W1209-222 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Niu, Zhengyi Solar PV power forecasting using AI tech |
| title | Solar PV power forecasting using AI tech |
| title_full | Solar PV power forecasting using AI tech |
| title_fullStr | Solar PV power forecasting using AI tech |
| title_full_unstemmed | Solar PV power forecasting using AI tech |
| title_short | Solar PV power forecasting using AI tech |
| title_sort | solar pv power forecasting using ai tech |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167652 |
| work_keys_str_mv | AT niuzhengyi solarpvpowerforecastingusingaitech |