A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/1100 |
| _version_ | 1797407506656395264 |
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| author | Quoc Thang Phan Yuan Kang Wu Quoc Dung Phan |
| author_facet | Quoc Thang Phan Yuan Kang Wu Quoc Dung Phan |
| author_sort | Quoc Thang Phan |
| collection | DOAJ |
| description | In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts. |
| first_indexed | 2024-03-09T03:42:29Z |
| format | Article |
| id | doaj.art-6b466a0edd8740ab9876c7e54b09d801 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:42:29Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6b466a0edd8740ab9876c7e54b09d8012023-12-03T14:38:09ZengMDPI AGApplied Sciences2076-34172021-01-01113110010.3390/app11031100A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPsQuoc Thang Phan0Yuan Kang Wu1Quoc Dung Phan2Department of Electrical Engineering, College of Engineering, National Chung Cheng University, Chiayi 62102, TaiwanDepartment of Electrical Engineering, College of Engineering, National Chung Cheng University, Chiayi 62102, TaiwanFaculty of Electronics and Electrical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 70000, VietnamIn recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts.https://www.mdpi.com/2076-3417/11/3/1100wind power forecastingdeterministic forecastingprobabilistic forecastingnumerical weather prediction (NWP)data preprocessingXGBoost |
| spellingShingle | Quoc Thang Phan Yuan Kang Wu Quoc Dung Phan A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs Applied Sciences wind power forecasting deterministic forecasting probabilistic forecasting numerical weather prediction (NWP) data preprocessing XGBoost |
| title | A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs |
| title_full | A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs |
| title_fullStr | A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs |
| title_full_unstemmed | A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs |
| title_short | A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs |
| title_sort | hybrid wind power forecasting model with xgboost data preprocessing considering different nwps |
| topic | wind power forecasting deterministic forecasting probabilistic forecasting numerical weather prediction (NWP) data preprocessing XGBoost |
| url | https://www.mdpi.com/2076-3417/11/3/1100 |
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