Spatial load forecasting method based on double-layer XGBoost and data enhancement
Spatial load forecasting faces the problems of multiple characteristic factors and data shortage. A spatial load forecasting method based on double-layer extreme gradient boosting (XGBoost) and data enhancement is proposed. Firstly, the area to be predicted is divided into several sub regions accord...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2023-01-01
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| Series: | 电力工程技术 |
| Subjects: | |
| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/220114068 |
| _version_ | 1828048062283513856 |
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| author | HUANG Dongmei ZHANG Ningning HU Anduo HU Wei XIAO Yong CHEN Anqing |
| author_facet | HUANG Dongmei ZHANG Ningning HU Anduo HU Wei XIAO Yong CHEN Anqing |
| author_sort | HUANG Dongmei |
| collection | DOAJ |
| description | Spatial load forecasting faces the problems of multiple characteristic factors and data shortage. A spatial load forecasting method based on double-layer extreme gradient boosting (XGBoost) and data enhancement is proposed. Firstly, the area to be predicted is divided into several sub regions according to the supply range of feeder power. Secondly, a feature selection model based on double-layer XGBoost is constructed. The first layer XGBoost scores and sorts the features. The combined features are loaded into the second layer XGBoost for sub regional load forecasting. The best feature variables of each sub region are selected according to the load forecasting results. Then, the training set samples of each sub region are enhanced by the generative adversarial network (GAN), and the load of sub regions is forecasted through the extreme learning machine (ELM). Finally, the predicted values of sub regions are added to obtain the load of the region to be predicted. Taking local areas of Shanghai as an example, the simulated experiment and comparative analysis are carried out. The results show that the proposed method can solve the problems of characteristic variable selection and data shortage at the same time, and has high prediction accuracy. |
| first_indexed | 2024-04-10T18:51:17Z |
| format | Article |
| id | doaj.art-43627b03be7a4a41a2e52ae71f4b098b |
| institution | Directory Open Access Journal |
| issn | 2096-3203 |
| language | zho |
| last_indexed | 2024-04-10T18:51:17Z |
| publishDate | 2023-01-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj.art-43627b03be7a4a41a2e52ae71f4b098b2023-02-01T07:22:44ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032023-01-0142120120810.12158/j.2096-3203.2023.01.024220114068Spatial load forecasting method based on double-layer XGBoost and data enhancementHUANG Dongmei0ZHANG Ningning1HU Anduo2HU Wei3XIAO Yong4CHEN Anqing5College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Economics and Management, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Continuing Education, Shanghai University of Electric Power, Shanghai 200090, ChinaState Grid Info-Telecom Great Power Science and Technology Co., Ltd., Fuzhou 350001, ChinaSpatial load forecasting faces the problems of multiple characteristic factors and data shortage. A spatial load forecasting method based on double-layer extreme gradient boosting (XGBoost) and data enhancement is proposed. Firstly, the area to be predicted is divided into several sub regions according to the supply range of feeder power. Secondly, a feature selection model based on double-layer XGBoost is constructed. The first layer XGBoost scores and sorts the features. The combined features are loaded into the second layer XGBoost for sub regional load forecasting. The best feature variables of each sub region are selected according to the load forecasting results. Then, the training set samples of each sub region are enhanced by the generative adversarial network (GAN), and the load of sub regions is forecasted through the extreme learning machine (ELM). Finally, the predicted values of sub regions are added to obtain the load of the region to be predicted. Taking local areas of Shanghai as an example, the simulated experiment and comparative analysis are carried out. The results show that the proposed method can solve the problems of characteristic variable selection and data shortage at the same time, and has high prediction accuracy.https://www.epet-info.com/dlgcjsen/article/abstract/220114068spatial load forecastingextreme gradient boosting (xgboost)feature selectiongenerative adversarial network (gan)data enhancementextreme learning machine (elm) |
| spellingShingle | HUANG Dongmei ZHANG Ningning HU Anduo HU Wei XIAO Yong CHEN Anqing Spatial load forecasting method based on double-layer XGBoost and data enhancement 电力工程技术 spatial load forecasting extreme gradient boosting (xgboost) feature selection generative adversarial network (gan) data enhancement extreme learning machine (elm) |
| title | Spatial load forecasting method based on double-layer XGBoost and data enhancement |
| title_full | Spatial load forecasting method based on double-layer XGBoost and data enhancement |
| title_fullStr | Spatial load forecasting method based on double-layer XGBoost and data enhancement |
| title_full_unstemmed | Spatial load forecasting method based on double-layer XGBoost and data enhancement |
| title_short | Spatial load forecasting method based on double-layer XGBoost and data enhancement |
| title_sort | spatial load forecasting method based on double layer xgboost and data enhancement |
| topic | spatial load forecasting extreme gradient boosting (xgboost) feature selection generative adversarial network (gan) data enhancement extreme learning machine (elm) |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/220114068 |
| work_keys_str_mv | AT huangdongmei spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement AT zhangningning spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement AT huanduo spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement AT huwei spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement AT xiaoyong spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement AT chenanqing spatialloadforecastingmethodbasedondoublelayerxgboostanddataenhancement |