A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China
Accurate forecasting of annual national electricity consumption data is crucial for energy and economic development planning. However, the small amount of data and the complex influencing factors pose great challenges. Here we propose a novel transfer learning combination model for the 5-year foreca...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11015997/ |
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| author | Ling Liu Jujie Wang |
| author_facet | Ling Liu Jujie Wang |
| author_sort | Ling Liu |
| collection | DOAJ |
| description | Accurate forecasting of annual national electricity consumption data is crucial for energy and economic development planning. However, the small amount of data and the complex influencing factors pose great challenges. Here we propose a novel transfer learning combination model for the 5-year forecasting of annual national electricity consumption data in China. To improve the forecasting accuracy, we adopted a data transfer learning approach to extend the training set of China by migrating relevant data from 17 developed countries. To increase the data information of training sets, a new data preprocessing procedure containing trend calculation and data extension was designed. To fully utilize the advantages of different models, a multi-model integration framework with a neural network weighting unit is designed. The comparison results show that the proposed model has the lowest errors, with the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 0.0105, 0.0002, and 0.3047, respectively. |
| format | Article |
| id | doaj-art-e4752cef86224ca3a73dfd6542d6320f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e4752cef86224ca3a73dfd6542d6320f2025-08-20T03:19:32ZengIEEEIEEE Access2169-35362025-01-0113928829289010.1109/ACCESS.2025.357413711015997A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in ChinaLing Liu0https://orcid.org/0000-0001-9190-1629Jujie Wang1https://orcid.org/0000-0003-0574-5661School of Economics and Management, Zhejiang University of Water Resources and Electric Power, Hangzhou, ChinaSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaAccurate forecasting of annual national electricity consumption data is crucial for energy and economic development planning. However, the small amount of data and the complex influencing factors pose great challenges. Here we propose a novel transfer learning combination model for the 5-year forecasting of annual national electricity consumption data in China. To improve the forecasting accuracy, we adopted a data transfer learning approach to extend the training set of China by migrating relevant data from 17 developed countries. To increase the data information of training sets, a new data preprocessing procedure containing trend calculation and data extension was designed. To fully utilize the advantages of different models, a multi-model integration framework with a neural network weighting unit is designed. The comparison results show that the proposed model has the lowest errors, with the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 0.0105, 0.0002, and 0.3047, respectively.https://ieeexplore.ieee.org/document/11015997/Data extensiontransfer learningtrend attachment |
| spellingShingle | Ling Liu Jujie Wang A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China IEEE Access Data extension transfer learning trend attachment |
| title | A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China |
| title_full | A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China |
| title_fullStr | A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China |
| title_full_unstemmed | A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China |
| title_short | A Transfer Learning Combination Model for Annual National Electricity Consumption Forecasting in China |
| title_sort | transfer learning combination model for annual national electricity consumption forecasting in china |
| topic | Data extension transfer learning trend attachment |
| url | https://ieeexplore.ieee.org/document/11015997/ |
| work_keys_str_mv | AT lingliu atransferlearningcombinationmodelforannualnationalelectricityconsumptionforecastinginchina AT jujiewang atransferlearningcombinationmodelforannualnationalelectricityconsumptionforecastinginchina AT lingliu transferlearningcombinationmodelforannualnationalelectricityconsumptionforecastinginchina AT jujiewang transferlearningcombinationmodelforannualnationalelectricityconsumptionforecastinginchina |