Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm
In recent years, Transformer-based methods have shown full potential in power load forecasting problems. However, their computational cost is high, while it is difficult to capture the global characteristics of the time series. When the forecasting time length is long, the overall shift of the forec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/15/4057 |
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| _version_ | 1849770901835350016 |
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| author | Xin Jin Tingzhe Pan Heyang Yu Zongyi Wang Wangzhang Cao |
| author_facet | Xin Jin Tingzhe Pan Heyang Yu Zongyi Wang Wangzhang Cao |
| author_sort | Xin Jin |
| collection | DOAJ |
| description | In recent years, Transformer-based methods have shown full potential in power load forecasting problems. However, their computational cost is high, while it is difficult to capture the global characteristics of the time series. When the forecasting time length is long, the overall shift of the forecasting trend often occurs. Therefore, this paper proposes a gray relation analysis–frequency-enhanced decomposition transformer (GRA-FEDformer) method for forecasting power loads in power systems. Firstly, considering the impact of different weather factors on power loads, the correlation between various factors and power loads was analyzed using the GRA method to screen out the high-correlation factors as model inputs. Secondly, a frequency decomposition method for long short-time-scale components was utilized. Its combination with the transformer-based model can give the deep learning model an ability to simultaneously capture the fluctuating behavior of the short time scale and the overall trend of changes in the long time scale in power loads. The experimental results show that the proposed method had better forecasting performance than the other methods for a one-year dataset in a region of Morocco. In particular, the advantages of the proposed method were more obvious in the forecasting task with a longer forecasting length. |
| format | Article |
| id | doaj-art-91fb308664eb4743bb935afa3c9972d8 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-91fb308664eb4743bb935afa3c9972d82025-08-20T03:02:48ZengMDPI AGEnergies1996-10732025-07-011815405710.3390/en18154057Electricity Load Forecasting Method Based on the GRA-FEDformer AlgorithmXin Jin0Tingzhe Pan1Heyang Yu2Zongyi Wang3Wangzhang Cao4Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, ChinaElectric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, ChinaElectric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, ChinaElectric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, ChinaElectric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510000, ChinaIn recent years, Transformer-based methods have shown full potential in power load forecasting problems. However, their computational cost is high, while it is difficult to capture the global characteristics of the time series. When the forecasting time length is long, the overall shift of the forecasting trend often occurs. Therefore, this paper proposes a gray relation analysis–frequency-enhanced decomposition transformer (GRA-FEDformer) method for forecasting power loads in power systems. Firstly, considering the impact of different weather factors on power loads, the correlation between various factors and power loads was analyzed using the GRA method to screen out the high-correlation factors as model inputs. Secondly, a frequency decomposition method for long short-time-scale components was utilized. Its combination with the transformer-based model can give the deep learning model an ability to simultaneously capture the fluctuating behavior of the short time scale and the overall trend of changes in the long time scale in power loads. The experimental results show that the proposed method had better forecasting performance than the other methods for a one-year dataset in a region of Morocco. In particular, the advantages of the proposed method were more obvious in the forecasting task with a longer forecasting length.https://www.mdpi.com/1996-1073/18/15/4057power load forecastingtransformergrey relation analysisdeep learning |
| spellingShingle | Xin Jin Tingzhe Pan Heyang Yu Zongyi Wang Wangzhang Cao Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm Energies power load forecasting transformer grey relation analysis deep learning |
| title | Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm |
| title_full | Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm |
| title_fullStr | Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm |
| title_full_unstemmed | Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm |
| title_short | Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm |
| title_sort | electricity load forecasting method based on the gra fedformer algorithm |
| topic | power load forecasting transformer grey relation analysis deep learning |
| url | https://www.mdpi.com/1996-1073/18/15/4057 |
| work_keys_str_mv | AT xinjin electricityloadforecastingmethodbasedonthegrafedformeralgorithm AT tingzhepan electricityloadforecastingmethodbasedonthegrafedformeralgorithm AT heyangyu electricityloadforecastingmethodbasedonthegrafedformeralgorithm AT zongyiwang electricityloadforecastingmethodbasedonthegrafedformeralgorithm AT wangzhangcao electricityloadforecastingmethodbasedonthegrafedformeralgorithm |