A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a la...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/7/3081 |
| _version_ | 1797608052956856320 |
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| author | Zizhen Cheng Li Wang Yumeng Yang |
| author_facet | Zizhen Cheng Li Wang Yumeng Yang |
| author_sort | Zizhen Cheng |
| collection | DOAJ |
| description | Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model’s ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City. |
| first_indexed | 2024-03-11T05:39:10Z |
| format | Article |
| id | doaj.art-c1c62814d050495f8d47720d8adb1558 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T05:39:10Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c1c62814d050495f8d47720d8adb15582023-11-17T16:37:07ZengMDPI AGEnergies1996-10732023-03-01167308110.3390/en16073081A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load ForecastingZizhen Cheng0Li Wang1Yumeng Yang2School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaAccurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model’s ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City.https://www.mdpi.com/1996-1073/16/7/3081causal dilated convolutionfeature pyramid CNN-LSTM hybrid neural networkmedium- and long-term load forecasting |
| spellingShingle | Zizhen Cheng Li Wang Yumeng Yang A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting Energies causal dilated convolution feature pyramid CNN-LSTM hybrid neural network medium- and long-term load forecasting |
| title | A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting |
| title_full | A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting |
| title_fullStr | A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting |
| title_full_unstemmed | A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting |
| title_short | A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting |
| title_sort | hybrid feature pyramid cnn lstm model with seasonal inflection month correction for medium and long term power load forecasting |
| topic | causal dilated convolution feature pyramid CNN-LSTM hybrid neural network medium- and long-term load forecasting |
| url | https://www.mdpi.com/1996-1073/16/7/3081 |
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