Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load
Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bingjie Jin [verfasserIn] Guihua Zeng [verfasserIn] Zhilin Lu [verfasserIn] Hongqiao Peng [verfasserIn] Shuxin Luo [verfasserIn] Xinhe Yang [verfasserIn] Haojun Zhu [verfasserIn] Mingbo Liu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Energies - MDPI AG, 2008, 15(2022), 20, p 7584 |
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| Übergeordnetes Werk: |
volume:15 ; year:2022 ; number:20, p 7584 |
| Links: |
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| DOI / URN: |
10.3390/en15207584 |
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| Katalog-ID: |
DOAJ086793837 |
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| 520 | |a Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. | ||
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10.3390/en15207584 doi (DE-627)DOAJ086793837 (DE-599)DOAJffb24d40ec5a42a6b3d8e9cb2a03379b DE-627 ger DE-627 rakwb eng Bingjie Jin verfasserin aut Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) Technology T Guihua Zeng verfasserin aut Zhilin Lu verfasserin aut Hongqiao Peng verfasserin aut Shuxin Luo verfasserin aut Xinhe Yang verfasserin aut Haojun Zhu verfasserin aut Mingbo Liu verfasserin aut In Energies MDPI AG, 2008 15(2022), 20, p 7584 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:20, p 7584 https://doi.org/10.3390/en15207584 kostenfrei https://doaj.org/article/ffb24d40ec5a42a6b3d8e9cb2a03379b kostenfrei https://www.mdpi.com/1996-1073/15/20/7584 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 20, p 7584 |
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10.3390/en15207584 doi (DE-627)DOAJ086793837 (DE-599)DOAJffb24d40ec5a42a6b3d8e9cb2a03379b DE-627 ger DE-627 rakwb eng Bingjie Jin verfasserin aut Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) Technology T Guihua Zeng verfasserin aut Zhilin Lu verfasserin aut Hongqiao Peng verfasserin aut Shuxin Luo verfasserin aut Xinhe Yang verfasserin aut Haojun Zhu verfasserin aut Mingbo Liu verfasserin aut In Energies MDPI AG, 2008 15(2022), 20, p 7584 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:20, p 7584 https://doi.org/10.3390/en15207584 kostenfrei https://doaj.org/article/ffb24d40ec5a42a6b3d8e9cb2a03379b kostenfrei https://www.mdpi.com/1996-1073/15/20/7584 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 20, p 7584 |
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10.3390/en15207584 doi (DE-627)DOAJ086793837 (DE-599)DOAJffb24d40ec5a42a6b3d8e9cb2a03379b DE-627 ger DE-627 rakwb eng Bingjie Jin verfasserin aut Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) Technology T Guihua Zeng verfasserin aut Zhilin Lu verfasserin aut Hongqiao Peng verfasserin aut Shuxin Luo verfasserin aut Xinhe Yang verfasserin aut Haojun Zhu verfasserin aut Mingbo Liu verfasserin aut In Energies MDPI AG, 2008 15(2022), 20, p 7584 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:20, p 7584 https://doi.org/10.3390/en15207584 kostenfrei https://doaj.org/article/ffb24d40ec5a42a6b3d8e9cb2a03379b kostenfrei https://www.mdpi.com/1996-1073/15/20/7584 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 20, p 7584 |
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10.3390/en15207584 doi (DE-627)DOAJ086793837 (DE-599)DOAJffb24d40ec5a42a6b3d8e9cb2a03379b DE-627 ger DE-627 rakwb eng Bingjie Jin verfasserin aut Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) Technology T Guihua Zeng verfasserin aut Zhilin Lu verfasserin aut Hongqiao Peng verfasserin aut Shuxin Luo verfasserin aut Xinhe Yang verfasserin aut Haojun Zhu verfasserin aut Mingbo Liu verfasserin aut In Energies MDPI AG, 2008 15(2022), 20, p 7584 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:20, p 7584 https://doi.org/10.3390/en15207584 kostenfrei https://doaj.org/article/ffb24d40ec5a42a6b3d8e9cb2a03379b kostenfrei https://www.mdpi.com/1996-1073/15/20/7584 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 20, p 7584 |
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10.3390/en15207584 doi (DE-627)DOAJ086793837 (DE-599)DOAJffb24d40ec5a42a6b3d8e9cb2a03379b DE-627 ger DE-627 rakwb eng Bingjie Jin verfasserin aut Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) Technology T Guihua Zeng verfasserin aut Zhilin Lu verfasserin aut Hongqiao Peng verfasserin aut Shuxin Luo verfasserin aut Xinhe Yang verfasserin aut Haojun Zhu verfasserin aut Mingbo Liu verfasserin aut In Energies MDPI AG, 2008 15(2022), 20, p 7584 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:20, p 7584 https://doi.org/10.3390/en15207584 kostenfrei https://doaj.org/article/ffb24d40ec5a42a6b3d8e9cb2a03379b kostenfrei https://www.mdpi.com/1996-1073/15/20/7584 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 20, p 7584 |
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Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load medium- and long-term peak load forecasting multi-source information long short-term memory (LSTM) back propagation neural network (BPNN) |
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Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load |
| abstract |
Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. |
| abstractGer |
Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. |
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Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset. |
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