Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting
Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buil...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Peng, Chao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:202 ; year:2022 ; day:15 ; month:09 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.eswa.2022.117194 |
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ELV057751226 |
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| 520 | |a Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. | ||
| 520 | |a Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. | ||
| 650 | 7 | |a Building load forecasting |2 Elsevier | |
| 650 | 7 | |a LSTM |2 Elsevier | |
| 650 | 7 | |a Transfer learning |2 Elsevier | |
| 650 | 7 | |a Multi-source |2 Elsevier | |
| 700 | 1 | |a Tao, Yifan |4 oth | |
| 700 | 1 | |a Chen, Zhipeng |4 oth | |
| 700 | 1 | |a Zhang, Yong |4 oth | |
| 700 | 1 | |a Sun, Xiaoyan |4 oth | |
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10.1016/j.eswa.2022.117194 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001985.pica (DE-627)ELV057751226 (ELSEVIER)S0957-4174(22)00581-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Peng, Chao verfasserin aut Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Building load forecasting Elsevier LSTM Elsevier Transfer learning Elsevier Multi-source Elsevier Tao, Yifan oth Chen, Zhipeng oth Zhang, Yong oth Sun, Xiaoyan oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:202 year:2022 day:15 month:09 pages:0 https://doi.org/10.1016/j.eswa.2022.117194 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 202 2022 15 0915 0 |
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10.1016/j.eswa.2022.117194 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001985.pica (DE-627)ELV057751226 (ELSEVIER)S0957-4174(22)00581-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Peng, Chao verfasserin aut Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Building load forecasting Elsevier LSTM Elsevier Transfer learning Elsevier Multi-source Elsevier Tao, Yifan oth Chen, Zhipeng oth Zhang, Yong oth Sun, Xiaoyan oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:202 year:2022 day:15 month:09 pages:0 https://doi.org/10.1016/j.eswa.2022.117194 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 202 2022 15 0915 0 |
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10.1016/j.eswa.2022.117194 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001985.pica (DE-627)ELV057751226 (ELSEVIER)S0957-4174(22)00581-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Peng, Chao verfasserin aut Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Building load forecasting Elsevier LSTM Elsevier Transfer learning Elsevier Multi-source Elsevier Tao, Yifan oth Chen, Zhipeng oth Zhang, Yong oth Sun, Xiaoyan oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:202 year:2022 day:15 month:09 pages:0 https://doi.org/10.1016/j.eswa.2022.117194 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 202 2022 15 0915 0 |
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10.1016/j.eswa.2022.117194 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001985.pica (DE-627)ELV057751226 (ELSEVIER)S0957-4174(22)00581-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Peng, Chao verfasserin aut Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Building load forecasting Elsevier LSTM Elsevier Transfer learning Elsevier Multi-source Elsevier Tao, Yifan oth Chen, Zhipeng oth Zhang, Yong oth Sun, Xiaoyan oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:202 year:2022 day:15 month:09 pages:0 https://doi.org/10.1016/j.eswa.2022.117194 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 202 2022 15 0915 0 |
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10.1016/j.eswa.2022.117194 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001985.pica (DE-627)ELV057751226 (ELSEVIER)S0957-4174(22)00581-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Peng, Chao verfasserin aut Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. Building load forecasting Elsevier LSTM Elsevier Transfer learning Elsevier Multi-source Elsevier Tao, Yifan oth Chen, Zhipeng oth Zhang, Yong oth Sun, Xiaoyan oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:202 year:2022 day:15 month:09 pages:0 https://doi.org/10.1016/j.eswa.2022.117194 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 202 2022 15 0915 0 |
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Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting |
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Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. |
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Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. |
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Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few. |
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