Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings
The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train sepa...
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| Vydané v: | Energy (Oxford) Ročník 299; s. 131467 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.07.2024
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| ISSN: | 0360-5442 |
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| Abstract | The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings.
•Combining encoder and decoder architectures to build deep neural networks•Combining TDC and deep neural networks to train generalization model•Model training and testing using real office building energy consumption datasets•Setting up different comparison experiments on the source and target domains |
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| AbstractList | The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings. The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings. •Combining encoder and decoder architectures to build deep neural networks•Combining TDC and deep neural networks to train generalization model•Model training and testing using real office building energy consumption datasets•Setting up different comparison experiments on the source and target domains |
| ArticleNumber | 131467 |
| Author | Li, Yu Wang, Peng Zhang, Chengyu Jiang, Ben Rezgui, Yacine Zhao, Tianyi |
| Author_xml | – sequence: 1 givenname: Ben surname: Jiang fullname: Jiang, Ben organization: Institute of Building Energy, Dalian University of Technology, Dalian, China – sequence: 2 givenname: Yu surname: Li fullname: Li, Yu organization: College of Environment Science and Engineering, Donghua University, Shanghai, China – sequence: 3 givenname: Yacine surname: Rezgui fullname: Rezgui, Yacine organization: School of Engineering, Cardiff University, Cardiff, UK – sequence: 4 givenname: Chengyu surname: Zhang fullname: Zhang, Chengyu organization: Institute of Building Energy, Dalian University of Technology, Dalian, China – sequence: 5 givenname: Peng surname: Wang fullname: Wang, Peng organization: Institute of Building Energy, Dalian University of Technology, Dalian, China – sequence: 6 givenname: Tianyi surname: Zhao fullname: Zhao, Tianyi email: zhaotianyi@dlut.edu.cn organization: Institute of Building Energy, Dalian University of Technology, Dalian, China |
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| Keywords | Deep neural network Multi-source domain generalization Encoder and decoder architecture Energy consumption prediction Office buildings |
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