Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be...
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| Published in: | Energy (Oxford) Vol. 148; pp. 269 - 282 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Elsevier Ltd
01.04.2018
Elsevier BV |
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| ISSN: | 0360-5442, 1873-6785 |
| Online Access: | Get full text |
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| Abstract | Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be forecasted accurately. Therefore, a novel deep learning based hybrid approach is originally proposed in this paper for deterministic cooling load forecasting with high accuracy. The approach is a hybrid of empirical mode decomposition, deep belief network and ensemble technique. Empirical mode decomposition is applied to decompose the original cooling load data series into several components with better outliers and behaviors. The hidden nonlinear features and high-level invariant structures in data are effectively extracted by layer-wise pre-training based deep belief network. In addition, ensemble technique is introduced and properly designed to mitigate the impact of uncertainties, i.e., model uncertainty and data noise, on forecasting accuracy. Case studies using real cooling load data collected from Shenzhen and Hong Kong have been implemented. The numerical results demonstrate that the proposed forecasting approach exhibits competitive performance when compared to the prediction algorithms of the state of the art. It is therefore convinced that the proposed approach has a high potential for improving the operating performance in energy systems.
•Deep belief network is used to extract the hidden features in cooling load data.•A hybrid forecaster based on EMD and DBN is innovatively proposed.•The diverse errors in term of model misspecification and data noise are analyzed.•Ensemble technique is used to mitigate the diverse errors. |
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| AbstractList | Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be forecasted accurately. Therefore, a novel deep learning based hybrid approach is originally proposed in this paper for deterministic cooling load forecasting with high accuracy. The approach is a hybrid of empirical mode decomposition, deep belief network and ensemble technique. Empirical mode decomposition is applied to decompose the original cooling load data series into several components with better outliers and behaviors. The hidden nonlinear features and high-level invariant structures in data are effectively extracted by layer-wise pre-training based deep belief network. In addition, ensemble technique is introduced and properly designed to mitigate the impact of uncertainties, i.e., model uncertainty and data noise, on forecasting accuracy. Case studies using real cooling load data collected from Shenzhen and Hong Kong have been implemented. The numerical results demonstrate that the proposed forecasting approach exhibits competitive performance when compared to the prediction algorithms of the state of the art. It is therefore convinced that the proposed approach has a high potential for improving the operating performance in energy systems. Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be forecasted accurately. Therefore, a novel deep learning based hybrid approach is originally proposed in this paper for deterministic cooling load forecasting with high accuracy. The approach is a hybrid of empirical mode decomposition, deep belief network and ensemble technique. Empirical mode decomposition is applied to decompose the original cooling load data series into several components with better outliers and behaviors. The hidden nonlinear features and high-level invariant structures in data are effectively extracted by layer-wise pre-training based deep belief network. In addition, ensemble technique is introduced and properly designed to mitigate the impact of uncertainties, i.e., model uncertainty and data noise, on forecasting accuracy. Case studies using real cooling load data collected from Shenzhen and Hong Kong have been implemented. The numerical results demonstrate that the proposed forecasting approach exhibits competitive performance when compared to the prediction algorithms of the state of the art. It is therefore convinced that the proposed approach has a high potential for improving the operating performance in energy systems. •Deep belief network is used to extract the hidden features in cooling load data.•A hybrid forecaster based on EMD and DBN is innovatively proposed.•The diverse errors in term of model misspecification and data noise are analyzed.•Ensemble technique is used to mitigate the diverse errors. |
| Author | Fu, Guoyin |
| Author_xml | – sequence: 1 givenname: Guoyin surname: Fu fullname: Fu, Guoyin email: Paul.Fu@ul.com organization: School of Electric Power, South China University of Technology, Guangzhou, Guangdong, China |
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| Keywords | Deep learning Ensemble technique Air-conditioning system Deep belief network Cooling load prediction |
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| SubjectTerms | Air conditioners Air conditioning Air-conditioning system algorithms Artificial intelligence Belief networks Buildings case studies China Cooling Cooling load prediction Cooling loads Cooling systems data collection Decomposition Deep belief network Deep learning Electricity consumption energy Energy consumption Ensemble technique Feature extraction Forecasting heat Machine learning Mathematical models model uncertainty Outliers (statistics) planning prediction Uncertainty |
| Title | Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system |
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