Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption
Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.730640/full |
| _version_ | 1819239284890140672 |
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| author | Yang Xu Yang Xu Weijun Gao Weijun Gao Fanyue Qian Yanxue Li |
| author_facet | Yang Xu Yang Xu Weijun Gao Weijun Gao Fanyue Qian Yanxue Li |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed to evaluate the potential of using an attentional-based LSTM network (A-LSTM) to predict HVAC energy consumption in practical applications. To evaluate the application potential of the A-LSTM model in real cases, the training set and test set used in experiments are the real energy consumption data collected by Kitakyushu Science Research Park in Japan. Pearce analysis was first carried out on the source data set and built the target database. Then five baseline models (A-LSTM, LSTM, RNN, DNN, and SVR) were built. Besides, to optimize the super parameters of the model, the Tree-structured of Parzen Estimators (TPE) algorithm was introduced. Finally, the applications are performed on the target database, and the results are analyzed from multiple perspectives, including model comparisons on different sizes of the training set, model comparisons on different system operation modes, graphical examination, etc. The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption. |
| first_indexed | 2024-12-23T13:49:41Z |
| format | Article |
| id | doaj.art-1b15fc3d229c4e78809e81a00f0bbc84 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-23T13:49:41Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-1b15fc3d229c4e78809e81a00f0bbc842022-12-21T17:44:37ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-08-01910.3389/fenrg.2021.730640730640Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy ConsumptionYang Xu0Yang Xu1Weijun Gao2Weijun Gao3Fanyue Qian4Yanxue Li5Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao, ChinaFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, JapanInnovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao, ChinaFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, JapanFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, JapanInnovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao, ChinaPredicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed to evaluate the potential of using an attentional-based LSTM network (A-LSTM) to predict HVAC energy consumption in practical applications. To evaluate the application potential of the A-LSTM model in real cases, the training set and test set used in experiments are the real energy consumption data collected by Kitakyushu Science Research Park in Japan. Pearce analysis was first carried out on the source data set and built the target database. Then five baseline models (A-LSTM, LSTM, RNN, DNN, and SVR) were built. Besides, to optimize the super parameters of the model, the Tree-structured of Parzen Estimators (TPE) algorithm was introduced. Finally, the applications are performed on the target database, and the results are analyzed from multiple perspectives, including model comparisons on different sizes of the training set, model comparisons on different system operation modes, graphical examination, etc. The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption.https://www.frontiersin.org/articles/10.3389/fenrg.2021.730640/fullenergy consumption predictionultra-short-term forecastdeep learningLSTM networkattention mechanism |
| spellingShingle | Yang Xu Yang Xu Weijun Gao Weijun Gao Fanyue Qian Yanxue Li Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption Frontiers in Energy Research energy consumption prediction ultra-short-term forecast deep learning LSTM network attention mechanism |
| title | Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption |
| title_full | Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption |
| title_fullStr | Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption |
| title_full_unstemmed | Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption |
| title_short | Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption |
| title_sort | potential analysis of the attention based lstm model in ultra short term forecasting of building hvac energy consumption |
| topic | energy consumption prediction ultra-short-term forecast deep learning LSTM network attention mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.730640/full |
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