A closed-loop data-fusion framework for air conditioning load prediction based on LBF
Accurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722011337 |
| _version_ | 1797901921344815104 |
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| author | Ning He Liqiang Liu Cheng Qian Lijun Zhang Ziqi Yang Shang Li |
| author_facet | Ning He Liqiang Liu Cheng Qian Lijun Zhang Ziqi Yang Shang Li |
| author_sort | Ning He |
| collection | DOAJ |
| description | Accurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long short-term memory (LSTM) and back propagation neural network (BP) is proposed. Firstly, spearman correlation analysis is used to select the influencing factors with high correlation as feature input. Aiming at the problem that the original signal is easy to be disturbed by noise and the data features are not obvious, locally weighted scatterplot smoothing (LOWESS) method is used to denoise the data to improve the data quality for further accurate prediction. Secondly, the data-driven air conditioning load state-space representation is established, which takes air conditioning load as the state variable and takes the load features collected by the sensor in real-time as the input variables. Thirdly, combined with the space representation method of air conditioning load based on LSTM-BP, PF is introduced to estimate the air conditioning load by using the fusion model. Meanwhile, the output load value of BP is fed back to the fusion model as the observation value to update the state-space representation of air conditioning load. Finally, two practical cases are used to verify the effectiveness of the method. The results indicate that the proposed method can provide more accurate and robust air conditioning load prediction. |
| first_indexed | 2024-04-10T09:09:37Z |
| format | Article |
| id | doaj.art-23958f1cb4044cc2aa1d03248cbeca30 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:37Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-23958f1cb4044cc2aa1d03248cbeca302023-02-21T05:11:53ZengElsevierEnergy Reports2352-48472022-11-01877247734A closed-loop data-fusion framework for air conditioning load prediction based on LBFNing He0Liqiang Liu1Cheng Qian2Lijun Zhang3Ziqi Yang4Shang Li5School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, 710049, ChinaSchool of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, ChinaSchool of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China; Corresponding author.School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, ChinaSchool of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, ChinaSchool of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, ChinaAccurate air conditioning load prediction is a key component of intelligent building management system for ensuring energy saving and safe operation of air conditioning system. In order to improve the prediction accuracy, a particle filter (PF) load prediction fusion estimation method based on long short-term memory (LSTM) and back propagation neural network (BP) is proposed. Firstly, spearman correlation analysis is used to select the influencing factors with high correlation as feature input. Aiming at the problem that the original signal is easy to be disturbed by noise and the data features are not obvious, locally weighted scatterplot smoothing (LOWESS) method is used to denoise the data to improve the data quality for further accurate prediction. Secondly, the data-driven air conditioning load state-space representation is established, which takes air conditioning load as the state variable and takes the load features collected by the sensor in real-time as the input variables. Thirdly, combined with the space representation method of air conditioning load based on LSTM-BP, PF is introduced to estimate the air conditioning load by using the fusion model. Meanwhile, the output load value of BP is fed back to the fusion model as the observation value to update the state-space representation of air conditioning load. Finally, two practical cases are used to verify the effectiveness of the method. The results indicate that the proposed method can provide more accurate and robust air conditioning load prediction.http://www.sciencedirect.com/science/article/pii/S2352484722011337Air conditioning load predictionLong short-term memoryBack propagation neural networkParticle filterLocally weighted scatterplot smoothing |
| spellingShingle | Ning He Liqiang Liu Cheng Qian Lijun Zhang Ziqi Yang Shang Li A closed-loop data-fusion framework for air conditioning load prediction based on LBF Energy Reports Air conditioning load prediction Long short-term memory Back propagation neural network Particle filter Locally weighted scatterplot smoothing |
| title | A closed-loop data-fusion framework for air conditioning load prediction based on LBF |
| title_full | A closed-loop data-fusion framework for air conditioning load prediction based on LBF |
| title_fullStr | A closed-loop data-fusion framework for air conditioning load prediction based on LBF |
| title_full_unstemmed | A closed-loop data-fusion framework for air conditioning load prediction based on LBF |
| title_short | A closed-loop data-fusion framework for air conditioning load prediction based on LBF |
| title_sort | closed loop data fusion framework for air conditioning load prediction based on lbf |
| topic | Air conditioning load prediction Long short-term memory Back propagation neural network Particle filter Locally weighted scatterplot smoothing |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722011337 |
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