Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building
The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently bring...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9705280/ |
| _version_ | 1818307420443639808 |
|---|---|
| author | Xiao Han Chaohai Zhang Yi Tang Yujian Ye |
| author_facet | Xiao Han Chaohai Zhang Yi Tang Yujian Ye |
| author_sort | Xiao Han |
| collection | DOAJ |
| description | The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy. |
| first_indexed | 2024-12-13T06:58:06Z |
| format | Article |
| id | doaj.art-95ad61dbd50940d58fc00848daff27e5 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-13T06:58:06Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-95ad61dbd50940d58fc00848daff27e52022-12-21T23:56:00ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202022-01-0110248249110.35833/MPCE.2021.0000509705280Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart BuildingXiao Han0Chaohai Zhang1Yi Tang2Yujian Ye3Jiangsu Key Laboratory of New Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106Jiangsu Key Laboratory of New Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106School of Electrical Engineering, Southeast University,Nanjing,China,210096Imperial College London,Department of Electrical and Electronic Engineering,London,U.K.The energy consumption of buildings accounts for approximately 40% of total energy consumption. An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately, and consequently brings benefits to the upstream power grid. This paper proposes a novel physical-data fusion modeling (PFM) method for modeling smart buildings that can accurately assess energy consumption. First, a thermal process model of buildings and an electrical load model that focus on building heating, ventilation, and air conditioning (HVAC) systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings. Second, the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model (i. e., it effectively modifies the electrical load model based on the proposed PFM method). Finally, case studies involving a real-world dataset recorded in a high-tech park in Changzhou, China, demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling (TPM) method and data-driven modeling (DDM) method in terms of the achieved accuracy.https://ieeexplore.ieee.org/document/9705280/Smart buildingphysical-data fusion modeling methodenergy consumptionprecision modelthermal-electrical conversion |
| spellingShingle | Xiao Han Chaohai Zhang Yi Tang Yujian Ye Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building Journal of Modern Power Systems and Clean Energy Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion |
| title | Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building |
| title_full | Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building |
| title_fullStr | Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building |
| title_full_unstemmed | Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building |
| title_short | Physical-data Fusion Modeling Method for Energy Consumption Analysis of Smart Building |
| title_sort | physical data fusion modeling method for energy consumption analysis of smart building |
| topic | Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion |
| url | https://ieeexplore.ieee.org/document/9705280/ |
| work_keys_str_mv | AT xiaohan physicaldatafusionmodelingmethodforenergyconsumptionanalysisofsmartbuilding AT chaohaizhang physicaldatafusionmodelingmethodforenergyconsumptionanalysisofsmartbuilding AT yitang physicaldatafusionmodelingmethodforenergyconsumptionanalysisofsmartbuilding AT yujianye physicaldatafusionmodelingmethodforenergyconsumptionanalysisofsmartbuilding |