A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data
Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greate...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1395 |
| _version_ | 1797396265873440768 |
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| author | Shuang Yuan Zhen-Zhong Hu Jia-Rui Lin Yun-Yi Zhang |
| author_facet | Shuang Yuan Zhen-Zhong Hu Jia-Rui Lin Yun-Yi Zhang |
| author_sort | Shuang Yuan |
| collection | DOAJ |
| description | Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. |
| first_indexed | 2024-03-09T00:48:50Z |
| format | Article |
| id | doaj.art-abf20268bc904c0382fb3758f63121fb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:48:50Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-abf20268bc904c0382fb3758f63121fb2023-12-11T17:21:43ZengMDPI AGSensors1424-82202021-02-01214139510.3390/s21041395A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption DataShuang Yuan0Zhen-Zhong Hu1Jia-Rui Lin2Yun-Yi Zhang3Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaBuildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China.https://www.mdpi.com/1424-8220/21/4/1395building energy consumptiondata integrationenergy usage diagnosisartificial neural network |
| spellingShingle | Shuang Yuan Zhen-Zhong Hu Jia-Rui Lin Yun-Yi Zhang A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data Sensors building energy consumption data integration energy usage diagnosis artificial neural network |
| title | A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data |
| title_full | A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data |
| title_fullStr | A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data |
| title_full_unstemmed | A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data |
| title_short | A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data |
| title_sort | framework for the automatic integration and diagnosis of building energy consumption data |
| topic | building energy consumption data integration energy usage diagnosis artificial neural network |
| url | https://www.mdpi.com/1424-8220/21/4/1395 |
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