A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption
Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/2/567 |
| _version_ | 1797621941256847360 |
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| author | Jiefan Gu Peng Xu Ying Ji |
| author_facet | Jiefan Gu Peng Xu Ying Ji |
| author_sort | Jiefan Gu |
| collection | DOAJ |
| description | Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R<sup>2</sup> between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications. |
| first_indexed | 2024-03-11T09:03:11Z |
| format | Article |
| id | doaj.art-8ef755931c8e46eb9d06f4ac963eab2c |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T09:03:11Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-8ef755931c8e46eb9d06f4ac963eab2c2023-11-16T19:34:33ZengMDPI AGBuildings2075-53092023-02-0113256710.3390/buildings13020567A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy ConsumptionJiefan Gu0Peng Xu1Ying Ji2College of Mechanical and Energy Engineering, Tongji University, Shanghai 201804, ChinaCollege of Mechanical and Energy Engineering, Tongji University, Shanghai 201804, ChinaFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, ChinaOccupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R<sup>2</sup> between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications.https://www.mdpi.com/2075-5309/13/2/567occupant behaviorbuilding energy consumptionbalance point temperaturetotal heat transmission coefficient |
| spellingShingle | Jiefan Gu Peng Xu Ying Ji A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption Buildings occupant behavior building energy consumption balance point temperature total heat transmission coefficient |
| title | A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption |
| title_full | A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption |
| title_fullStr | A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption |
| title_full_unstemmed | A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption |
| title_short | A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption |
| title_sort | fast method for calculating the impact of occupancy on commercial building energy consumption |
| topic | occupant behavior building energy consumption balance point temperature total heat transmission coefficient |
| url | https://www.mdpi.com/2075-5309/13/2/567 |
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