Impact of occupant behavior on energy use of HVAC system in offices
The current methods for simulating building energy consumption are often inaccurate, and the error could be as large as 150%. Various types of occupant behavior may explain this inaccuracy. Therefore, it is important to identify an approach to estimate the impact of the behaviors on the energy consu...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2019-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/37/e3sconf_clima2019_04055.pdf |
| _version_ | 1818918988166463488 |
|---|---|
| author | Deng Zhipeng Chen Qingyan |
| author_facet | Deng Zhipeng Chen Qingyan |
| author_sort | Deng Zhipeng |
| collection | DOAJ |
| description | The current methods for simulating building energy consumption are often inaccurate, and the error could be as large as 150%. Various types of occupant behavior may explain this inaccuracy. Therefore, it is important to identify an approach to estimate the impact of the behaviors on the energy consumption. The present study used EnergyPlus program to simulate the energy consumption of the HVAC system in an office building by implementing a behavioral artificial neural network (ANN) model. The behavioral ANN model calculates the probability of behavior occurrence according to indoor air temperature, relative humidity, clothing level and metabolic rate. The probability was used to predict energy use in 20 offices for one month in winter, spring and summer in 2018, respectively. Measured energy data from the offices were used to validate the simulated results. When a behavioral artificial neural network (ANN) model was implemented in the energy simulation, the difference between the simulated results and the measured data was less than 13%. Energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Our further simulations found that adjustment of thermostat set point and clothing level by occupants could lead to 25% and 15% energy use variation in interior offices and exterior offices, respectively. |
| first_indexed | 2024-12-20T00:58:42Z |
| format | Article |
| id | doaj.art-81c13d21aca84b3c843f5685c7884946 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-20T00:58:42Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-81c13d21aca84b3c843f5685c78849462022-12-21T19:59:02ZengEDP SciencesE3S Web of Conferences2267-12422019-01-011110405510.1051/e3sconf/201911104055e3sconf_clima2019_04055Impact of occupant behavior on energy use of HVAC system in officesDeng Zhipeng0Chen Qingyan1Center for High Performance Buildings (CHPB), School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West LafayetteCenter for High Performance Buildings (CHPB), School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West LafayetteThe current methods for simulating building energy consumption are often inaccurate, and the error could be as large as 150%. Various types of occupant behavior may explain this inaccuracy. Therefore, it is important to identify an approach to estimate the impact of the behaviors on the energy consumption. The present study used EnergyPlus program to simulate the energy consumption of the HVAC system in an office building by implementing a behavioral artificial neural network (ANN) model. The behavioral ANN model calculates the probability of behavior occurrence according to indoor air temperature, relative humidity, clothing level and metabolic rate. The probability was used to predict energy use in 20 offices for one month in winter, spring and summer in 2018, respectively. Measured energy data from the offices were used to validate the simulated results. When a behavioral artificial neural network (ANN) model was implemented in the energy simulation, the difference between the simulated results and the measured data was less than 13%. Energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Our further simulations found that adjustment of thermostat set point and clothing level by occupants could lead to 25% and 15% energy use variation in interior offices and exterior offices, respectively.https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/37/e3sconf_clima2019_04055.pdf |
| spellingShingle | Deng Zhipeng Chen Qingyan Impact of occupant behavior on energy use of HVAC system in offices E3S Web of Conferences |
| title | Impact of occupant behavior on energy use of HVAC system in offices |
| title_full | Impact of occupant behavior on energy use of HVAC system in offices |
| title_fullStr | Impact of occupant behavior on energy use of HVAC system in offices |
| title_full_unstemmed | Impact of occupant behavior on energy use of HVAC system in offices |
| title_short | Impact of occupant behavior on energy use of HVAC system in offices |
| title_sort | impact of occupant behavior on energy use of hvac system in offices |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/37/e3sconf_clima2019_04055.pdf |
| work_keys_str_mv | AT dengzhipeng impactofoccupantbehavioronenergyuseofhvacsysteminoffices AT chenqingyan impactofoccupantbehavioronenergyuseofhvacsysteminoffices |