TY - JOUR
T1 - Impact of occupant behavior on energy use of HVAC system in offices
AU - Deng, Zhipeng
AU - Chen, Qingyan
N1 - Funding Information:
The authors would like to thank Dr. Orkan Kurtulus of the Center for High Performance Buildings at Purdue University for his assistance in setting the BAS in the HLAB building. We would also like to thank all the occupants of the offices for their participation and assistance in obtaining the data reported in this study. The research presented in this paper was supported by the Center for High Performance Buildings at Purdue University.
Publisher Copyright:
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0
PY - 2019/8/13
Y1 - 2019/8/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071847021&partnerID=8YFLogxK
U2 - 10.1051/e3sconf/201911104055
DO - 10.1051/e3sconf/201911104055
M3 - Conference article
AN - SCOPUS:85071847021
SN - 2555-0403
VL - 111
JO - E3S Web of Conferences
JF - E3S Web of Conferences
M1 - 04055
T2 - 13th REHVA World Congress, CLIMA 2019
Y2 - 26 May 2019 through 29 May 2019
ER -
