Search

The acceptable temperature ranges of temporarily occupied space (TOS) such as supermarkets could be different from the ranges of normal indoor spaces such as offices. However, current thermal comfort standards do not distinguish such a space from normal spaces and identify its design temperature range clearly. This study proposed TOS, and explored human responses to TOS with the ultimate goal of finding its optimal design temperature range in winter. Questionnaire surveys were performed at six temperature levels in a climatic chamber in Tianjin. Human participants were exposed to outdoors for 20 min and then they stayed in the chamber for 40 min. During this period their thermal sensation, comfort and preference were recorded for evaluation at five different time points. The study shows that their neutral temperature ranges from 16.9 °C to 18.4 °C. Their thermal preference maintains “no change” in the range of 15.4 °C–19.3 °C. Meanwhile, from the viewpoint of thermal comfort, a temperature range of 15.4 °C–17.4 °C is highly desirable for occupants to stay in TOS. Considering the above three ranges, the optimal design temperature range for TOS in Tianjin is recommended to be 16.9 °C–17.4 °C, which achieves highly desirable thermal comfort, neutral thermal sensation and “no change” thermal preference simultaneously. This range is significantly lower than the design temperature range in Chinese national codes (20 °C–22 °C). The results of this study can be used as reference for future code formulation and building retrofitting. [ABSTRACT FROM AUTHOR]

• The occupant-related electricity consumption is explained by a growth limit theory. • A two-part model is proposed to predict the building electricity consumption. • The variable part is related to the building function and equipment control mode. • The error of the prediction results does not exceed 5% compared with actual bills. The accurate prediction of a building's electricity consumption can provide baselines for energy management and indicate the building's energy-saving potential. However, electricity utilization indicators based on the building area are no longer applicable because of the overall increase in the building area per person and occupant energy demand of buildings. To tackle this challenge, the building electricity consumption was split into 'basic' and 'variable' forms in this study and a two-part building electricity consumption prediction model based on human behavior was established. The basic electricity consumption is related to the building area, while the variable electricity consumption is related to the building occupancy. The probability function and Markov model were used to describe the electricity consumption caused by the randomness of occupancy in buildings. The model was validated using three campus buildings. Based on the comparison of the actual electricity bills of the campus buildings with the model prediction results, the model accuracy error is less than 5%. The results show that the building electricity consumption of a building has a growth limit when multiple people share a room, which is related to a person's initiative or ability to control the electricity use. [ABSTRACT FROM AUTHOR]