Background: In recent years monitoring of user behavior became an imperative for building energy optimization. Very often there is a significant discrepancy between predicted building energy performance at the design stage and the actual one rendered during the building operation. This stems from the difference in users’ behavior. In spite of that, users’ interaction and feedback is rarely taken into account and evidence of the impact of occupants’ behavior on energy consumption is still scarce. Thus, the purpose of this study is to apply crowd-sensing techniques to understand how energy is consumed, define appropriate performance indicators, and provide inputs for building operations on more efficient use of resources. Methods: Monitoring strategies were implemented in an office lab with controlled variables to collect quality data on occupancy patterns, ambient factors and energy consumption. In addition, crowd-sensing techniques were applied to model user behavior at different ambient conditions over time and to contrast this behavior with energy consumption patterns combined with new inquiry tools to identify how occupants perceive their comfort level. Also, a set of energy efficiency indicators was used to compare energy performance in different periods. Results: It was found out, that there is a strong relation between users’ behavior and energy consumption, however, more than 50% of energy was consumed when no users’ activity was registered. Energy performance indicators revealed that measuring energy efficiency in terms of kWh per surface area encourages less efficient use of space and therefore including coefficient of person hours is advised. It was also found out that users do not relay fully on feedback mechanism and they rather prefer to take an action to adapt the ambient conditions instead of simply expressing their opinion. Analysis of energy usage during the covid-19 lock down revealed substantial use of energy unlike it was expected. It was because home computers were used only as terminals, while the actual tasks were performed on the lab computers with remote desktop connection turned on 24/7. In addition, energy consumed by each employee at their home has to be taken into account. Moreover, a set of practical recommendations was formulated.
Influence of User Behavior on Energy Consumption and Its Relation With Comfort. A Case Study Based on Sensor and Crowd-Sensed Data