Energies 2015,8 11000 are doing.Considering all of these levels of occupancy resolution in conjunction with temporal and spatial resolution leads to correct and successful occupancy sensing. Days Hours Minutes Seconds Activity Identity 6u!pl!ng Count Occupancy Spatial resolution Temporal resolution Figure 1.Dimensions of occupancy sensing resolution [62] In commercial buildings,building management systems typically dedicate operational settings of main end-users-such as HVAC-according to assumed occupied and unoccupied periods during a day [63].However,it has been found that average building occupancy for commercial buildings is at most a third of its maximum designed-for occupancy,even among office spaces at their peak working hours [64].In this regards,occupancy-sensing data provides significant information for building management systems to adapt their system-e.g.,HVAC and lighting-according to the exact number of occupants in a building at a given time [65-67].The current status of sensing technologies therefore provides opportunities to economically monitor individual occupants and their energy consumption [68,69]. Concerning the linkage between aggregated energy data and occupancy-sensing data in commercial buildings,in order to find the energy use of individual occupants,Kavulya and Becerik-Gerber [70] linked the results of occupants'observations with NILM to study individual occupant's energy-consuming behaviors in an office environment.They employed visual observation in order to collect occupancy-sensing data.Their research was conducted for five weeks in an office space containing five occupants,and their results identified the energy consumption and potential waste ofeach occupant. The outcome of their research indicated the ability of the linkage concept to monitor occupant-specific energy consumption.Although visual observation is not an effective method for collecting occupancy-sensing data,this research revealed opportunities for further research into the concept of coupling NILM with occupancy-sensing technologies to track the energy consumption of individual occupants. 3.Simulating Occupant Energy-Consuming Behaviors Nowadays,simulation approaches are widely used in various branches of science in order to model a real process over time.In built environments,a number of simulation models and software exist to predict energy consumption during the operational phase.These common,traditional energy software (e.g.,BLAST,DOE-2.2,eQUEST,EnergyPlus,and ENERGY-10)are typically employed during the construction phase of buildings to simulate and predict the energy use within the operational phase.Energies 2015, 8 11000 are doing. Considering all of these levels of occupancy resolution in conjunction with temporal and spatial resolution leads to correct and successful occupancy sensing. Figure 1. Dimensions of occupancy sensing resolution [62]. In commercial buildings, building management systems typically dedicate operational settings of main end-users—such as HVAC—according to assumed occupied and unoccupied periods during a day [63]. However, it has been found that average building occupancy for commercial buildings is at most a third of its maximum designed-for occupancy, even among office spaces at their peak working hours [64]. In this regards, occupancy-sensing data provides significant information for building management systems to adapt their system—e.g., HVAC and lighting—according to the exact number of occupants in a building at a given time [65–67]. The current status of sensing technologies therefore provides opportunities to economically monitor individual occupants and their energy consumption [68,69]. Concerning the linkage between aggregated energy data and occupancy-sensing data in commercial buildings, in order to find the energy use of individual occupants, Kavulya and Becerik-Gerber [70] linked the results of occupants’ observations with NILM to study individual occupant’s energy-consuming behaviors in an office environment. They employed visual observation in order to collect occupancy-sensing data. Their research was conducted for five weeks in an office space containing five occupants, and their results identified the energy consumption and potential waste of each occupant. The outcome of their research indicated the ability of the linkage concept to monitor occupant-specific energy consumption. Although visual observation is not an effective method for collecting occupancy-sensing data, this research revealed opportunities for further research into the concept of coupling NILM with occupancy-sensing technologies to track the energy consumption of individual occupants. 3. Simulating Occupant Energy-Consuming Behaviors Nowadays, simulation approaches are widely used in various branches of science in order to model a real process over time. In built environments, a number of simulation models and software exist to predict energy consumption during the operational phase. These common, traditional energy software (e.g., BLAST, DOE-2.2, eQUEST, EnergyPlus, and ENERGY-10) are typically employed during the construction phase of buildings to simulate and predict the energy use within the operational phase