Energies 2015,8 11002 Li et al.[86]employed ABM to simulate occupant load in HVAC design in order to optimize HVAC system size.By simulating the correct occupancy behavior characteristics,the model estimated a more accurate load and effectively designed an HVAC system that saved up to 43 percent of total energy.The number of occupants in each specific space at a given time became the main parameter of their proposed model.Erickson et al.[75]also used ABM to optimize HVAC loading and showed a total energy reduction of 14 percent at the room level of commercial buildings.They used wireless camera sensor networks to find occupants'mobility patterns in buildings.Then,they employed ABM to simulate the mobility patterns for various control strategies of HVAC.Li et al.'s [86]and Erickson et al.'s [75] approaches feed various dynamic occupants'information into the ABM simulation tools in order to directly calculate the HVAC loads.HVAC controls the indoor comfort;however,in their models,they did not clearly respond to the ventilation requirement that decreases CO2 levels inside the building. Lee and Malkawi [81]developed an ABM tool that simulates multiple occupant behaviors(i.e.,adjusted clothing levels,adjusted activity levels,window use,blind use,and space heater/personal fan use)in order to predict such behavior changes due to changes in climate and buildings topologies.Their proposed tool is an open architecture program that can adapt to different building functions and climate topologies, and that provides opportunities for an occupant to make decisions based on his/her thermal comfort level. However,this tool cannot track the thermal comfort conditions of individual occupants to fully understand whether they are satisfied with the thermal comfort level.Azar and Menassa [28,80]proposed an ABM technique to simulate the diverse and dynamic energy-use patterns of occupants and their behavior changes over time.This technique also considers various interactions among occupants.Compared to common energy software,their proposed model showed a 25 percent reduction in energy use at a small office due to the correct modeling of occupant behavior.However,this technique is limited to interactions of occupants within a room,and could not account for occupants'interactions in different rooms of a building.Such interactions may be considered to achieve more realistic results. Furthermore,social network type and structure can affect occupants'energy-use behaviors. The commercial sector frequently has complex social structures due to presence of multiple independent entities within the same building [87].In most commercial buildings in the United States,at least two companies (i.e.,entities)work in the same building [88].Some researchers recently employed ABM to simulate interactions of occupants in different entities within a commercial building.ABM can also differentiate the impact of various dynamic interactions of occupants from different social structures/networks [89],which greatly affect occupants'energy use behaviors [32,90].Anderson et al.[78] applied ABM to simulate the interactions of heterogeneous building occupants in their social networks to examine how social network type and structure can affect occupants'energy use behaviors.They considered four social network types:random graph,scale-free network,small-world network,and regular ring lattice.The results from their case study of a commercial building with different social network structures and connectivity levels proved that network type and structure hold significant influence over an occupant's energy-use behavior.Anderson and Lee [91]employed ABM to evaluate the effect of static and dynamic social networks on occupants'energy-use behavior.Their results indicated that dynamic networks increase the uncertainties of energy behavior and therefore have more influence on occupant energy behavior than static networks.However,Anderson et al.[78]and Anderson and Lee [91]did not mention at what rate occupants'energy-use behaviors can be affected.Finding a rate for behavioral change would better indicate how different social networks affect occupants'Energies 2015, 8 11002 Li et al. [86] employed ABM to simulate occupant load in HVAC design in order to optimize HVAC system size. By simulating the correct occupancy behavior characteristics, the model estimated a more accurate load and effectively designed an HVAC system that saved up to 43 percent of total energy. The number of occupants in each specific space at a given time became the main parameter of their proposed model. Erickson et al. [75] also used ABM to optimize HVAC loading and showed a total energy reduction of 14 percent at the room level of commercial buildings. They used wireless camera sensor networks to find occupants’ mobility patterns in buildings. Then, they employed ABM to simulate the mobility patterns for various control strategies of HVAC. Li et al.’s [86] and Erickson et al.’s [75] approaches feed various dynamic occupants’ information into the ABM simulation tools in order to directly calculate the HVAC loads. HVAC controls the indoor comfort; however, in their models, they did not clearly respond to the ventilation requirement that decreases CO2 levels inside the building. Lee and Malkawi [81] developed an ABM tool that simulates multiple occupant behaviors (i.e., adjusted clothing levels, adjusted activity levels, window use, blind use, and space heater/personal fan use) in order to predict such behavior changes due to changes in climate and buildings topologies. Their proposed tool is an open architecture program that can adapt to different building functions and climate topologies, and that provides opportunities for an occupant to make decisions based on his/her thermal comfort level. However, this tool cannot track the thermal comfort conditions of individual occupants to fully understand whether they are satisfied with the thermal comfort level. Azar and Menassa [28,80] proposed an ABM technique to simulate the diverse and dynamic energy-use patterns of occupants and their behavior changes over time. This technique also considers various interactions among occupants. Compared to common energy software, their proposed model showed a 25 percent reduction in energy use at a small office due to the correct modeling of occupant behavior. However, this technique is limited to interactions of occupants within a room, and could not account for occupants’ interactions in different rooms of a building. Such interactions may be considered to achieve more realistic results. Furthermore, social network type and structure can affect occupants’ energy-use behaviors. The commercial sector frequently has complex social structures due to presence of multiple independent entities within the same building [87]. In most commercial buildings in the United States, at least two companies (i.e., entities) work in the same building [88]. Some researchers recently employed ABM to simulate interactions of occupants in different entities within a commercial building. ABM can also differentiate the impact of various dynamic interactions of occupants from different social structures/networks [89], which greatly affect occupants’ energy use behaviors [32,90]. Anderson et al. [78] applied ABM to simulate the interactions of heterogeneous building occupants in their social networks to examine how social network type and structure can affect occupants’ energy use behaviors. They considered four social network types: random graph, scale-free network, small-world network, and regular ring lattice. The results from their case study of a commercial building with different social network structures and connectivity levels proved that network type and structure hold significant influence over an occupant’s energy-use behavior. Anderson and Lee [91] employed ABM to evaluate the effect of static and dynamic social networks on occupants’ energy-use behavior. Their results indicated that dynamic networks increase the uncertainties of energy behavior and therefore have more influence on occupant energy behavior than static networks. However, Anderson et al. [78] and Anderson and Lee [91] did not mention at what rate occupants’ energy-use behaviors can be affected. Finding a rate for behavioral change would better indicate how different social networks affect occupants’