Modeling occupant energy use interventions in evolving social networks

Occupant behavior in buildings can contribute significantly to building energy demand and consumption. As a result, occupant behavior interventions to promote sustainability are becoming more widespread. Due to the expense in applying interventions, researchers have begun using computer simulations to analyze potential outcomes and better understand how complex systems can affect intervention success, in particular the effect of social network structure. In previous literature, studies have only evaluated social network effects using static social networks which are far from reality. Therefore, in this study we evaluate how a behavior intervention, here a comparative feedback system, is affected as social networks evolve over time using agent-based modeling. Results indicate that static social networks are much less volatile in their behavior and tend to have more convergent behavior relative to dynamic social networks. This implies that for normative interventions, dynamic networks have increased uncertainty in intervention outcome compared to static networks.