Even though various tools and simulation frameworks have been developed in prior work for evaluating, monitoring, and regulating the energy use in buildings, their deployment has primarily been in the form of standalone applications that consider limited aspects of the entire system.

The results also highlight opportunities for achieving energy savings by influencing the occupants’ comfort related behavior.īuildings consume 40% of the total energy produced in the United States (US), making this sector an opportune choice for devising strategies aimed at reducing energy consumption.

Measuring these effects in a real building for a lengthy period is impractical and resource-intensive and the LABS framework can be used for understanding this system better. These behavioral patterns are also influenced by various interventions (e.g., peer pressure, energy-based education) that may occur in the building. As a case study demonstrating this new framework, a building energy simulation model is coupled with an agent-based occupant behavior model to understand the energy effects of occupants’ thermal comfort related actions (e.g., adjusting the thermostat set point). This study addresses these gaps by proposing a Lightweight and Adaptive Building Simulation (LABS) framework that capitalizes on Lightweight Communications and Marshalling (LCM), an inter-process communication framework widely used by the robotics community. In addition, several of these frameworks embody tight coupling, which means they lack the flexibility to incorporate models and components of decision-makers’ choice. However, the existing coupling frameworks such as Building Control Virtual Test Bed (BCVTB), MLE+, High-Level Architecture (HLA), and Functional Mockup Unit are characterized by their inherent complexity, making it a challenge for the building practitioners to widely deploy them in everyday decision-making. In the building energy analysis domain, several tools have been created in the past to facilitate such analyses. Coupled and distributed simulation helps in understanding the complexity arising from the combined effects of interdependent systems, by connecting and exchanging information across several software programs.

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