Abstract:
Over the past years, several tools and methods have been developed to address performance-related designs and provide designers with integrative platforms to estimate building energy consumption and mitigate its impact. However, the predictions obtained through different energy modeling engines have been typically deviating from the actual energy consumption.
As such, many efforts have attempted at bridging this so-called “performance gap” by integrating behavioral aspects within energy modeling besides design parameters. Yet, this is being conducted in a fragmented fashion whereby synchronizing the geometric exchange of Building Information Modeling (BIM) to Building Energy Modeling (BEM) was done independently from incorporating, through Agent-Based Modeling (ABM), building occupants’ behavior vis-à-vis energy consumption.
Therefore, the proposed research effort presents an energy-aware intelligent framework targeted at merging the aforementioned approaches and assessing the diverse/ dynamic energy-use behavior and comfort of occupants using BIM, BEM and ABM.
Accordingly, the proposed research objective is four-fold : (1) synchronizing the data exchange between energy and building information models without the need for an interface, (2) assessing the behavior of building occupants while considering both parametric design and behavioral factors, (3) designing an ABM model to test different ways of applying behavioral insights to encourage the uptake of effective energy efficient measures, and (4) setting-up a hybrid model of behavioral-based simulation and a machine learning algorithm to predict a building’s energy consumption.
