Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach

Authored by Elie Azar, Sokratis Papadopoulos

Date Published: 2016

DOI: 10.1016/j.enbuild.2016.06.079

Sponsors: No sponsors listed

Platforms: AnyLogic EnergyPlus MATLAB

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Building Performance Simulation (BPS) is an established method used in the design phase of buildings to predict energy consumption and guide design choices. Despite their advanced abilities to model complex building systems, BPS tools typically fail to account for different and changing energy use characteristics of building occupants, contributing to important prediction errors. In parallel, Agent-Based Modeling (ABM) has emerged in recent years as a technique capable of capturing occupants' dynamic energy consumption behaviors and actions. However, ABM lacks the building simulation capabilities to account for the complexity of various building systems in energy calculations. This research proposes a new modeling framework that integrates BPS in ABM using trained regression surrogate models. The framework is unique in its ability to (1) simulate energy use attributes of building occupants and facility managers, (2) translate those attributes to robust energy consumption estimates, and (3) help quantify the impact of uncertainty in human actions on the performance of the built environment. The framework is tested and illustrated in a case study on a prototype office building. Results indicate that providing occupants with control over their building systems can mitigate the effect of uncertainty in human actions on the performance of the built environment. (C) 2016 Elsevier B.V. All rights reserved.

Tags

Uncertainty Design Commercial buildings Office Buildings Framework Consumption Artificial neural-network Occupancy interventions Residential buildings Use behavior