Investigating the Impact of Actual and Modeled Occupant Behavior Information Input to Building Performance Simulation

Mengda Jia,Nathan Weyer,Robert Ries,Ravi Srinivasan,Gnana Bharathy

doi:10.3390/buildings11010032

Mengda Jia, Nathan Weyer + Show 3 more

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https://doi.org/10.3390/buildings11010032

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Abstract

Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.

Highlights

Building energy efficiency attracts enormous attention from industry and academia.Buildings take up more than one third of the world’s total energy use [1]
This paper’s contribution is two–fold: first, the simulation experiment quantifies the mutual impact of building and its occupants, under multiple behavioral input using the same test bed, essentially enabling the test bed to plan energy conservation measures (ECM); and second, the findings of this study reveal potential development areas and open up avenues for future enhancement of Building energy modeling (BEM) tools
The schedule update process was carried out for each occupant in five separate IDF files rather than a simultaneous execution, considering the computational speed and a more specific comparison among different individuals. This setting is applied to survey– and agent–based modeling (ABM)–based scenarios respectively, with each file representing the behavior information change of one occupant

Summary

Introduction

Building energy efficiency attracts enormous attention from industry and academia. Buildings take up more than one third of the world’s total energy use [1]. There is a great potential to reduce greenhouse gas emissions and pursue the ultimate goal of sustainability by improving building energy performance. Building energy modeling (BEM) is one of the most effective paths to enable an energy–. A valid simulation model makes it convenient for users to assess building energy performance, identify system control strategies, and explore opportunities for occupant comfort enhancement [3]. In current BEM approaches, a wide range of factors associated with building energy use can be captured and simulated, such as: (1) climate, (2) building characteristics and material properties, (3) building service systems and user-related operation, (4) economic factors, (5) indoor environmental quality and other criteria, and (6)

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