10.1016%j.buildenv.2020.106768.pdf (3.51 MB)
Modeling occupant behavior in buildings
journal contribution
posted on 2020-03-16, 15:00 authored by Salvatore Carlucci, Marilena De Simone, Steven FirthSteven Firth, Mikkel B Kjærgaard, Romana Markovic, Mohammad Saiedur Rahaman, Masab Khalid Annaqeeb, Silvia Biandrate, Anooshmita Das, Jakub Wladyslaw Dziedzic, Gianmarco Fajilla, Matteo Favero, Martina Ferrando, Jakob Hahn, Mengjie Han, Yuzhen Peng, Flora Salim, Arno Schlüter, Christoph van TreeckIn the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants’ actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.
Funding
REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology : EP/K002457/1
Research Centre on Zero Emission Neighborhoods in Smart Cities (FME ZEN, Grant n. 257660)
Research Council of Norway (Norges Forskingsrådet)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TR 892/4-1)
Calabria Region Government for Ph.D. scholarship (POR Calabria FSE/FESR 2014–2020, Grant n. H21G18000170006)
Energy Technology Development and Demonstration Program (Energistyrelsen, Grant n. 64018-0558)
Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie, Grant n. 03ET1299A–EnEff Stadt: +EQ-Net and Grant n. 03ET1648A–EnOB: NuData Campus)
Humboldt and Bayer Foundation
History
School
- Architecture, Building and Civil Engineering
Published in
Building and EnvironmentVolume
174Issue
May 2020Publisher
Elsevier BVVersion
- AM (Accepted Manuscript)
Rights holder
© Elsevier LtdPublisher statement
This paper was accepted for publication in the journal Building and Environment and the definitive published version is available at https://doi.org/10.1016/j.buildenv.2020.106768.Acceptance date
2020-02-24Publication date
2020-02-28Copyright date
2020ISSN
0360-1323Publisher version
Language
- en
Depositor
Dr Steven Firth. Deposit date: 16 March 2020Article number
106768Usage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC