Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

Chen, Jianli; Adhikari, Rajendra; Wilson, Eric; Robertson, Joseph; Fontanini, Anthony; Polly, Ben; Olawale, Opeoluwa

doi:10.1016/j.apenergy.2022.119890

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

Journal Article · Mon Sep 05 00:00:00 EDT 2022 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2022.119890· OSTI ID:1889672

Chen, Jianli ^[1]; ^[2]; ^[2]; ^[2]; ^[2]; ^[2]; Olawale, Opeoluwa ^[2]

Univ. of Utah, Salt Lake City, UT (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1889672

Report Number(s):: NREL/JA-5500-83706; MainId:84479; UUID:583b3b67-ebde-4d39-8963-69e448cadbe3; MainAdminID:67512

Journal Information:: Applied Energy, Journal Name: Applied Energy Vol. 325; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (40)

A model for generating household electricity load profiles Paatero, Jukka V.; Lund, Peter D. International Journal of Energy Research, Vol. 30, Issue 5 https://doi.org/10.1002/er.1136	journal	January 2006
K-modes Clustering Chaturvedi, Anil; Green, Paul E.; Caroll, J. Douglas Journal of Classification, Vol. 18, Issue 1 https://doi.org/10.1007/s00357-001-0004-3	journal	January 2001
An end-use electricity load simulation model Bartels, Robert; Fiebig, Denzil G.; Garben, Michael Utilities Policy, Vol. 2, Issue 1 https://doi.org/10.1016/0957-1787(92)90055-N	journal	January 1992
EnergyPlus: creating a new-generation building energy simulation program Crawley, Drury B.; Lawrie, Linda K.; Winkelmann, Frederick C. Energy and Buildings, Vol. 33, Issue 4 https://doi.org/10.1016/S0378-7788(00)00114-6	journal	April 2001
A high-resolution stochastic model of domestic activity patterns and electricity demand Widén, Joakim; Wäckelgård, Ewa Applied Energy, Vol. 87, Issue 6 https://doi.org/10.1016/j.apenergy.2009.11.006	journal	June 2010
A highly resolved modeling technique to simulate residential power demand Muratori, Matteo; Roberts, Matthew C.; Sioshansi, Ramteen Applied Energy, Vol. 107, p. 465-473 https://doi.org/10.1016/j.apenergy.2013.02.057	journal	July 2013
Residential activity pattern modelling through stochastic chains of variable memory length Ramírez-Mendiola, José Luis; Grünewald, Philipp; Eyre, Nick Applied Energy, Vol. 237 https://doi.org/10.1016/j.apenergy.2019.01.019	journal	March 2019
A review of bottom-up building stock models for energy consumption in the residential sector Kavgic, M.; Mavrogianni, A.; Mumovic, D. Building and Environment, Vol. 45, Issue 7 https://doi.org/10.1016/j.buildenv.2010.01.021	journal	July 2010
A bottom-up stochastic model to predict building occupants' time-dependent activities Wilke, Urs; Haldi, Frédéric; Scartezzini, Jean-Louis Building and Environment, Vol. 60 https://doi.org/10.1016/j.buildenv.2012.10.021	journal	February 2013
A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison Aerts, D.; Minnen, J.; Glorieux, I. Building and Environment, Vol. 75 https://doi.org/10.1016/j.buildenv.2014.01.021	journal	May 2014
Air-conditioning usage conditional probability model for residential buildings Ren, Xiaoxin; Yan, Da; Wang, Chuang Building and Environment, Vol. 81 https://doi.org/10.1016/j.buildenv.2014.06.022	journal	November 2014
A comprehensive review of time use surveys in modelling occupant presence and behavior: Data, methods, and applications Osman, Mohamed; Ouf, Mohamed Building and Environment, Vol. 196 https://doi.org/10.1016/j.buildenv.2021.107785	journal	June 2021
A review on buildings energy consumption information Pérez-Lombard, Luis; Ortiz, José; Pout, Christine Energy and Buildings, Vol. 40, Issue 3 https://doi.org/10.1016/j.enbuild.2007.03.007	journal	January 2008
A high-resolution domestic building occupancy model for energy demand simulations Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 40, Issue 8 https://doi.org/10.1016/j.enbuild.2008.02.006	journal	January 2008
Domestic lighting: A high-resolution energy demand model Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 41, Issue 7 https://doi.org/10.1016/j.enbuild.2009.02.010	journal	July 2009
Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation Widén, Joakim; Lundh, Magdalena; Vassileva, Iana Energy and Buildings, Vol. 41, Issue 7 https://doi.org/10.1016/j.enbuild.2009.02.013	journal	July 2009
Domestic electricity use: A high-resolution energy demand model Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 42, Issue 10 https://doi.org/10.1016/j.enbuild.2010.05.023	journal	October 2010
A high spatial resolution residential energy model based on American Time Use Survey data and the bootstrap sampling method Chiou, Yun-Shang; Carley, Kathleen M.; Davidson, Cliff I. Energy and Buildings, Vol. 43, Issue 12 https://doi.org/10.1016/j.enbuild.2011.09.020	journal	December 2011
Dynamic modeling of presence of occupants using inhomogeneous Markov chains Andersen, Philip Delff; Iversen, Anne; Madsen, Henrik Energy and Buildings, Vol. 69 https://doi.org/10.1016/j.enbuild.2013.10.001	journal	February 2014
Stochastic model for electrical loads in Mediterranean residential buildings: Validation and applications Ortiz, Joana; Guarino, Francesco; Salom, Jaume Energy and Buildings, Vol. 80 https://doi.org/10.1016/j.enbuild.2014.04.053	journal	September 2014
Model for electric load profiles with high time resolution for German households Fischer, David; Härtl, Andreas; Wille-Haussmann, Bernhard Energy and Buildings, Vol. 92 https://doi.org/10.1016/j.enbuild.2015.01.058	journal	April 2015
There's a measure for that! Roth, Amir; Goldwasser, David; Parker, Andrew Energy and Buildings, Vol. 117 https://doi.org/10.1016/j.enbuild.2015.09.056	journal	April 2016
Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation Diao, Longquan; Sun, Yongjun; Chen, Zejun Energy and Buildings, Vol. 147 https://doi.org/10.1016/j.enbuild.2017.04.072	journal	July 2017
A review on occupant behavior in urban building energy models Happle, Gabriel; Fonseca, Jimeno A.; Schlueter, Arno Energy and Buildings, Vol. 174 https://doi.org/10.1016/j.enbuild.2018.06.030	journal	September 2018
High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications Bouvenot, Jean-Baptiste; Latour, Benjamin; Flament, Bernard Energy and Buildings, Vol. 229 https://doi.org/10.1016/j.enbuild.2020.110480	journal	December 2020
A dissimilarity measure for the k-Modes clustering algorithm Cao, Fuyuan; Liang, Jiye; Li, Deyu Knowledge-Based Systems, Vol. 26 https://doi.org/10.1016/j.knosys.2011.07.011	journal	February 2012
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques Swan, Lukas G.; Ugursal, V. Ismet Renewable and Sustainable Energy Reviews, Vol. 13, Issue 8, p. 1819-1835 https://doi.org/10.1016/j.rser.2008.09.033	journal	October 2009
A review and an analysis of the residential electric load curve models Grandjean, A.; Adnot, J.; Binet, G. Renewable and Sustainable Energy Reviews, Vol. 16, Issue 9 https://doi.org/10.1016/j.rser.2012.08.013	journal	December 2012
Developing a common approach for classifying building stock energy models Langevin, J.; Reyna, J. L.; Ebrahimigharehbaghi, S. Renewable and Sustainable Energy Reviews, Vol. 133 https://doi.org/10.1016/j.rser.2020.110276	journal	November 2020
Occupancy schedules for energy simulation in new prEN16798-1 and ISO/FDIS 17772-1 standards Ahmed, Kaiser; Akhondzada, Ali; Kurnitski, Jarek Sustainable Cities and Society, Vol. 35 https://doi.org/10.1016/j.scs.2017.07.010	journal	November 2017
Negative Wholesale Electricity Prices in the German, French and Belgian Day-Ahead, Intra-Day and Real-Time Markets De Vos, Kristof The Electricity Journal, Vol. 28, Issue 4 https://doi.org/10.1016/j.tej.2015.04.001	journal	May 2015
Synthetically derived profiles for representing occupant-driven electric loads in Canadian housing Armstrong, Marianne M.; Swinton, Mike C.; Ribberink, Hajo Journal of Building Performance Simulation, Vol. 2, Issue 1 https://doi.org/10.1080/19401490802706653	journal	March 2009
A cross analysis of existing methods for modelling household appliance use Yamaguchi, Y.; Yilmaz, S.; Prakash, N. Journal of Building Performance Simulation, Vol. 12, Issue 2 https://doi.org/10.1080/19401493.2018.1497087	journal	July 2018
A bottom-up approach to residential load modeling Capasso, A.; Grattieri, W.; Lamedica, R. IEEE Transactions on Power Systems, Vol. 9, Issue 2 https://doi.org/10.1109/59.317650	journal	May 1994
Residential Load Shape Modelling Based on Customer Behavior Walker, C. F.; Pokoski, J. L. IEEE Transactions on Power Apparatus and Systems, Vol. PAS-104, Issue 7 https://doi.org/10.1109/TPAS.1985.319202	journal	July 1985
Development of Standardized Domestic Hot Water Event Schedules for Residential Buildings Hendron, Robert; Burch, Jay ASME 2007 Energy Sustainability Conference https://doi.org/10.1115/ES2007-36104	conference	January 2007
A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile Mahmood, Imran; Nasir, Hasan Arshad SIMULATION, Vol. 96, Issue 8 https://doi.org/10.1177/0037549720923401	journal	June 2020
2014 Building America House Simulation Protocols Wilson, E.; Engebrecht-Metzger, C.; Horowitz, S. https://doi.org/10.2172/1126820	report	March 2014
Development of Realistic Water Draw Profiles for California Residential Water Heating Energy Estimation Kruis, Neal; Wilcox, Bruce; Lutz, Jim Building Simulation Conference Proceedings https://doi.org/10.26868/25222708.2017.237	conference	August 2017
Conditional Demand Analysis for Estimating Residential End-Use Load Profiles Aigner, Dennis J.; Sorooshian, Cynts; Kerwin, Pamela The Energy Journal, Vol. 5, Issue 3 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol5-No3-6	journal	July 1984

Similar Records

Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules

Journal Article · Wed Jul 08 20:00:00 EDT 2020 · Applied Energy · OSTI ID:1660214

Short-run residential demand for electricity

Journal Article · Sat Oct 31 23:00:00 EST 1981 · Rev. Econ. Stat.; (United States) · OSTI ID:5774861

Household energy consumption and expenditures 1993

Technical Report · Thu Oct 05 00:00:00 EDT 1995 · OSTI ID:110664

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
Markov chain
agent-based modeling
building stock modeling
occupant modeling
residential electricity use
stochastic occupant behavior model
urban building energy modeling

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

Citation Formats

References (40)

Similar Records

Related Subjects