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

Abstract

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-usemore » 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.« less

Authors:

Chen, Jianli ^[1]; Adhikari, Rajendra  ^[2];

• Search DOE PAGES for author "Adhikari, Rajendra"
• Search DOE PAGES for ORCID "0000-0002-0832-0880"

Wilson, Eric  ^[2];

• Search DOE PAGES for author "Wilson, Eric"
• Search DOE PAGES for ORCID "0000-0003-3185-655X"

Robertson, Joseph  ^[2];

• Search DOE PAGES for author "Robertson, Joseph"
• Search DOE PAGES for ORCID "0000-0003-0994-9899"

Fontanini, Anthony  ^[2];

• Search DOE PAGES for author "Fontanini, Anthony"
• Search DOE PAGES for ORCID "0000-0002-5921-9635"

Polly, Ben  ^[2];

• Search DOE PAGES for author "Polly, Ben"
• Search DOE PAGES for ORCID "0000-0002-5700-4163"

Olawale, Opeoluwa ^[2]

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+ Show Author Affiliations

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

Publication Date:

Mon Sep 05 00:00:00 EDT 2022

Research Org.:

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

Sponsoring Org.:

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

OSTI Identifier:

1889672

Report Number(s):

NREL/JA-5500-83706
Journal ID: ISSN 0306-2619; MainId:84479;UUID:583b3b67-ebde-4d39-8963-69e448cadbe3;MainAdminID:67512

Grant/Contract Number:  

AC36-08GO28308

Resource Type:

Accepted Manuscript

Journal Name:

Applied Energy

Additional Journal Information:

Journal Volume: 325; Journal ID: ISSN 0306-2619

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

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