Parameter identification methods for low-order gray box building energy models: A critical review

Serasinghe, Rawisha; Long, Nicholas; Clark, Jordan D.

doi:10.1016/j.enbuild.2024.114123

Title: Parameter identification methods for low-order gray box building energy models: A critical review

Journal Article · 02 April 2024 · Energy and Buildings

DOI: https://doi.org/10.1016/j.enbuild.2024.114123 · OSTI ID:2405068

Serasinghe, Rawisha ^[1];

^[2];

^[1]

The Ohio State Univ., Columbus, OH (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

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

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

Grant/Contract Number:: AC36-08GO28308; EE0009776

OSTI ID:: 2405068

Report Number(s):: NREL/JA--5500-89072; MainId:89851; UUID:6dd0dd40-9f56-47db-b23d-c5ccbdedaf1b; MainAdminId:73138

Journal Information:: Energy and Buildings, Journal Name: Energy and Buildings Vol. 311; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
building energy modeling
gray box modeling
reduced order models

Title: Parameter identification methods for low-order gray box building energy models: A critical review

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References (88)

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