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Title: Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?

Abstract

We present that analytics software is increasingly used to improve and maintain operational efficiency in commercial buildings. Energy managers, owners, and operators are using a diversity of commercial offerings often referred to as Energy Information Systems, Fault Detection and Diagnostic (FDD) systems, or more broadly Energy Management and Information Systems, to cost-effectively enable savings on the order of ten to twenty percent. Most of these systems use data from meters and sensors, with rule-based and/or data-driven models to characterize system and building behavior. In contrast, physics-based modeling uses first-principles and engineering models (e.g., efficiency curves) to characterize system and building behavior. Historically, these physics-based approaches have been used in the design phase of the building life cycle or in retrofit analyses. Researchers have begun exploring the benefits of integrating physics-based models with operational data analytics tools, bridging the gap between design and operations. In this paper, we detail the development and operator use of a software tool that uses hybrid data-driven and physics-based approaches to cooling plant FDD and optimization. Specifically, we describe the system architecture, models, and FDD and optimization algorithms; advantages and disadvantages with respect to purely data-driven approaches; and practical implications for scaling and replicating these techniques.more » Finally, we conclude with an evaluation of the future potential for such tools and future research opportunities.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Whisker Labs, Oakland, CA (United States)
  3. Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1374726
Report Number(s):
LBNL-1006282
ir:1006282
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA (United States), 12-26 Aug 2016
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY

Citation Formats

Granderson, Jessica, Bonvini, Marco, Piette, Mary Ann, Page, Janie, Lin, Guanjing, and Hu, R. Lilly. Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?. United States: N. p., 2017. Web.
Granderson, Jessica, Bonvini, Marco, Piette, Mary Ann, Page, Janie, Lin, Guanjing, & Hu, R. Lilly. Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?. United States.
Granderson, Jessica, Bonvini, Marco, Piette, Mary Ann, Page, Janie, Lin, Guanjing, and Hu, R. Lilly. Fri . "Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?". United States.
@article{osti_1374726,
title = {Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?},
author = {Granderson, Jessica and Bonvini, Marco and Piette, Mary Ann and Page, Janie and Lin, Guanjing and Hu, R. Lilly},
abstractNote = {We present that analytics software is increasingly used to improve and maintain operational efficiency in commercial buildings. Energy managers, owners, and operators are using a diversity of commercial offerings often referred to as Energy Information Systems, Fault Detection and Diagnostic (FDD) systems, or more broadly Energy Management and Information Systems, to cost-effectively enable savings on the order of ten to twenty percent. Most of these systems use data from meters and sensors, with rule-based and/or data-driven models to characterize system and building behavior. In contrast, physics-based modeling uses first-principles and engineering models (e.g., efficiency curves) to characterize system and building behavior. Historically, these physics-based approaches have been used in the design phase of the building life cycle or in retrofit analyses. Researchers have begun exploring the benefits of integrating physics-based models with operational data analytics tools, bridging the gap between design and operations. In this paper, we detail the development and operator use of a software tool that uses hybrid data-driven and physics-based approaches to cooling plant FDD and optimization. Specifically, we describe the system architecture, models, and FDD and optimization algorithms; advantages and disadvantages with respect to purely data-driven approaches; and practical implications for scaling and replicating these techniques. Finally, we conclude with an evaluation of the future potential for such tools and future research opportunities.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {8}
}

Conference:
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