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

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

TitleCan We Practically Bring Physics-based Modeling Into Operational Analytics Tools?
Publication TypeJournal Article
Year of Publication2016
AuthorsJessica Granderson, Marco Bonvini, Mary Ann Piette, Janie Page, Guanjing Lin, R. Lily Hu
JournalProceedings of the 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, August 2016
Abstract

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. We conclude with an evaluation of the future potential for such tools and future research opportunities.

LBNL Report Number

LBNL-1006282