Integrating diagnostics and model-based optimization

Integrating diagnostics and model-based optimization

TitleIntegrating diagnostics and model-based optimization
Publication TypeJournal Article
Year of Publication2019
AuthorsJessica Granderson, Guanjing Lin, David Blum, Janie Page, Michael Spears, Mary Ann Piette
JournalEnergy and Buildings
Volume182
Pagination187 - 195
Date Published01/2019
ISSN03787788
KeywordsCentral cooling plant, Energy management and information systems (EMIS), Fault detection and diagnostics (FDD), modelica, optimization, Physics-based modeling
Abstract

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

URLhttps://linkinghub.elsevier.com/retrieve/pii/S0378778818314129
DOI10.1016/j.enbuild.2018.10.015
Short TitleEnergy and Buildings