Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation

Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation

TitleLinking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation
Publication TypeReport
Year of Publication2014
AuthorsYin, Rongxin, Sila Kiliccote, and Mary Ann Piette
Date Published12/2014
Keywordsmodel calibration
Abstract

The use of simulation to evaluate energy-efficient operations, commissioning problems, and demand-response (DR) strategies offers important insights into building operations. This paper describes a step-by-step procedure for using measured end-use energy data from a campus building to calibrate a simulation model developed in EnergyPlus. This process included identification of key input parameters for reducing uncertainties in the model. The building geometry and internal thermal zones were modeled to match the actual heating ventilation and air conditioning (HVAC) zoning for each individual variable air-volume (VAV) zone. We evaluated most key building and HVAC system components, including space loads (actual occupancy number, lighting and plug loads), HVAC air-side components (VAV terminals, supply and return fans) and water-side components (chillers, pumps, and cooling towers). Comparison of the pre- and post-calibration model shows that the calibration process greatly improves the model’s accuracy for each end use. We propose an automated model calibration procedure that links the model to a real-time data monitoring system, allowing the model to be updated any time. The approach enables the automated data feed from sMAP into the EnergyPlus model to create realistic schedules of space loads (occupancy, lighting and plug), performance curves of fans, chillers and cooling towers. We also field-tested DR control strategies to evaluate the model’s performance in predicting dynamic response effects. Finally, this paper describes application of the calibrated model to analyze control systems and DR strategies with the goal of reducing peak demand. We compare end-use data from modeled and actual DR events.

LBNL Report Number

LBNL-7006E