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

Abstract

The use of simulation to evaluate energy-efficient operations, commissioning problems, and demandresponse (DR) strategies offers important insights into building operations. This paper describes a stepby-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.

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

“…In particular, the detailed HVAC load models in EnergyPlus can be burdensome to re-run when a reassessment of DR potential is needed. Although HVAC's electrical load is dynamic and sensitive to weather conditions, occupancy, and other factors, previous research and field demonstration have found that HVAC demand and DR potential tend correlate positively with outside air temperature until the system limits are reached [28,30,49]. Motivated by this, the proposed model of DR potential of TCLs uses outside air temperature as the predictor.…”

“…Software tools such as EnergyPlus, DOE-2, ESP-r, and TRNSYS, are used to create physical models for building energy simulation. A number of studies demonstrated the use of physical models for predicting DR performance of various control strategies in both commercial and residential buildings [27,28,29,30,31]. Overall, physics based energy models, such as whole-building EnergyPlus models, capture sufficient details to simulate the effect of DR strategies such as thermostat setpoint or fan speed adjustment in individual buildings.…”

Building simulation: Ten challenges

“…As the first challenge section described, previous studies have indicated significant discrepancies between simulated energy use from building energy models and actual measured data (Balaras et al 2016;Yoshino et al 2017;Yin et al 2014;Karlsson et al 2007;La Fleur et al 2017;Maile et al 2012). Again, this undermines confidence in model predictions and curtails adoption of building energy performance tools during design, commissioning, operation (Samuelson et al 2016;Coakley et al 2014) and retrofit (Johnson 2017;Heo et al 2012).…”

Integration of Demand Response and Photovoltaic Resources in Residential Segments

“…An interesting way to evaluate this response is through PBLM, a methodology first proposed by Ihara and Schweppe to solve the so called "cold load pickup" problem [46]. The proposed approach in this section follows the same idea proposed in thermal design software tools such as eQuest and EnergyPlus [47,48]: a white-box model. These approaches sometimes are too complex to evaluate load response in the short term (e.g., EnergyPlus works with high order state-space models, e.g., the model order is approximately 30-40) and they need some simplification to make costs affordable and efforts feasible to engage DR.…”