An eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters which have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in eQUEST. The model is calibrated using Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by the American Society of Heating, Refrigeration, and Air-Conditioning (ASHRAE) Guideline 14, Federal Energy Management Program (FEMP), and International Performance Measurement and Verification Protocol (IPMVP) for building energy model calibration. The values of the parameters are varied in two levels, and then the percentage change in output is calculated. Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP. For Building A, top 3 parameters from percentage change method are: Heating setpoint, cooling setpoint and server room. From fractional factorial design, top 3 parameters are: heating setpoint (p-value= 0.00129), cooling setpoint (p-value= 0.00133), and setback control (p-value= 0.00317). For Building B, top 3 parameters from both methods are: Server room (p-value= 0.0000), heating setpoint (p-value= 0.00014), and cooling setpoint (p-value= 0.00035). If the best values for all top three parameters are taken simultaneously, energy efficiency improves by 29% for Building A and 35 % for Building B

Distributed Energy Feedback Control for Demand Response Operations in Commercial HVAC Systems

The goal of this thesis is to identify methods to control a commercial Heating, Ventilation, and Air Conditioning (HVAC) system that results in precise control of the cooling coil load and the ability to prioritize the comfort of certain zones. The state of the art method of zone temperature setpoint adjustments is introduced and the weaknesses are identified. Temporarily resetting the zone temperature setpoints shows the capacity to significantly reduce the cooling coil load, but the load is not controlled precisely. The setpoint reset method is compared through simulation and experimentation to the proposed solutions of Energy Demand Response control (EDR) and Transactive Control and Coordination (TCC) integrated with EDR control. A model of the system that captures the relevant behaviors was developed to test each of the control strategies under different scenarios. Through simulation it was found that EDR control can precisely regulate the cooling coil load but does not provide any way to prioritize the zones. EDR combined with TCC precisely controls the cooling coil load and allows for the comfort of specific zones to be prioritized. The setpoint reset and EDR control strategies were validated experimentally on a multizone variable air volume air handling unit. The cooling coil load during a period of setpoint reset was experimentally found to vary between 1.1±0.4 refrigeration tons. The cooling coil load under EDR control was regulated between 1.1±0.1 refrigeration tons. Future areas of study have been identified, which includes using a virtual flow meter for EDR control and increasing the scale of EDR+TCC implementation