Development and application of a procedure to estimate overall building and ventilation parameters from monitored commercial building energy use

Abstract

This thesis proposes and validates a simplified model appropriate for parameter identification and evaluates several different inverse parameter identification schemes suitable for use when heating and cooling data from a commercial building are available. The validation has been performed using such data generated from a detailed building simulation program for different building geometries and building mass levels in two different climatic locations. Such a synthetic evaluation will validate the model used as well as determine the best parameter identification scheme, i.e., one likely to yield the most accurate set of parameter estimates. A multistep identification scheme has been found to yield very accurate results, and a more careful evaluation has been performed in order to evaluate its accuracy and stability with synthetic data against the effects of solar energy, HVAC system operation, internal load schedule, building then-thermal mass and geometry, and climatic location. This method is also evaluated using data from different time periods and when utility bill data (i.e. monthly data) only is available. The model is then applied to energy use data from two buildings being monitored under the Texas LoanSTAR Program, which are in different locations and have different HVAC systems. With parameters thus determined, two energy use indices, Energy Delivery Efficiency (EDE) and Multizone Efficiency Index (MEI), are calculated to present some insights into the benefits of retrofit from a constant volume (CV) to a variable air volume (VAV) system and of continuous commissioning (CC) work done to these two buildings, respectively. Uses and limitations of EDE and MEI are also discussed. Based on these findings, it is suggested that the multistep regression approach is an accurate and practical building physical parameter determination method, and the combined use of the EDE and MEI indices calculated from these parameters can provide insights into the HVAC system, and the potential for optimizing its operation.