The commercial and residential building sector accounts for 39% of carbon dioxide (CO2) emissions in the United States per year, more than any other sector. The most significant factor contributing to CO2 emissions from buildings is their use of electricity. Commercial and residential buildings are tremendous users of electricity, accounting for more than 72% of electricity use in the U.S. Energy use data from CBECS is an average value based on the range of Heating Degree Days (HDD) and Cooling Degree Days (CDD), which can’t show the specific condition of each building category within one area. In addition, the average value is too general to evaluate if a specific building case is energy efficient or not. On the other hand, it is very time consuming to develop a simulation model in software, which also needs very detailed information about the building itself. The accuracy depends on how much specific information of envelop thermal conditions, mechanical system performance, occupancy level and schedule, etc.
Among 3 main factors to influence building energy performance, building façade features are easy to be obtained rather than building system and schedule information. By using façade features, certain key attributes could be input to generate a customized baseline model and to estimate building energy use intensity (EUI). A simple regression model can be used to calculate the EUI baseline instead of complicated simulation tools, and the results are accurate and reasonable at an acceptable level. The calculated baseline can be used for setting a practical baseline for energy reduction target. Due to its simplicity and quick processing time, the research outcome would also be applicable to the real-time energy estimation of multiple buildings at an urban scale.
This new method of linear regression analysis is developed to estimate building energy consumption just based on simple façade attributes and weather conditions. Building façade features, for example, including shading, window-to-wall ratio, orientation, surface-to-volume ratio, etc. are easy to obtain. It is meaningful to use a simple way to predict heating and cooling energy use instead of traditional energy performance simulation tool which is time and resource consuming. Based on collected building physical attribute data, statistical methods could be used to generate a customized baseline Energy Use Intensity (EUI) estimation model. The proposed idea will also adopt a simplified building energy performance prediction model as a function of architectural physical frames and their dynamic ambient environmental condition, such as monthly cooling/heating degree days. The main goal of this research is to develop a mathematical method to provide a customized baseline model for buildings, considering specific façade features and local climate condition. It will provide a direct estimation and prediction of project building energy performance to provide a reasonable baseline for designer, engineer and client.