Building energy performance estimation approach: facade visual information-driven benchmark performance model

2019-04-21In the U.S., building sectors account for a large proportion of the national energy consumption. With more and more attention on urban sustainability, large-scale building energy master plan with comprehensive energy reduction strategies are essential today in meeting the energy reduction goal. However, traditional energy predictions, as a very complicated and time-consuming process, require multiple details and information about a building when preparing for energy modeling. The goal of the research is to provide stakeholders with a simplified but reliable energy performance benchmark model to assess the existing building performance at urban level, while motivating the establishment of a performance goal. In this research, how building facade information, as well as climatic characteristics could affect building energy performance were investigated. Compared with these easily accessible façade features, parameters including envelope thermal properties, internal systems, and operating schedules are regulated by current codes and regulations, based on different building functionalities and activities. Such façade parameters are variables that have large potentials in affecting building energy performance. These façade features including but not limited to building floor space, height, aspect ratio, and window to wall ratio were extracted as the independent variables to predict the building site energy use intensity by different data mining methods. Other key determinants of building energy performance including the building vintage and geographic region, and building type were also selected. Principal component analysis, multivariable regression, decision tree and artificial neural network are data mining techniques adopted in this research. By comparing and evaluating model output from different data mining techniques, the building function/type was found to be one of the most significant factors which largely affects the building EUI. Considering its comprehensive interpretation of variable variance and better predictive ability, the monthly EUI classification model derived from decision tree was selected as the best fitted EUI estimation model, with the accountability and accuracy rate around 80%. It was proved that it is capable and feasible to use the building façade visual information as the building key performance indicator, for estimating the building energy use, which is a fast and straightforward way to predict the energy use at urban scale. Incorporating a transformative building energy performance estimation approach may enable stakeholders to easily assess their existing building energy consumption, establish urban energy reduction goals and propose a viable integrated energy master plan