Spatio-temporal heterogeneity analysis of energy use in residential buildings

Abstract

Conventional building energy dynamic analysis relies on climatic and geographical statistics of buildings. A large number of studies focus on investigating the major influencing factors and the energy use with engineering methods and statistical methods to predict buildings' energy use. However, due to the lack of temporal statistics when analyzing buildings' energy use intensity (EUI), very few studies concerned the energy consumption from spatial and temporal heterogeneity simultaneously. This study extends the traditional regression model by introducing the temporal variations into the geographically weighted regression (GWR) model and developed a geographically and temporally weighted regression (GTWR) model. A case study, which is based on four years’ urban building energy consumption (from 2015 to 2018) in the city of Seattle, is used to validate the performance of the proposed regression model. Meanwhile, the ordinary least square (OLS), GWR, and temporally weighted regression (TWR) models are developed for making cross-comparison of the goodness-of-fit. The results show the GTWR and TWR models reduce the mean absolute error by 13% and 7%, respectively, relative to the GWR model. Moreover, the GTWR model has a substantial improvement of goodness-of-fit, which can be referred from the value of R2 increasing by 49% in OLS, 16% in GWR, and 6% in TWR. Two driving forces, the number of floors (NF) and energy stars (ES), are identified to have the most significant coefficients in evaluating the site EUI. The novelty of this research lies in achieving better reliability and accuracy in building energy modeling by GTWR, which provides an assisting tool for government and local authorities to make the master plan and sustainability strategies for the new buildings in the city.