An exploratory study on the impact of physical and geospatial characteristics of the urban built environment on the buildings annual electricity usage. (August 2021)

• Record Type: Journal Article
• Authors: Mohammadi, Saleh; de Vries, Bauke; Rafiee, Azarakhsh; Esfandiari, Masoud; Dias, Eduardo
• Is Part Of: Journal of building engineering. Volume 40(2021)
• Publication Date: 2021-08
• Subjects: Electricity demand modelling -- Built environment physical And geospatial characteristics -- Multiple linear regression -- Ordinary least Squares technique; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2021.102359
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively. Highlights: An exploratory investigation of integrating the urban spatial techniques with the statistical bottom-up demand models. Highlighting most important electricity usage explanatory variables including geospatial and physical characteristics. Determination of the annual electricity usage of the residential and non-residential buildings.