Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam

Abstract

Abstract Energy retrofit of buildings represents an important sector for mobilizing investments to address carbon mitigation of cities. The identification of the actual energy consumption profile of large building stocks is a necessary step to evaluate the impact of retrofit measures, e.g. energy savings, at city scale. The present study introduces a bottom-up statistical methodology based on a Geographical Information System (GIS) to estimate the energy consumption of residential stocks across an entire city. The adoption of a multiple linear regression model allows the downscaling of measured natural gas and electricity consumption from the aggregated post-code level to single dwellings, based on several descriptors, such as dwelling type, period of construction, floor surface and number of occupants. The energy consumption is apportioned to different end-uses and corrected for weather, then the energy savings potential is estimated by accounting for the implementation of typical refurbishment measures. Results are finally aggregated across the whole city for evidence-based decision support in sustainable urban planning. The study provided relevant results to prioritize the implementation of energy retrofit measures for the residential stock of Rotterdam city, consisting of about 300,000 dwellings. The methodology can be further applied to other contexts due to its generic nature.