Predicting residential electricity consumption using aerial and street view images

Markus Rosenfelder, Moritz Wussow, Gunther Gust, Roger Cremades and Dirk Neumann

Applied Energy, 2021, vol. 301, issue C, No S0306261921008047

Abstract: Reducing the electricity consumption of buildings is an important lever in the global effort to reduce greenhouse gas emissions. However, for privacy and other reasons, there is a lack of data on building electricity consumption. As a consequence, data-driven tools that support decision-makers in this area are scarce. To address this problem, we present an innovative approach to modeling building electricity consumption that relies exclusively on publicly available aerial and street view images. We evaluate our approach in a case study based on real world data from Gainesville, Florida. The results show that our model can predict electricity consumption about as well as conventional models, which are trained on commonly used features that are typically not publicly available at a large scale. Furthermore, our model achieves 68% of the potential accuracy improvements of a model that relies on an extensive set of fine-grained tabular features. Spatially aggregating the predictions from the level of buildings to areas of up to 1km2 further improves the results.

Keywords: Buildings; Electricity consumption; Image recognition; Deep learning; Decision support (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2021.117407

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