Predicting residential electricity consumption using aerial and street view images

Lecturer: Prof. Dr. Dirk Neumann / Chairholder for Information Systems Research at the Albert-Ludwigs-University of Freiburg

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 relying 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. We present a live demo of our software implementation of this algorithm.

Registration and zoom link for GERICS Colloquium: "Predicting residential electricity consumption using aerial and street view images", October 13, 4 pm