A method for using street view imagery to auto-extract window-to-wall ratios and its relevance for urban-level daylighting and energy simulations

Abstract Urban building energy and daylight modeling are bottom-up, physics-based approaches to simulate the thermal and daylight performance of neighborhoods and cities. The field has flourished in recent years due to a wider accessibility of urban data sets which contain the required information regarding building geometry and program. However, key building-level parameters, most notably window-to-wall ratio (WWR), are generally unavailable at the urban scale and tedious to collect manually. To resolve this challenge, this paper proposes a methodology to automatically extract facade opening layouts for each building adjacent to a Google Street View route. A comparison between auto-generated and manually determined WWRs for 1057 buildings in Manhattan yielded identical results (less than 10% difference) for 66% of all investigated facades. Manual and automated methods were within a 20% error in 90% of all cases. The validated method is applied to daylighting and building energy models of 2014 buildings in downtown Chicago to quantify the impact of building-by-building WWRs versus a uniform, industry-standard WWR of 40% for all buildings. The results reveal that while the total energy use predictions are within 0.2% difference, the total daylit area increases by 9.5% when the WWRs are detected. Furthermore, when individual buildings are ranked in terms of their daylight autonomy or suitability for employing different retrofitting strategies, they are oftentimes misplaced when 40% WWR assumption is used. For example, in the downtown Chicago model, 46 buildings were misclassified as belonging to the top 100 buildings with the greatest percentage-wise savings potential resulting from glazing retrofitting.