We propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock using OpenStreetMap data, and (ii) estimating the energy use of buildings individually with a back-end model.
For mitigating climate change, it is crucial to minimize energy use in buildings while maintaining decent wellbeing (1). Buildings contribute more than a quarter of the global energy-related emissions (2). On the one hand, buildings are among the lowest-hanging fruit for mitigation—with technological solutions available for energy-neutral or even energy-positive buildings. On the other hand, deep decarbonization is challenging because of the heterogeneity in the building stock (2). Moreover, achieving decent well-being conditions for everyone could increase the energy consumption of buildings. For instance, in countries like India where global warming is likely is intensify the deadly effects of heatwaves, air-conditioning is considered an important adaptation strategy. Currently, building energy demand, e.g. for heating and cooling, is mostly estimated for individual buildings with data-intensive models, or with fudge factors in highly aggregated models globally. For rapid, wide-spread mitigation, such methods are not sufficient either to develop solutions that can be transferred across urban areas, nor to develop tailored solutions based on local data everywhere (3). This situation confines impactful climate action to a limited group of cities. There are entire regions with pressing mitigation and development issues that are left behind, in particular rapidly-urbanizing urban areas in the global South (4). The rise of big data and artificial intelligence offer new opportunities to model building energy demand for urban areas, and can even take into account geographical diversity (3). Such data could inform municipal policymakers on spatially stratified but city-wide strategies for climate change mitigation in buildings. However, large-scale spatially-explicit models that compute the minimum energy demand satisfying essential thermal comfort in buildings are still missing. Here, we propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock, and (ii) estimating the energy use of buildings individually with a back-end model. In a first step, we use a machine learning model to learn and predict buildings heights from OpenStreetMap and ground truth LiDAR data. The latent energy use of a building can be approximated by its shape and its height (5). However, the height attribute is sparsely populated in OSM. Previous research (6) has demonstrated that building heights can be predicted from features describing buildings and their surroundings. We compare several machine learning architectures and input features spaces. In particular, we are developing a hybrid convolutional neural network based on OSM raster data and scalar features (7). Such architecture enables to take full advantage of both the spatial and the higher-level information available in OSM. We will test how well the model transfers to new cities, and how to improve generalization. In a second step, a back-end model computes the latent, minimal energy demand of each building for heating and cooling. The model accounts for simplified building characteristics, local climatological conditions and ancillary factors. Climatological conditions strongly influence building energy use. The latent energy demand is a simple metric that does not account for occupancy patterns or appliance use. However, this metric provides a lower bound for the energy necessary for thermal well-being, and it is simple enough so that it can be computed for any building. Preliminary results indicate that on average the energy demand in the studied areas can be reduced by a large amount while maintaining decent thermal comfort. However, strategies for energy demand reduction depend on building stock vintage, cultural standards, and the local climate. Our framework provides new insights from OpenStreetMap for more detailed and globally consistent analyses of mitigation strategies in cities. 1. N. D. Rao, P. Baer, “Decent Living” Emissions: A Conceptual Framework. Sustainability (2012) 2. O. Lucon et al., in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)] (2014) 3. F. Creutzig et al., Upscaling urban data science for global climate solutions. Global Sustainability (2019) 4. F. Creutzig et al., Urban infrastructure choices structure climate solutions. Nature Climate Change (2016) 5. P. Depecker, C. Menezo, J. Virgone, S. Lepers, Design of buildings shape and energetic consumption. Building and Environment (2001) 6. F. Biljecki, H. Ledoux, J. Stoter, Generating 3D city models without elevation data. Computers, Environment and Urban Systems (2017) 7. K. Tang, M. Paluri, L. Fei-Fei, R. Fergus, L. Bourdev, IEEE CPVR (2015)
Speakers: Milojevic-Dupont Nikola Steffen Lohrey