Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

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Aggregated building energy demand is a useful indicator for urban energy planning. It can be used by planners and decision-makers to identify clusters of high energy demand in a given urban area and efficiently plan, for example, district heating networks. Various data sources exist at the pan-European level describing land use and built areas. Combined with statistical data, such maps have been used in previous research for estimating building energy performance aggregated at the hectare level, using engineering assumptions. In this paper, we show that large-scale land-use maps alone can be used for predicting annual building energy demand with an accuracy comparable to the one of previous engineering models. We hence present a preliminary method based on Convolutional Neural Networks at different spatial resolutions. The resulting model was trained and tested in an area of about 170 km1 in Geneva (Switzerland) using a local annual heating demand dataset comprising 16239 buildings. On a 300-m aggregation tile, the obtained mean error (14.3%) is significantly reduced compared to the one of a simple linear model (37.2%). Using solely land-use data, we also achieve similar results for a 100-m tile as those of an engineering model from the literature.

Buildings account for the highest share of primary energy usage and greenhouse gas emission in the E.U. and U.S. [1], and most of this energy is used for space and water heating. Being able to gain a broader understanding of the gap between predicted and in situ measured thermal performance of buildings may, in a lot of cases, help reducing the energy consumption and, therefore, alleviating our pressure on the environment [2]. The aim of this research is to further investigate this performance gap and to evaluate the possibility of using machine learning algorithms to effectively predict the energy demand of buildings. For this purpose, a group of residential buildings in the city of Turin, Italy, is taken as case study: an estimation of their yearly heating demand is made using different machine learning algorithms, and their results are evaluated and discussed. The research showed that the use of machine learning resulted in a performance gap in line, if not lower, with the current literature. The reasons for this outcome, as well as possible future research directions are finally discussed.