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.

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.

With the increasing interest in District Heating Networks (DHNs) as a potential solution to decarbonize heating, new simulation tools are being developed, raising the need for standardized benchmarks to validate their performance. Currently, the main benchmark used for DHN simulation models is the DESTEST, which consists in an inter-model comparison on the simulation of a toy radial network. However, no common benchmarks based on monitoring data from a meshed network exist at the moment, which would be needed to complement the DESTEST. To address this issue, this paper presents aggregated monitoring data from a medium-sized meshed DHN and proposes a benchmark based on this data. While aggregating the data and assuming steady-state conditions is not a suitable strategy for representing locally high dynamic behaviours, applying the benchmark to an existing simulation tool showed that the simulation results are coherent with the published monitoring data, as a low difference in temperature across most available sensors is found. The published data and the proposed benchmark aim to encourage the development of more accurate models for DHNs and to facilitate the evaluation of the performance of different simulation tools and enable their optimization, which will ultimately lead to more efficient and reliable DHNs.