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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.
Valeria Todeschi; Roberto Boghetti; Jérôme Kämpf; Guglielmina Mutani. Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland. Sustainability 2021, 13, 1595 .
AMA StyleValeria Todeschi, Roberto Boghetti, Jérôme Kämpf, Guglielmina Mutani. Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland. Sustainability. 2021; 13 (4):1595.
Chicago/Turabian StyleValeria Todeschi; Roberto Boghetti; Jérôme Kämpf; Guglielmina Mutani. 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland." Sustainability 13, no. 4: 1595.
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.
Roberto Boghetti; Fabio Fantozzi; Jérôme H Kämpf; Giacomo Salvadori. Understanding the performance gap: a machine learning approach on residential buildings in Turin, Italy. Journal of Physics: Conference Series 2019, 1343, 012042 .
AMA StyleRoberto Boghetti, Fabio Fantozzi, Jérôme H Kämpf, Giacomo Salvadori. Understanding the performance gap: a machine learning approach on residential buildings in Turin, Italy. Journal of Physics: Conference Series. 2019; 1343 (1):012042.
Chicago/Turabian StyleRoberto Boghetti; Fabio Fantozzi; Jérôme H Kämpf; Giacomo Salvadori. 2019. "Understanding the performance gap: a machine learning approach on residential buildings in Turin, Italy." Journal of Physics: Conference Series 1343, no. 1: 012042.