Abstract
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.
Highlights
The building sector is the most important energy consumer in the European Union.to achieve energy and climate targets for 2030, the improvement of energy performance (EP) of buildings and the reduction of energy consumptions is a fundamental point in European policies [1]
The building energy-use models used in this work are: evaluated and compared using the results of the hourly space heating energy simulation at
Building energy-use models used in this work are: machine alAlevel machine learning model based on the light gradient boosting which makes an estimation of the hourly energy consumption of algoeach
Summary
To achieve energy and climate targets for 2030, the improvement of energy performance (EP) of buildings and the reduction of energy consumptions is a fundamental point in European policies [1]. USEMs give an important contribution in the assessment of energy performance of buildings at urban scale, by analyzing the energy consumption, production and productivity from renewable energy sources [3]. These models can be used to support the urban planning of new and existing neighborhoods, to promote retrofit analysis of building stock, to improve the EP of buildings using smart green technologies, and to design and optimize district energy networks [4,5]
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