Building Energy Models with Morphological Urban-Scale Parameters: A Case Study in Turin

With a growing awareness around the importance of the optimization of building efficiency, being ...able to make accurate predictions of building energy demand is an invalu¬able asset for practitioners and designers. For this reason, it is important to continually improve existing models as well as introduce new methods that can help reduce the so-called energy performance gap, which separates pre¬dicted from actual consumption values. This is particularly true for urban scale simulations, where even small scenes can be very complex and carry the necessity of finding a reasonable balance between precision and computational efforts. The scope of this work is to present two different models that make use of morphological urban-scale pa¬rameters to improve their performances, taking into account the interactions between buildings and their surroundings. In order to do this, two neigh¬bourhoods in the city of Turin (IT) were taken as case stud¬ies. The buildings studied present similar characteristics but are inserted in a different urban context. Several urban pa-rameters were extracted using a GIS tool and used as input, alongside the building-scale features, for two different mod-els: i) a bottom-up engineering approach that evaluates the energy balance of residential buildings and introduc¬es some variables at block-of-buildings scale, ii) a ma¬chine learning approach based on the bootstrap aggregat¬ing (bagging) algorithm, which takes the same parameters used by the previous model as inputs and makes an es¬timation of the hourly energy consumption of each build¬ing. The main results obtained confirm that the urban context strongly influences the energy performance of buildings located in high built-up areas, and that intro¬ducing simple morphological urban-scale parameters in the models to take these effects into account can improve their performance while having a very low impact on the computational efforts.