Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

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

Obstructive sleep apnea syndrome and cognitive impairment: effects of CPAP

Obstructive Sleep Apnea Syndrome (OSAS) is a sleep disorder characterised by repetitive episodes of upper airway obstruction (apnea) or reduced airflow (hypopnoea) despite persistent respiratory effort. Apnea is defined as the cessation of breathing for at least 10 seconds during sleep, while hypopnoea is defined as at least 30% reduction in airflow for 10 seconds associated with oxygen desaturation and sleep fragmentation. The presence in the general population is about 4%. The principal symptoms are: excessive daytime sleepiness (EDS), snoring, dry throat, morning headache, night sweats, gastro-esophageal reflux, and increased blood pressure.Long term complications can be: increased cardio-cerebrovascular risk and cognitive impairment such as deficiency in attention, vigilance, visual abilities, thought, speech, perception and short term memory.Continuous Positive Airway Pressure (CPAP) is currently the best non-invasive therapy for OSAS.CPAP guarantees the opening of upper airways using pulmonary reflexive mechanisms increasing lung volume during exhalation and resistance reduction, decreasing electromyografical muscular activity around airways.The causes of cognitive impairments and their possible reversibility after CPAP treatment have been analysed in numerous studies. The findings, albeit controversial, show that memory, attention and executive functions are the most compromised cognitive functions.The necessity of increasing the patient compliance with ventilotherapy is evident, in order to prevent cognitive deterioration and, when possible, rehabilitate the compromised functions, a difficult task for executive functions

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

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior

Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID-19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GIS-based approach, based on pre-pandemic, partial, and full lockdown scenarios, using a bottom-up engineering model to quantify these impacts. The model has been verified against measured energy data from a total number of 451 buildings in three urban neighborhoods in the Canton of Geneva, Switzerland. The accuracy of the engineering model in predicting the energy demand has been improved by 10%, in terms of the mean absolute percentage error, as a result of adopting a data-driven correction with a random forest algorithm. The obtained results show that the energy demand for space heating and cooling tended to increase by 8% and 17%, respectively, during the partial lockdown, while these numbers rose to 13% and 28% in the case of the full lockdown. The study also reveals that the introduced detailed occupancy scenarios are the key to improving the accuracy of urban building energy models (UBEMs). Finally, it is shown that the proposed GIS-based approach can be used to mitigate the expected impacts of any possible future pandemic in urban neighborhoods