• Energy Research

Citizens in dense built environments are susceptible to the simultaneous occurrence of Slow Onset Disaster (SLOD) events, being particularly prone to increasing temperatures and air pollution. Previous research works have assessed these events’ arousal separately and have identified when their intensity is critical. However, few have integrated their analysis, possibly limited by the quality and granularity of available data, the accessibility and distribution of sensors, and measurements not emulating the surroundings of a pedestrian. Thus, this work performed an outdoor meso-scale multi-hazard-based risk analysis to study the aggregated effects of the SLODs mentioned above. The study was carried out to narrow down the time-frames within 2019 in which these two events could have affected citizens’ health the most. A weighted fuzzy logic was applied to superimpose climatic (temperature, humidity, wind speed, and solar irradiance) and air quality (particulate matter, ozone, and ammonium) distress (true risk) on an hourly basis, allocated using set healthy and comfortable ranges for a specific dense urban climate context within Milan (Italy), processing data from Milano via Juvara station. The findings show that sensitive groups were at risk of high temperature and pollution separately during 26% and 29% of summer and mid-season hours, respectively; while multi-hazard risk would arise during 10.93% of summer and mid-season hours, concentrated mainly between 14:00 and 20:00.

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities’ lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.

In most European countries, residential assets account for as much as 85% of the building stock floor area and are, on average, very outdated and energy inefficient. Moreover, the European Commission published the EU Green Deal invigorating higher retrofit of private and public buildings. Nowadays, public authorities collect extensive datasets to analyze the existing building stock; however, the complex and diverse scenario makes the definition of retrofit policies cumbersome. The biggest hurdle is often linked to the high cost of acquiring information. The presented research tries to overcome these issues by introducing a decision support system for retrofit policymaking from low-cost data-driven approaches. The method is based on: i) clustering techniques to divide building assets into groups with similar characteristics and energy consumption, and ii) Montecarlo simulation to compute each cluster's energy savings based on different retrofit scenarios. The proposed method has been successfully applied to an extensive portfolio of residential assets in Lombardy Region in Italy, called the CENED database, with over one million assets. As a result, the introduced method defines the optimum retrofit scenario with a low cost of information (e.g., without expensive surveys to gather data on existing assets' characteristics and performance indicators) and determines the number of assets to be retrofitted along with the expected energy savings. This data-driven approach can be easily updated given new renovations and status changes in the built environment, making it useable for the long term or in different regions. To summarize, data-driven solutions are now required to accomplish the European Union's decarbonization ambitions, and the proposed method helps decision-makers choose better energy retrofit policies for the built environment.

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning.

Abstract The market value of the physical assets over the time varies as a function of different factors related, for instance, to the correct execution of buildings and systems; the conformity with procedures defined for the use phase of the systems; the implementation of maintenance plan, defined in order to maintain the building and its parts in good conditions. Nevertheless, there is not a clear evidence of how the market value is affected by the energy performance measured by the Energy Performance Certification in Italy. The aim of this research concerns the definition of an assessment methodology of the impact of the energy performance on the market value of assets. The methodology has been developed and tested in a case study on the city of Milan, Italy. At first, an investigation of homogeneous income areas has been carried out, afterwards an analysis of the market values of assets has been done. The results have been integrated in a Geographic Information System and gaps in the market values have been associated to the energy performance. Despite the methodology has still to be refined, it has been demonstrated its validity. To conclude, further parameters representing the energy performance of the buildings could be encompassed in the assessment, in order to achieve a more precise energy profile of the building under analysis.

L’implementazione di un processo di gestione, ed eventualmente progettazione della conservazione, basato sul BIM per il patrimonio esistente si scontra con il problema di scomporre il bene costruito in parti costituenti univocamente definite per poter associare queste parti agli oggetti che i software di BIM authoring mettono a disposizione. La moltitudine di soggetti coinvolti, di oggetti da gestire e i differenti approcci al problema creano delle ambiguità nella definizione degli oggetti. In particolare, il cantiere di recupero presenta le seguenti problematiche all’applicazione diffusa degli strumenti informatici nel processo di gestione: mancanza di standardizzazione nell’identificazione degli oggetti della filiera e di standardizzazione nella raccolta, trasmissione e conservazione delle informazioni. La soluzione a questi problemi è quella di utilizzare una struttura gerarchica, su più livelli, che consenta di identificare con precisione l’oggetto di analisi, sia esso un edificio o una sua parte. I sistemi di classificazione di oggetti edilizi sono molti e, pur avendo all’incirca la stessa finalità, coprono ambiti differenti e sono strutturati in maniera spesso difficile da confrontare, non tanto ai livelli dettagliati (componenti, prodotti) quanto a quelli più alti (unità tecnologiche o funzionali). Per questo è necessario visionare più strutture, al fine di riuscire a ottenere una destrutturazione coerente dell’organismo edilizio che risulti di utilità per la definizione degli oggetti BIM e dei relativi attributi informativi, oltre che per la gestione del patrimonio esistente lungo l’intero ciclo di vita. La classificazione da sola non basta, è infatti necessario associare ai vari oggetti edilizi, spaziali ed impiantistici degli attributi informativi che permettano di elaborare le informazioni, aggregandole in modo tale da renderle comprensibili e disponibili a tutti gli attori del processo. Attributi e classificazione vanno infine tarati in base agli obiettivi e agli strumenti di cui si dispone, in modo da non complicare inutilmente le fasi di modellazione e gestione, ma nemmeno di avere il problema opposto: mancanza di informazioni, dati non aggiornati o non coerenti. Si riporta la struttura principale del sistema di classificazione proposto, con alcuni esempi e con una serie di attributi che possono essere associati ai vari oggetti, in aggiunta alla classificazione.

Buildings’ long-term techno-economic performance monitoring is critical for benchmarking in order to reduce costs and environmental impact while providing adequate services. Reliable building stock performance data provide a fundamental knowledge foundation for evidence-based energy efficiency interventions and decarbonisation strategies. Simply put, an adequate understanding of building performance is required to reduce energy consumption, as well as associated costs and emissions. In this framework, Variable-base degree-days-based methods have been widely used for weather normalisation of energy statistics and energy monitoring for Measurement and Verification (M & V) purposes. The base temperature used to calculate degree-days is determined by building thermal characteristics, operation strategies, and occupant behaviour, and thus varies from building to building. In this paper, we develop a variable-base degrees days regression model, typically used for energy monitoring and M & V, using a “proxy” variable, the cost of energy services. The study’s goal is to assess the applicability of this type of model as a screening tool to analyse the impact of efficiency measures, as well as to understand the evolution of performance over time, and we test it on nine public schools in the Northern Italian city of Seregno. While not as accurate as M & V techniques, this regression-based approach can be a low-cost tool for tracking performance over time using cost data typically available in digital format and can work reasonably well with limited resolution, such as monthly data. The modelling methodology is simple, scalable and can be automated further, contributing to long-term techno-economic performance monitoring of building stock in the context of incremental built environment digitalization.