Effects of COVID-19 confinement on the simulation of energy needs and uses of residential buildings in Milan

This paper examines the impact of COVID-19 confinement on the simulation of energy needs and uses of residential buildings in Milan. Data-driven schedules for electricity use before and during lockdown, derived from smart metering data, are applied to an urban building energy model to analyze their effects on energy needs for heating and cooling and the energy use for lighting and for other services. Electricity uses, heating and cooling needs, and total primary energy (TOE) are compared for pre-COVID and during-COVID cases. Electricity increases by 8%, while heating decreases by 10%, and cooling increases by 26%. The 5% decrease in TOE is mainly due to the decrease in heating. The study uses heat maps to display the coefficient of variation of root mean square error (CVRMSE) at different temporal and spatial aggregations, indicating significant differences between pre- and during-COVID cases. The CVRMSE for electricity consumption is highest at the hourly level for single buildings, reaching a maximum of 44, and decreases at higher levels of aggregation. The CVRMSE for TOE is highest at the hourly level for single buildings, reaching a maximum of 230. A scenario is created by combining during-COVID and pre-COVID schedules for a hybrid work model, called post-COVID. The post-COVID scenario results indicate a significant impact of remote work on energy consumption patterns

Environmental impact assessment of renewable energy communities: the analysis of an Italian neighbourhood

In recent years, research in renewable energy community (REC) schemes, coupling renewable energy sources and building energy efficiency, is gaining momentum. In this context, Urban Building Energy Modelling tools (UBEMs) have proved to comply with the design requirements of such schemes. However, a clear methodology exploiting UBEMs to support the design of RECs is still missing, especially for assessing the greenhouse gas (GHG) emissions associated with their specific technical configuration. Here, the REC is modelled in “urban modeling interface” (umi), one of the main bottom-up physics-based UBEMs. A building archetype approach is exploited to model the scenarios and assess embodied GHG emissions. The proposed methdology gives the possibility to investigate both the embodied and operational emissions for different REC configuration. A residential neighbourhood in Italy is selected as case study. The results demonstrate the importance of considering building characteristics when analysing emissions reductions in energy-sharing schemes, underlining the necessity of coupling the REC design with energy retrofit interventions

Comparison of different clustering approaches on different databases of smart meter data

Various clustering methods have been applied to determine representative groups of buildings based on their energy use patterns. We reviewed and selected the most commonly used clustering methods, including kmeans, k-medoids, Self-Organizing Map (SOM) coupled with k-means and hierarchical, and our proposed deep clustering algorithm for comparative performance assessment using datasets of smart meters. After the data preparation (data cleaning, segmentation, and normalization), the clustering is run, firstly, letting the number of clusters free to be chosen by the optimization process, and then forcing it to be equal to the number of primary functions of buildings. Depending on the purpose of clustering, e.g., to identify daily 24-hour load shape, to identify primary building use type (e.g., office, residential, school, retail), the optimal number of clustering can vary greatly. Thus, based on the final aim, forcing somehow the number of clusters is the most followed and suggested for engineering purposes. The k-means, the k-medoid, and the hierarchical algorithms show the best results, in all cases. While for the nature of the databases the additional step of adding a SOM to the k-means algorithms does not show improvements in terms of evaluation metrics. The direct comparison of the different algorithms gives a clear overview of the existing main clustering approaches and their performance in capturing typical use patterns in typical smart meter databases. The resulting cluster centroids could be used to better understand and characterize the energy use patterns of different buildings and building typologies with the final aims of benchmarking or customers segmentation

The Emerging Trends of Renewable Energy Communities’ Development in Italy

Increasing concerns over climate change and energy poverty have triggered the transition toward a decentralized energy system through the widespread adoption of renewable energy technologies. Although this transition was led, over past decades, mainly by major investors and large industrial players, citizens and local authorities are increasingly playing an active role in delivering clean energy investments. In particular, the current European Renewable Energy Directive introduced Renewable Energy Communities (RECs), which allow citizens to collectively organize their participation in the energy market, leading to a more distributed renewable energy system and new forms of sustainable, collaborative, and democratic economies. RECs currently under implementation show differences among European countries due to the different national contexts. A literature review exploring the peculiar Italian regulatory framework on RECs and its recent evolution has been carried out to identify available national and regional financial support mechanisms, barriers, and emerging trends in the diffusion of RECs across the country. The paper reviews and describes three main approaches that emerged in the development of RECs in Italy, discussing their strengths, and limitations. In addition, it provides a brief comparison of the regulatory framework in different European countries, highlighting the distinctive features of the Italian experience. Although the development of RECs in Italy involved a combination of both public and private initiatives, the leading role of local authorities as promoters and aggregators of RECs is evident. This role helps preserve the social impact of RECs but might slow down their implementation due to bureaucratic issues often linked to public procedures and procurement processes, as well as the lack of sufficient expertise within local authorities

UBEM's archetypes improvement via data-driven occupant-related schedules randomly distributed and their impact assessment

In Urban Building Energy Models (UBEMs), buildings are usually modelled via archetypes describing occupants’ behaviour via fixed schedules. This research (i) creates data-driven schedules for electric use and occupancy from smart meter readings randomly distributed in the model to improve residential archetypes, (ii) assesses the impact of these schedules on UBEMs’ energy results at different temporal resolutions and spatial scales. The novel assessment procedure exploits integrated heat maps based on coefficients of variation of the root means square error (CVRMSE). The outcomes show that differences in energy needs, with randomized schedules, range based on temporal and spatial aggregation. Yearly, for the entire neighbourhood, heating and cooling energy needs, and electric uses are estimated -2%, +1%, and +18% compared to the base case. The outputs show that, when simulations are focused on the entire district, fixed schedules can be enough to describe energy patterns. However, if the simulation is focused on small groups of buildings (e.g., 5 or fewer), randomising the schedules can create variability in the model in terms of electric use and occupancy among buildings characterized by the same archetype. The followed methodology can be exploited also with larger databases and eventually verified with also other types of data

Light switch behaviour: occupant behaviour stochastic models in office buildings

It is common knowledge that occupants’ behaviour on building control systems plays a significant role to achieve comfortable indoor environmental conditions. Moreover, different research studies have shown how occupants’ behaviour also has a huge influence on energy consumption. Consequently, since the building sector still consumes nearly half of the total amount of energy used in Europe and because occupants’ comfort should be one of the major aim of a building construction, this influential factor should be further investigated. Reliable information concerning occupants’ behaviours in a building could help to better evaluate building energy performances and design robustness, as well as, it could help supporting the development of occupants’ education to energy awareness. Concerning occupant behaviour related to indoor lighting systems, many studies have been made regarding occupants’ feelings and performances to certain visual stimuli due to different light systems. Nevertheless, occupants’ interactions with lighting control systems needs further investigation also because few models to predict switching operations have been implemented in energy simulation programmes. This study proposes probabilistic models to describe occupants’ switching on-off control over lighting. They have been developed using a multivariate logistic regression based on measurements of indoor climate parameters, outdoor environmental conditions and artificial lights “switch on/off” actions. Measurements were made over eleven months for three different office rooms. Two predictive light-switch behaviour models were inferred in relation to the number of actions carried out by the users (active or passive). The models are presented and critically discussed in this paper. The study extends the information on environmental parameters influencing occupants’ manual control of the lighting system in offices and energy consumption

From nearly zero energy to carbon‐neutral: Case study of a hospitality building

In recent years, many cities around the world have pledged to upgrade their building stocks to carbon‐neutral. However, the literature does not yet provide a shared definition of carbon-neutral building (CNB), and the assessment objectives and methodological approaches are vague and fragmented. Starting from the available standards and scientific literature on life cycle assessment (LCA), this paper advances an operational definition for CNB on the basis of an explicit calculation approach. It then applies the definition to an urban case study, comparing it against a state-of‐the‐art nearly Zero Energy Building (nZEB) scenario, with the intent of highlighting the major practical limitations connected to the application of a methodologically sound carbon neutrality cal-culation. The case study shows that carbon neutral objectives can hardly be achieved by single urban buildings because of the lack of spaces that can provide onsite carbon offsetting actions. Carbon neutrality may be better approached at the city, regional, or national scales, where overarching policies may be defined

Solar Technologies as a Driver to Limit Energy Poverty in the Rocinha Favela

In developing countries, the constant conditions of economic and social crises resulted in a continuous expansion of non-regulated solutions to access energy services, especially in low-income settlements of urban areas such as Brazilian favelas, where people rely on illegal connections to the power grid, called gato, to fulfil their energy needs. An appropriate exploitation of renewable energy could reduce these energy thefts, contributing to urban sustainability and creating employment opportunities for locals. This section presents the results of a study developed for a pilot area within the favela of Rocinha, meant to establish ways to limit energy poverty, by spreading access to renewable energy. Both the favela’s energy uses, the potential of local climate and of photovoltaic (PV) panel’s production, have been assessed, leading to the proposal of a solar district based on the use of PV and of battery storage systems. In addition to it, the deployment of an urban management system (UMS), able to manage data coming from different urban facilities, will contribute to outline a new sustainable culture on the use of energy and urban services through the active participation of consumers

Coloured BIPV technologies: Methodological and experimental assessment for architecturally sensitive areas

Energy flexibility in buildings is gaining momentum with the introduction of new European directives that enable buildings to manage their own energy demand and production, by storing, consuming or selling electricity according to their need. The transition towards a low-carbon energy system, through the promotion of on-site energy production and enhancement of self-consumption, can be supported by building-integrated photovoltaics (BIPV) technologies. This paper investigates the aesthetic and technological integration of hidden coloured PV modules in architecturally sensitive areas that seem to be the best possibility to favour a balance between conservation and energy issues. First, a multidisciplinary methodology for evaluating the aesthetic and technical integration of PV systems in architecturally sensitive area is proposed, referring to the technologies available on the market. Second, the experimental characterisation of the technical performance specific BIPV modules and their comparison with standard modules under standard weather condition are analysed, with the aim of acquiring useful data for comparing the modules' integration properties and performance. For this purpose, new testbeds have been set up to investigate the aesthetic integration and the energy performances of innovative BIPV products. The paper describes the analyses carried out to define the final configuration of these experimental testbeds. Finally, the experimental characterisation at standard test conditions of two coloured BIPV modules is presented and the experimental design for the outdoor testing is outlined