• Energy Research

According to the Global Status report for Buildings and Construction, the building and construction sector accounts for 36% of global energy consumption and 39% of energy-related carbon dioxide (CO2) emissions. Specifically, the construction site represents one of the most significant sources of environmental impact, making it a pivotal element in achieving sustainability within the construction industry. The construction process and maintenance activities on buildings are, therefore, critical phases in which the construction industry is tasked with finding a balance between economic development, social well-being, and environmental protection to ensure a sustainable future for both current and future generations. To promote a construction model focused on environmental, economic, and social sustainability, this paper introduces a Performance Protocol. This protocol serves as an operational tool that allows both the construction company and the contracting authority to manage these aspects throughout the entire construction process. Digital technologies such as BIM and Digital Twin can take advantage of such model to integrate and develop sustainability analysis and simulation during the entire life cycle of a building. The use of digital tools is one of the challenges for the future of the built environment that needs to address not only the issues related to costs and management but also considering the social and environmental aspects of sustainable development.

According to the Global Status report for Buildings and Construction, the building and construction sector accounts for 36% of global energy consumption and 39% of energy-related carbon dioxide (CO2) emissions. Specifically, the construction site represents one of the most significant sources of environmental impact, making it a pivotal element in achieving sustainability within the construction industry. The construction process and maintenance activities on buildings are, therefore, critical phases in which the construction industry is tasked with finding a balance between economic development, social well-being, and environmental protection to ensure a sustainable future for both current and future generations. To promote a construction model focused on environmental, economic, and social sustainability, this paper introduces a Performance Protocol. This protocol serves as an operational tool that allows both the construction company and the contracting authority to manage these aspects throughout the entire construction process. Digital technologies such as BIM and Digital Twin can take advantage of such model to integrate and develop sustainability analysis and simulation during the entire life cycle of a building. The use of digital tools is one of the challenges for the future of the built environment that needs to address not only the issues related to costs and management but also considering the social and environmental aspects of sustainable development.

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

An optimized system management of a power system must take care of the actual operational and energetic duty of the power system distribution, promoting a high efficiency of power cables and avoiding their excessive gap on load and gap on lifetime. A procedure based on the Arrhenius model of the Thermal Aging of Insulating Materials allows implementing a tool to calculate the life loss and the residual useful life for insulated power cables. The paper deals with simplified models to evaluate the power cable temperature behavior considering the load current variable in the time. The life loss analysis can be performed both “off-line”, based on the available circuit parameters and measurements, or “on-line” by appropriate control systems. Furthermore, the paper proposes a specific device to count the “life loss hours”. The employment of such tools supports the system operators to monitor the cables life loss in the effective installation conditions, to optimize their operation and maintenance and, possibly, to upgrade the efficiency of the distribution system.

An optimized system management of a power system must take care of the actual operational and energetic duty of the power system distribution, promoting a high efficiency of power cables and avoiding their excessive gap on load and gap on lifetime. A procedure based on the Arrhenius model of the Thermal Aging of Insulating Materials allows implementing a tool to calculate the life loss and the residual useful life for insulated power cables. The paper deals with simplified models to evaluate the power cable temperature behavior considering the load current variable in the time. The life loss analysis can be performed both “off-line”, based on the available circuit parameters and measurements, or “on-line” by appropriate control systems. Furthermore, the paper proposes a specific device to count the “life loss hours”. The employment of such tools supports the system operators to monitor the cables life loss in the effective installation conditions, to optimize their operation and maintenance and, possibly, to upgrade the efficiency of the distribution system.

The growing sensibility on energy consumption has focused people interest in energy optimization technologies. Non-intrusive load monitoring system (NILMS) are a cheap solution that require minimal equipment and installation time. The aim of the research is to develop a load analyzing platform for electrical devices to obtain information on their functionality and to display the costumer advantage to change their energy class. It monitors malfunctions or non-critical failures in real-time. It also counts the device operative life-time. The architecture is made of a local analysis electrical plug, which get the load information, and a central unit which elaborates data. The hardware implements a hall-effect ammeter, a power transformer and a micro controller (AT-mega328). The central unit is a small integrated computer (Raspberry). The communication between the devices utilize a low-range radio transmitter.

The growing sensibility on energy consumption has focused people interest in energy optimization technologies. Non-intrusive load monitoring system (NILMS) are a cheap solution that require minimal equipment and installation time. The aim of the research is to develop a load analyzing platform for electrical devices to obtain information on their functionality and to display the costumer advantage to change their energy class. It monitors malfunctions or non-critical failures in real-time. It also counts the device operative life-time. The architecture is made of a local analysis electrical plug, which get the load information, and a central unit which elaborates data. The hardware implements a hall-effect ammeter, a power transformer and a micro controller (AT-mega328). The central unit is a small integrated computer (Raspberry). The communication between the devices utilize a low-range radio transmitter.