The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

The present paper deals with an infrastructure digitization policy to optimize maintenance processes and energy efficiency to transform port areas to ZED (Zero Energy District). The Lazio Region started the process for all its ports in 2020. The Anzio port started and developed as a pilot project as it is a particularly representative sample for the Mediterranean Sea reality due to its geomorphological conformation. The study aimed to develop energy-saving procedures and strategies and integrate production systems from Renewable Energy Systems (RESs) for sustainable mobility. In the article, these strategies are described in detail and energy analysis is carried out, starting from the current state and demonstrating the potential energy self-sufficiency of the infrastructure. Finally, the investigation’s potential utilizing a Digital Twin (DT) of the area is highlighted. Furthermore, the BIM (Building Information Modeling) and GIS (Geographic Information System) combining possibility to maximize the energy efficiency measures beneficial impact are discussed.

Among the several typologies of storage technologies, mainly on different physical principles (mechanical, electrical and chemical), hydrogen produced by power to gas (P2G) from renewable energy sources complies with chemical storage principle and is based on the conversion of electrical energy into chemical energy by means of the electrolysis of water which does not produce any toxic or climate-relevant emission. This paper aims to pinpoint the potential uses of renewable hydrogen storage systems in Europe, analysing current and potential locations, regulatory framework, governments’ outlooks, economic issues, and available renewable energy amounts. The expert opinion survey, already used in many research articles on different topics including energy, has been selected as an effective method to produce realistic results. The obtained results highlight strategies and actions to optimize the storage of hydrogen produced by renewables to face varying electricity demand and generation-driven fluctuations reducing the negative effects of the increasing share of renewables in the energy mix of European Countries.

Improving energy efficiency in public buildings is one of the main challenges for a sustainable requalification of energy issues and a consequent reduction of greenhouse gas (GHG) emissions. This paper aims to provide preliminary information about economic costs and energy consumption reductions (benefits) of some considered interventions in existing public buildings. Methods include an analysis of some feasible interventions in four selected public buildings. Energy efficiency improvements have been assessed for each feasible intervention. The difference of the building global energy performance index (EP_gl) has been assessed before and after each intervention. Economic costs of each intervention have been estimated by averaging the amount demanded by different companies for the same intervention. Results obtained show economic costs and the EP_gl percentage improvement for each intervention, highlighting and allowing for the comparison of energy consumption reduction and relative economic costs. The research results come from data gathered from four public buildings, and as such they could not be used to generically identify cost-beneficial energy efficiency interventions for every context or building type. However, the data reveals useful cost based considerations for selecting energy efficiency interventions in other public buildings.

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.