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In this work, we present a novel distributed software platform for patients with neurodegenerative diseases that affect motor neurons. The linking point between this wide range of diseases is the strong social impact they have, degrading the freedom of action of the patient; the loss of functionality of motor neurons, caused by progressive degradation, makes the body unable to move. Thus, our solution aims at making patients more autonomous in their daily activities and at improving remote health-care monitoring for medical staff by combining virtual reality, building information modeling (BIM), the Internet of Things (IoT), and eye-tracking technologies. Starting from the 3-D parametric BIM model, patients can virtually explore the surrounding environment (through eye movements) and control the environment by interacting with IoT smart devices, thus performing different tasks.

The uprising necessity to lower CO$_{2}$ emissions and reduce energy expenditures fosters the shift toward renewable energy sources. Photovoltaic installations are the most widespread choice of renewable sources as they are relatively cheap and suited even for urban environments due to their small footprint. To reduce the initial investment and maintenance costs, the market is pushing customers to participate in renewable energy community, i.e., groups of customers that share photovoltaic systems to satisfy their energy demand by maximizing self-consumption and minimizing energy withdrawal from the power grid (eventually, they can even sell production surplus). However, the organization of these communities brings new challenges, such as optimizing the facility and estimating its economic impact. This article proposes a framework that combines geographical, meteorological, and demographic information to design optimal photovoltaic systems and evaluate the following economic benefits for the community members. To provide a complete analysis, we also consider the environmental benefit in terms of reducing CO$_{2}$ emissions. Our tests on several real-world case studies prove that our framework facilitates the installation of efficient photovoltaic systems, reducing both the energy withdrawn from the power grid and the CO$_{2}$ emissions.

Electromagnetic transients (EMT) is the most accurate, but computationally expensive method of analyzing power system phenomena. Thereby, interconnecting several real-time simulators can unlock scalability and system coverage, but leads to a number of new challenges, mainly in time synchronization, numerical stability, and accuracy quantification. This study presents such a cosimulation, based on digital real-time simulators (DRTS), connected via Aurora 8B/10B protocol. Such a setup allows to analyze complex and hybrid system-of-systems whose resulting numerical phenomena and artifacts have been poorly investigated and understood so far. We experimentally investigate the impact of IEEE 1588 precision time protocol synchronization assessing both time and frequency domains. The analysis of the experimental results is encouraging and show that numerical stability can be maintained even with complex system setups. Growing shares of inverter-based renewable power generation require larger and interconnected EMT system studies. This work helps to understand the phenomena connected to such DRTS advanced cosimulation setups.

Buildings benchmarking based on their electric profiles is a fundamental step to identify, evaluate and then possibly implement energy efficiency oriented actions. Indeed, benchmarking enables comparison among peer buildings or industrial sites and the identification of reference cases, either efficient and inefficient ones. In this regard, temporal data clustering is an effective and widely applicable benchmarking tool. In this work, we propose a novel Machine Learning based methodology, taking advantage of two fundamental tools, namely a decomposition algorithm and a clustering one. Several clustering algorithms have been tested to identify k-Means as the most suitable one. The proposed methodology includes the evaluation of energy Key Performance Indicators for effective analysis and comparison of buildings. The proposed framework has been tested on a real-world case study including around 2000 non-residential buildings. The classification of buildings based on K-Means achieved an accuracy of 99.7% with respect to their usage category. Furthermore, reference Key Performance Indicator values for each cluster are obtained and discussed to understand buildings' energy behaviour and possible reasons for inefficiencies.

Residential demand side management (DSM) strategies increase the efficiency of the smart grid. However, the efficacy of these strategies relies on the participation of customers in DSM programs, an issue usually neglected in the analysis. To encompass all aspects, we tried to identify what are the drivers for the user engagement, focusing on the social and psychological behavior of the user in order to simulate and analyze a residential DSM program with a centralized approach. In particular, the DSM program minimizes costs taking into account different energy sources and performing load shifting considering and learning users’ acceptance of requests. The results show the advantage of a preferences-aware approach, highlighting the importance of user satisfaction on participation.

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists in reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such renewable systems by modeling variability and uncertainty of their generation profiles. In this paper, we present a distributed software infrastructure for modeling and simulating energy production of photovoltaic (PV) systems in urban context. In its core, it performs simulations in a spatio-temporal domain exploiting geographic information systems together with meteorological data to estimate PV generation profiles in real operating conditions. This solution provides results in real-sky conditions with different time intervals: yearly, monthly, and subhourly. To evaluate the accuracy of our simulations, we tested the proposed software infrastructure in a real-world case study. Finally, experimental results are presented and compared with real energy production data collected from PV systems deployed in the case study area.

Smart building is a rising interdisciplinary research field that aims to improve the monitoring, management, and maintenance of buildings. The authors present an innovative solution for combining building information modeling (BIM) data with ambient information collected by heterogeneous devices deployed in the building. To collect environmental information, they exploit a distributed software architecture. It enables interoperability between heterogeneous data sources--either physical devices, such as sensor nodes, or third-party software such as Archibus--where building information resides. On top of this infrastructure, they developed an Android-based application that presents environmental building information integrated with BIM data in an augmented and virtual reality environment. The proposed solution gives users awareness about building conditions and energy consumption.

•Advanced co-simulation of rooftop PV production and households electricity demand.•Urban Energy Systems design and RES integration in the context of future smart cities.•Enabling considerations on Demand Side Management and Demand Response strategies.•High spatio-temporal resolution (25 cm and 10 minutes respectively).•Integration in a GIS environment of open-data and models.

In this paper, the design of an event-driven user-centric middleware for monitoring and managing energy consumption in public buildings and spaces is presented. The main purpose is to increase energy efficiency in buildings and public spaces, thus reducing consumption. To achieve this, the proposed service-oriented middleware has been designed to be event based, also exploiting the user behavior patterns of people who live and work in buildings. Furthermore, it allows an easy integration of heterogeneous technologies in order to enable a hardware-independent interoperability between them. Moreover, a heating ventilation and air conditioning (HVAC) control strategy has been developed, and the whole infrastructure has been deployed in a real-world case study consisting of a historical building. Finally, the results will be presented and discussed.

In recent years, the European Commission has enhanced strategies to promote ICT for energy efficiency in buildings and cities. In the smart city context, energy-related information coming from different data sources-both hardware and software-must be integrated into a common, smart digital archive for the city. DIMMER is a distributed software infrastructure for district information modeling and energy management. It correlates energy-related information from different data sources with user behavior patterns and feedback. Hence, different actors in this scenario can access relevant information for providing new services and developing more efficient policies to enhance energy optimization in cities. This will provide support for strategic city planning and will foster competition in the marketplace.

In this paper, we report a methodology, developed in the context of Smart Energy Efficient Middleware for Public Spaces European Project, aimed at exploiting ICT monitoring and control services to reduce energy usage and CO2footprint in existing buildings. The approach does not require significant construction work as it is based on commercial-off-the-shelf devices and, where present, it exploits and integrates existing building management systems with new sensors and actuator networks. To make this possible, the proposed approach leverages upon the following main contributions: (a) to develop an integrated building automation and control system, (b) to implement a middleware for the energy-efficient buildings domain, (c) to provide a multi-dimensional building information modelling-based visualisation, and (d) to raise people’s awareness about energy efficiency. The research approach adopted in the project started with the selection, as case studies, of representative test and reference rooms in modern and historical buildings chosen for having different requirements and constraints in term of sensing and control technologies. Then, according to the features of the selected rooms, the strategies to reduce the energy consumptions were defined, taking into account the potential savings related to lighting, heating, ventilation, and air conditioning (HVAC) systems and other device loads (PC, printers, etc.). The strategies include both the control of building services and devices and the monitoring of environmental conditions and energy consumption. In the paper, the energy savings estimated through simulation, for both HVAC and lighting, are presented to highlight the potential of the designed system. After the implementation of the system in the demonstrator, results will be compared with the monitored data.