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A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

Recent strategic policies and regulations dealing with market liberalization and decarbonization plans, such as the European directives contained in the recent EU Clean Energy for All Europeans Package, are seeking to promote new roles for citizens in the management of the self-produced renewable energy and the development of local energy markets. In this context, this paper aims at presenting the planning actions for the transition of the current passive distribution system of the Municipality of Berchidda (Italy) towards a smart local energy community. This planning study represents the first stage of a development action financed by the Sardinian Region, whose Regional Energetic and Environmental Plan identifies the Municipality of Berchidda as a priority area to focus the experimental actions for innovative smart grids and intelligent energy management. The project, named “Berchidda Energy 4.0”, focuses on increasing the energy efficiency of the community by boosting local renewable generation production and maximizing its self-consumption, also with the support of storage systems, as well as increasing the active involvement of the consumers that will be equipped with a smart home automation system for demand response applications.

Reducing greenhouse gas emissions, limiting the effects of climate change and decreasing the environmental, social and economic costs of energy production are some of the main issues related to the sustainable development of modern society. Energy communities, envisioned to enable local energy exchange between consumers and producers of renewable energy, represent a possible scenario towards a cleaner and sustainable energy system. In this paper, an energy community management model called Power Cloud and presented in previous papers is proposed for a real-world practical application at the University of Calabria. In particular, the implementation of the information and communication technology (ICT) architecture and other enabling technologies, such as the nanogrid and the smart energy box, are discussed in detail. The experiment results show that by adopting the Power Cloud management model it is possible to obtain significant savings in terms of energy cost, which provide benefit for a community, such as a university campus.

In recent years, the distribution of local and renewable generation plants has introduced significant challenges in the management of electrical energy. In order to increase the usage of renewable energy, the prosumers, i.e., the residential users that can act both as producers and consumers, can benefit from joining together and forming energy communities. The deployment of an energy community is based both on technological advancements and on a deep understanding of human decision-making, which in turn requires knowledge about the factors that influence the behavior of residential users. This new scenario calls for great research investigations aimed to improve the management of energy exchanges inside energy communities. An important role in this context is played by the Internet of Things (IoT) technology, as smart IoT objects are used both as a source of real-time information regarding the energy production and the users’ requirements, and as actuators that can help to regulate the distribution and use of energy. In this paper, an IoT-aware optimization model for the energy management in energy communities is presented. The main novelty consists in modeling the entire energy community as a whole, rather than each prosumer separately, with the goal of optimizing the energy sharing and balance at the community level. Experimental results, performed in an university campus, show the advantages of the approach and its capability of reducing the energy costs and increasing the community’s energy autonomy.

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SummaryMulti‐agent systems have been proven very effective for the modelling and simulation (M&S) of complex systems like those related to biology, engineering, social sciences and so forth. The intrinsic spatial character of many such systems leads to the definition of a situated agent. A situated agent owns spatial coordinates and acts and interacts with its peers in a hosting territory. In the context of parallel/distributed simulation of situated agent models, the territory represents a huge shared variable that requires careful handling. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach to modelling and distributed simulation of large‐scale situated multi‐agent systems. Time management is exploited for resolving conflicts and achieving data consistency while accessing the environment. The approach allows a simplification of the M&S tasks by making the modeller unaware of distribution concerns while ensuring the achievement of good scalability and performance during the distributed simulation. Practical aspects of the approach are demonstrated through some modelling examples based on Tileworld. Copyright © 2014 John Wiley & Sons, Ltd.