It is evident that a patient empowerment approach based on self-management ICT tools is useful and accepted by patients and physicians. Further, there are clear indications that ICT frameworks such as the one presented in this paper support patients in behavioral changes and in better disease management. Finally, it was realized that self-management solutions should be built around the objective not only to educate and guide patients in disease self-management, but also to assist them in exploring the decision space and to provide insight and explanations about the impact of their own values on the decision.
Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications.
The formulation of a Personal Area Network (PAN), consisting of a wireless infrastructure of medical sensors, attached to patient's body, and a supervising device carried by them, lays the path for continuous and real-time monitoring of vital signs without discomforting the person in question. This infrastructure enhances the context of remote healthcare services by supporting flexible acquisition of crucial vital signs, while at the same time it provides more convenience to the patient. Aiming at the exploitation of the inherent features and requirements of wireless medical sensor networks, in this paper we focus on the main design guidelines of a low power Medium Access Control (MAC) protocol, designated to support a patient PAN. The proposed protocol intends to improve energy efficiency in such applications and thus is oriented towards the prevention of main energy wastage sources, such as collision, idle listening and power outspending.
Power systems undergo massive operational and technological changes amid increasing demand for environmental sustainability and energy efficiency. The traditional, supplydriven approach, relying on large-scale generation plants, which has dominated old utilities, is reconsidered to incorporate the increased penetration of variable renewable energy sources, distributed generation and storage. Demand Response is an important instrument for improving energy efficiency, since it increases consumers' engagement and provides a mechanism to reduce or shift consumption, resulting in energy savings. Regulators and policy makers in several European countries take substantial measures to encourage market uptake of Demand Response as a means to mitigate the limitations of the existing grid and boost the transition to a low carbon economy. This goes hand in hand with the deployment of services by stakeholders who aggregate consumption flexibility and offer it to grid operators or to the electricity market. Several large-scale pilot projects explore the feasibility of Demand Response in residential and small commercial customers. This paper provides an overview of the current regulatory and policy framework in Europe and summarizes the state of play of commercial and pilot Demand Response deployments in various European countries. Also, it highlights some key research objectives associated with Demand Response.
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