Charilaos Akasiadis

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In this paper, we present a directly applicable scheme for electricity consumption shifting and effective demand curve flattening. The scheme can employ the services of either individual or cooperating consumer agents alike. Agents participating in the scheme, however, are motivated to form cooperatives, in order to reduce their electricity bills via lower(More)
In this work we address the problem of coordinated consumption shifting for electricity prosumers. We show that individual optimization with respect to electricity prices does not always lead to minimized costs, thus necessitating a cooperative approach. A prosumer cooperative employs an internal cryptocurrency mechanism for coordinating members decisions(More)
In this paper we present SYNAISTHISI, i.e., a cloud-based platform, that provides the necessary infrastructure in order to interconnect heterogeneous devices and services over heterogeneous networks. SYNAISTHISI facilitates the orchestration of a collective functionality allowing several services to be managed through agents that dynamically allocate the(More)
A variety of multiagent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents uncertainty, and correctly predicting their future behaviour. In this paper, we adopt two stochastic(More)
Recent advancements in single board computers, communications technologies and protocols, as well as the concepts of service-oriented architectures (SoA) and everything as a service (EaaS), constitute a prelude to the Internet of Things (IoT) revolution. Billions of devices are interconnected and integrated as modular web services, which can be used and(More)
Managing energy consumption and production is a challenging problem and proactive balancing between the amount of electricity produced and consumed is needed. In this work, we examine mechanisms that give incentives to consumers to efficiently reschedule their demand, thus balancing the overall energy production and consumption. Viewing the smart grid as a(More)
Staff scheduling for public organizations and institutions is an NP-hard problem and many heuristic optimization approaches have already been developed to solve it. In the present paper, we present two meta-heuristic computational intelligence approaches (Genetic Algorithms and Particle Swarm Optimization) for solving the Staff scheduling problem. A general(More)
A variety of multiagent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents’ uncertainty, and correctly predicting their future behaviour regarding power consumption shifting(More)