Multi-agent system architecture for smart home energy management and optimization

Asare-Bediako, B.; Kling, W.L.; Ribeiro, Paulo F.

doi:10.1109/isgteurope.2013.6695331

Cited by 43 publications

(24 citation statements)

References 13 publications

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“…In the smart application setting based on MAS, agents are represented by hardware of software for learning the preferred environmental conditions, habits, states, and situations of users and subsequently taking appropriate actions [14]. In the literature, MAS consists of four agent groups, which are control and monitoring agents (CMA), Information Agents (IA), Application Agents (AA), and Management and Optimization Agents (MOA).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Bui

Jung

Camacho

2018

Sensors

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Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Bui

Jung

Camacho

2018

Sensors

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“…However, similarly to the above mentioned works, hard constraints are considered and no a posteriori evaluation as well as customer profiling are given. [22] proposes a multi-agent architecture for optimal energy management in smart homes considering grid power supply. Customer comfort is considered as a thermal comfort zone delimited by hard boundaries which must not be left, similarly to [19].…”

Section: Related Workmentioning

confidence: 99%

Smart Home Energy Management Including Renewable Sources: A QoE-driven Approach

Pilloni

Floris

Meloni

et al. 2016

IEEE Trans. Smart Grid

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Smart Home Energy Management (SHEM) systems can introduce adjustments in the working period and operations of home appliances to allow for energy cost savings, which can however affect the Quality of Experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoEaware SHEM system, which relies on the knowledge of the annoyance suffered by users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles describing different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware Cost Saving Appliance Scheduling and the QoE-aware Renewable Source Power Allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices. The latter reallocates appliances' operations whenever a surplus of energy is available by Renewable Energy Sources (RES). Experimental results demonstrate that the annoyance perceived by users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving.

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“…Hence, control flexibility from grid to devices through a HEMS has been a difficult proposition. Multi-agent system (MAS) architectures have been proven as means to provide the necessary infrastructure for HEMS to achieve control goals (Asare-Bediako, Kling, and Ribeiro 2013). A MAS consists of agents that can communicate, and coordinate behaviour based on the overall system goals and status of other agents.…”

Section: Introductionmentioning

confidence: 99%

An Implementation for Transforming a Home Energy Management System to a Multi-agent System

Zandi¹,

Starke²,

Kuruganti³

2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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In the United States, 41% of produced energy is consumed by the building sector, i.e. residential and commercial buildings (Building Energy Data Book, Buildings Sector, US Department of Energy, Office of Energy Efficiency and Renewable Energy, Building Technologies Office, 2012. n.d.). The anticipation is that Home Energy Management Systems (HEMS) will support energy efficiency gains through control of the devices in an optimal fashion. New opportunities are offering the ability to integrate grid type controls and the most suitable way to perform these controls is through a multi-agent system (MAS). In this paper, approaches on supporting a HEMS and MAS integration are discussed.

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Multi-agent system architecture for smart home energy management and optimization

Cited by 43 publications

References 13 publications

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Smart Home Energy Management Including Renewable Sources: A QoE-driven Approach

An Implementation for Transforming a Home Energy Management System to a Multi-agent System

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