Abstract
Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.
Highlights
The first mention of the smart plug in the news dates the year of 2008, reporting a solution able to remotely control a plug
This paper presents the first case study where several environmental awareness smart plug (EnAPlug) are deployed in a residential house, and where three air conditioner units are optimized using EnAPlugs’ shared knowledge
To test EnAPlug’s shared knowledge capability, the scenario where the air conditioner (AC) connected to EnAPlugs optimizes its consumptions using the shared knowledge regarding the user’s location was run—question iv of Section 4
Summary
The first mention of the smart plug in the news dates the year of 2008, reporting a solution able to remotely control a plug. Scientific publications report case studies where smart plugs are combined with energy management systems [3,4,5]. The proposed distributed optimization solution enables the knowledge and individual learning processes from all EnAPlugs to be combined by a centralized decision-making approach. Using this combined learning process, each EnAPlug is able to recommend the best decisions from its perspective, according to the overall situation of all plugs and to its individual awareness of the context. The proposed solution takes the scientific community, especially in the field of power and energy systems, one step further by combining distributed optimization with context-aware learning, providing a novel adaptive solution that takes the most out of different approaches.
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