Flexible artificial intelligence optimization for smart home energy systems with V2X

Rippstein, Florian; Lenk, Steve; Rudolph, Martin; Klaiber, Stefan; Bretschneider, Peter

doi:10.1109/vppc55846.2022.10003438

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…The idea of smart homes enriched with cognitive technologies has long been discussed, as they take the cognitive burden off users. This can be seen, for example, in a large amount of research, much of which focuses on energy savings with the help of machine learning and artificial intelligence [ 21 , 22 ]. User comfort is put at the center of the focus of numerous works, alongside other factors, such as energy saving.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Agent System for Service Provisioning in an Internet-of-Things Smart Space Based on User Preferences

Mandaric,

Keselj Dilberovic,

Jezic

2024

Sensors

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The integration of the Internet of Things (IoT) and artificial intelligence (AI) is critical to the advancement of ambient intelligence (AmI), as it enables systems to understand contextual information and react accordingly. While many solutions focus on user-centric services that provide enhanced comfort and support, few expand on scenarios in which multiple users are present simultaneously, leaving a significant gap in service provisioning. To address this problem, this paper presents a multi-agent system in which software agents, aware of context, advocate for their users’ preferences and negotiate service settings to achieve solutions that satisfy everyone, taking into account users’ flexibility. The proposed negotiation algorithm is illustrated through a smart lighting use case, and the results are analyzed in terms of the concrete preferences defined by the user and the selected settings resulting from the negotiation in regard to user flexibility.

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Section: Related Workmentioning

confidence: 99%