Raeiszadeh, Mahsa, Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146 and Tahayori, Hooman (2023) PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home. Journal of Reliable Intelligent Environments - Springer.

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Abstract

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants’ behavior patterns.
Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories.
Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating
residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels.

Item Type:	Article
Depositing User:	Ali Bahadori-Jahromi
Date Deposited:	14 Jun 2023 17:03
Last Modified:	06 Feb 2024 16:15
URI:	https://repository.uwl.ac.uk/id/eprint/10060

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