A probabilistic model to predict household occupancy profiles for home energy management applications


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Rueda, L., Sansregret, S., Le Lostec, B., Agbossou, K., Henao, N. et Kelouwani, S. (2021). A probabilistic model to predict household occupancy profiles for home energy management applications. IEEE Access, 9 . pp. 38187-38201. ISSN 2169-3536 DOI 10.1109/ACCESS.2021.3063502

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Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles.

Type de document: Article
Mots-clés libres: Occupancy Behavior Survival analysis Hazard rate Markov-chain Cox regression
Date de dépôt: 12 avr. 2023 12:06
Dernière modification: 12 avr. 2023 12:06
Version du document déposé: Version officielle de l'éditeur
URI: https://depot-e.uqtr.ca/id/eprint/10626

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