Critical review and illustrative examples of office occupant modelling formalisms
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Notice bibliographique
Résumé
It is widely understood that occupants can have a significant impact on building performance. Accordingly, the field has benefited extensively from research efforts in the past decade. However, the methods and terminology involved in modelling occupants in buildings remains fragmented across a large number of studies. This fragmentation represents a major obstacle to those who intend to join in this research endeavor as well as for the convergence and standardization of methods. To address this issue, this paper investigates occupant modelling methods for the key domains of electric lighting, blinds, operable windows, thermostats, plug loads, and occupancy. In the reviewed literature, five broad categories of occupant model formalisms were identified: schedules, Bernoulli models, discrete-time Markov models, discrete-event Markov models, and survival models. Illustrative examples were provided from two independent datasets to demonstrate the strengths and weaknesses of these model forms. It was shown that Markov models are suitable to represent occupants' adaptive behaviors, while survival models are suitable to represent occupancy, non-adaptive behaviors, and infrequently executed adaptive behaviors, such as the blinds opening behavior. Practical application: The engineering application of the occupant modelling formalisms that are critically reviewed in this paper is that these models are highly beneficial for incorporating occupants' presence and behaviors into building design and control. Building design can be improved significantly regarding energy use and occupant comfort when the most suitable occupant models are implemented in simulation-aided building design process. Ultimately, like for any modelling domain, the most suitable model is dependent on the modelling objective (e.g. optimizing passive design, equipment sizing), building type and size, occupant-related domain (e.g. occupancy, window-opening behavior), and climate zones. Furthermore, there is great potential in improving occupant comfort and energy savings of existing buildings when occupants' presence and interactions with buildings' systems and components are predicted accurately using occupant models.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle