Leveraging mobile positioning data to model building occupant behaviour in a mixed-use district
Notice bibliographique
Résumé
OBs are critical inputs for developing an accurate UBEM. Therefore, this paper aims to investigate the different approaches to use mobile positioning data for modelling OB in mixed-use districts and illustrate how the generated profiles can be integrated with other commonly used datasets in UBEMs. A case study is used as the basis of this investigation which focuses on Downtown Montreal. The geometry of the model was created using CityGML data while building characteristics were extracted from the tax assessment rolls (TAR). These characteristics were also used to group the buildings into archetypes, then the configuration of building systems of each archetype was assigned using the DOE available libraries.The investigation started with developing a base model where OBs were represented using standard deterministic profiles. Afterward, location-based mobile positioning data was used to generate data-driven OB profiles for different points of interest (POIs). Then, a probabilistic model was developed combined with clustering analysis to scale-up the generated profiles from POIs to a whole-building scale to match the commonly available LOD of the geometric data while considering the building types given the TAR. The final profiles were integrated into the simulation and the results were compared to the base model results.Urban building energy models (UBEMs) are expected to play a significant role in planning the current and future needs of the energy grid infrastructure. Consequently, researchers have been investigating the deployment of the available rich datasets to represent the different components of UBEMs at different resolutions and evaluate their impact on the accuracy of the model results. The representation of occupants in UBEMs has been one of the key targets of these research efforts, especially with occupant-related inputs being acknowledged as the main reasons behind the unrealistic estimation of the building performance indicators by UBEMs. However, the majority of these efforts focused only on simulating single building types and evaluating the impact of different levels of details (LODs) of occupancy profiles on the simulation results without considering the LODs available for the other UBEMs inputs. This can create a challenge for practitioners to fit the generated occupancy profiles into their simulations. Accordingly, research efforts should be directed to investigate the integration of occupant behaviour (OB) patterns with different resolutions into UBEMs in parallel with investigating the potential of the emerging data sources to represent occupants in the models.The results quantify the deviation between the standard occupancy profiles and the data-driven ones, which will help identify the necessary updates that should be considered in codes and standards for different building types. Moreover, the results will highlight the variation between the simulation results of the baseline with standard deterministic occupant-related profiles versus the data-driven models developed using mobile positioning data. More specifically, the investigations focus on the impact of the data-driven OB profiles on the peak energy demand and the energy use at different temporal and spatial resolutions (i.e., building scale, building sector scale, and district scale). Finally, the results will be used to develop guidelines and recommendations regarding how occupancy should be integrated for different UBEM applications.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».