Development of a surrogate model for interactive early-stage net-zero building design
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
The Net-Zero Navigator is a collaborative effort to leverage surrogate modeling for use in an opensource, online early stage building energy modeling and decision-making tool, formed among industry, academic and government stakeholders in Canada. The surrogate modelling underpinning Net Zero Navigator leverages machine learning techniques to train statistical meta-models based on BPS input and output data, emulating high-fidelity building simulation and providing an engine for rapid design performance estimates. The platform is built on a foundation of existing tools (EnergyPlus, TensorFlow, KERAS API) as well as the codebase of the Building and Energy Simulation, Optimization and Surrogate-modelling (BESOS) platform. Overall, the Net-Zero Navigator platform supports fast, interactive concept-stage building design, at a stage where decisions have a proportionally greater impact on downstream performance, and where flexibility and speed are important. To further disseminate the knowledge gained through development of Net Zero Navigator, this study dissects the architecture and hyperparameters that define the artificial neural networks, demonstrating importance for specific aspects of the building energy estimation through an experimental procedure aligned with hyperparameter optimization methods found in machine learning best practice. This methodology involves hundreds of thousands of EnergyPlus simulation runs and equivalent numbers of neural network configurations.High-performance building design requires rapid iteration involving many highly integrated decisions, with diminishing potential for impact as the design process progresses. Existing high-fidelity simulation approaches have limited ability to deliver fast, integrated design iterations, which can compromise the ability to achieve net-zero energy in new buildings.In most cases, these tools rely on generating direct representations of the building explicitly, using detail-oriented, physics-based modelling engines. This limits the computational speed and flexibility, as well as the ability to explore the complex problem space effectively. Attempts to simplify the model input process in these tools may also contribute to errors where the underlying engine cannot adaptively and automatically account for complex interactions that can result.Further research has shown that machine learning methods are an area of study with potential to connect the needs during early concept design and planning with a viable toolset for use by practitioners and researchers. Specifically, surrogate methods have been shown to produce robust, reliable building energy analysis results while greatly reducing computational time and input complexity by the user. However, there is trade-off potential between absolute accuracy and flexibility, breadth and speed of design space exploration that must also be examined.As a nascent field of study, surrogate modelling in the buildings domain faces many challenges, including loss of interpretability and amplification of errors due to poorly framed problems. However, as a new addition to building design toolset, with novel advantages and potential for development, surrogate modelling can be used to supplement traditional analytical and high-fidelity techniques. The Net Zero Navigator project was developed to help resolve these limitations.The results of this study highlight some of the computational benefits, the scale of modeling uncertainty introduced, and the relationships between energy model complexity and surrogate model configuration. Furthermore, this study provides a basis to discuss the benefits of Net Zero Navigator and surrogate modeling for building energy simulation, leveraging the multi-disciplinary development efforts to suggest best practice and promising directions for future work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 |
| 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