| id |
ecaade2023_51 |
| authors |
Aman, Jayedi, Kim, Jong Bum and Verniz, Debora |
| year |
2023 |
| title |
AI-Integrated Urban Building Energy Simulation: A framework to forecast the morphological impact on daylight availability |
| doi |
https://doi.org/10.52842/conf.ecaade.2023.2.369
|
| source |
Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 369–378 |
| summary |
The research presents a computational framework to investigate the relationship between urban morphology and environmental performance metrics of buildings. Understanding how buildings interact with their surroundings is crucial in optimizing environmental performance. Current urban building energy simulation methods (UBES) often overlook the complex interaction between urban morphology and environmental performance across a diverse set of attributes, resulting in inaccuracies. The proposed framework integrates machine learning (ML) with physics-based simulations and includes Parametric Building Information Modeling, iterative physics-based simulations, Multi-Objective Optimization, and a graph neural network. The framework leverages the detailed analysis capabilities of physics-based simulations and the data processing strengths of ML to analyze urban morphological attributes. Evaluations indicate that the framework enhances prediction accuracy while considering the influence of urban morphology on environmental performance. |
| keywords |
Urban Morphology, Urban Building Energy Modeling, Graph Neural Networks, Sustainable Urban Development, Environmental Performance, Multi-objective Optimization |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (2,681,983 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/12/10 10:49 |
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