Evaluating the Daylighting and Energy Performance of Container Housing Systems in Coastal Regions: An Iterative Modeling Approach

Haque, Md. Obidul; Rahman, Nabeela Nushaira; Tabassum, Nusrat; Aman, Jayedi

doi:10.1007/978-3-031-11232-4_13

Cited by 2 publications

(1 citation statement)

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“…Leveraging fundamental physics principles, these simulations accurately model complex systems such as building energy use and environmental performance. Through multiple simulation runs, the iterative approach refines model parameters (Haque et al 2022).…”

Section: Physics-based Simulationmentioning

confidence: 99%

AI-Integrated Urban Building Energy Simulation: A framework to forecast the morphological impact on daylight availability

Aman,

Kim,

Verniz

2023

eCAADe Proceedings

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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.

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Section: Physics-based Simulationmentioning

confidence: 99%