Energy Model Machine (EMM) - Instant Building Energy Prediction using Machine Learning

Asl, Mohammad Rahmani; Das, Subhajit; Tsai, Barry; Molloy, Ian; Hauck, Anthony

doi:10.52842/conf.ecaade.2017.2.277

Cited by 3 publications

(2 citation statements)

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“…This model of eager learning is generally called the surrogate model using synthetic datasets, and some research has already proved its contribution. Rahmani Asl showed the usefulness of the energy approximation model integrated into the Building Information Modelling (BIM), which enables design space exploration promptly (Rahmani Asl et al 2017).…”

Section: Literature Reviewsmentioning

confidence: 99%

A Data-Oriented Optimization Framework

Karoji

2023

eCAADe Proceedings

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Design optimization using the Multi-Objective Evolutionary Algorithm (MOEA) has still been studied, progressed well, and used to improve building performance. Besides, floor plan generation that is the problem of fitting several rooms into an outline given beforehand has recently been studied well using machine learning models. Although the building performance and a floor plan intimately relate, they are rarely combined in one optimization framework. A separation of these problems often forces users to manually explore accurate floor plans in a solution space or limit optimizing the building performance following certain machine learning methods and its dataset. We mainly focused on these issues and developed a custom-made model that contains association rule mining and the cosine similarity formula extracted from machine learning methods. This model of lazy learning is added to an MOEA-based optimization framework and outputs the total cosine similarity between each generated floor plan and the referred plans dynamically selected from our dataset, and the framework maximizes it. We applied this framework to a case study on generating eco-conscious office building designs that will enable them to convert easily in the future. This paper elaborates on how to create a dataset and formulation for optimization, and we emphasize the plausibility of floor plan generation. Finally, we demonstrated the efficiency of the framework by comparing the performance indicators of optimization.

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Section: Literature Reviewsmentioning

confidence: 99%

A Data-Oriented Optimization Framework

Karoji

2023

eCAADe Proceedings

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“…Since AI can analyze, learn, and synthesize data, it can aid designers in making successful decisions by enabling the prediction of environmental parameters of their designs. Yet, developing accurate, predictive real-time techniques for environmental analysis remains difficult (Rahmani Asl et al, 2017). A solution to achieve high-fidelity realtime prediction of environmental analysis in generative design is to employ deep learning-based surrogate modeling.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

2022

CAADRIA Proceedings

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The work collected in these volumes has been produced during extraordinary circumstances, as the Covid-19 pandemic has continued to impact working and living conditions. Papers have been written, reviewed and edited throughout lockdowns, challenging family situations and health crises. The high number of submissions and quality of the papers is a testament to the resilience and strength of the CAADRIA community.The papers in this publication have been selected through a two-stage, doubleblind peer review process. All reviews and papers have been evaluated by the Paper Selection Committee. After receiving an initial 488 abstract submissions, a final set of 150 full papers has been selected for publication. We thank all authors for their contributions to the process and congratulate the accepted paper authors.The papers in these proceedings are specifically selected for their contribution to this year's conference theme, following the conference organisers' call for authors to position their work in relation to the United Nations Sustainable Development Goals (SDGs). As the world is experiencing the increasing impacts of climate change, there is an urgent need to reflect on the potential of the latest research in architecture, urbanism and construction to address these global challenges.Our special thanks go out to our more than 180 international reviewers, who volunteered their valuable time and effort. We also thank the organising team in Sydney for developing the 2022 CAADRIA Conference as a strong collaborative effort between academic institutions, industry and practice. We thank Gabriel Wurzer for his support during the proceedings' final production stage, and for facilitating their publication via the open access database CumInCAD.org. Finally, we sincerely thank the CAADRIA community for offering us the honour to serve as members of the paper selection committee for Post-Carbon, the 27 th International CAADRIA Conference 2022.

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Deep Learning-Based Surrogate Modeling for Performance-Driven Generative Design Systems

Yousif¹,

Bolojan²

2022

CAADRIA Proceedings

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Within the context of recent research to augment the design process with artificial intelligence (AI), this work contributes by introducing a new method. The proposed method automates the design environmental performance evaluation by developing a deep learningbased surrogate model to inform the early design stages. The project is aimed at automating performative design aspects, enabling designers to focus on creative design space exploration while retrieving real-time predictions of environmental metrics of evolving design options in generative systems. This shift from a simulation-based to a predictionbased approach liberates designers from having to conduct simulation and optimization procedures and allows for their native design abilities to be augmented. When introduced into design systems, AI strategies can improve existing protocols, and enable attaining environmentally conscious designs and achieve UN Sustainable Development Goal 11.

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Energy Model Machine (EMM) - Instant Building Energy Prediction using Machine Learning

Cited by 3 publications

References 0 publications

A Data-Oriented Optimization Framework

A Data-Oriented Optimization Framework

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

Deep Learning-Based Surrogate Modeling for Performance-Driven Generative Design Systems

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