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Enhancing Thermal Comfort in Buildings with Machine Learning-Based Overheating Prediction

Enhancing Thermal Comfort in Buildings with Machine Learning...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Rajalekshmi, Mithun Vijayakumar Hashemi, Arman Sharif, Mhd Saeed Intelligent Technologies Research Group University of East London London United Kingdom Dept. Of Architecture and Visual Arts University of East London London United Kingdom

ISBN: (纸本)9798331533137

The goal of this project is to improve the application of machine learning techniques in the summertime prediction of thermal comfort in residential structures (for both present and future weather situations). Using DesignBuilder’s integrated simulation engine and simulated data, the research creates strong prediction models with Random Forest and XGBoost algorithms. Essential factors like building orientation, window-to-floor ratios, U-values, and operating temperatures were examined using exploratory data analysis, feature engineering, and thorough data preparation. Mean Absolute Error (MAE) and R-squared values were applied for the accurate and effective validation of the models. The findings demonstrate significant potential for early-stage decision making on building designs, for reducing risk of overheating and opening the door to more sustainable and comfortable living spaces. Future research endeavors aim to enlarge the dataset, explore different Machine learning modeling techniques, and enhance the models’ capability to predict and mitigate overheating in different building kinds and climatic conditions. ©2024 IEEE.

关键词： Thermal comfort

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Enhancing Thermal Comfort in Buildings with Machine Learning-Based Overheating Prediction

Enhancing Thermal Comfort in Buildings with Machine Learning...

引用

International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Mithun Vijayakumar Rajalekshmi Arman Hashemi Mhd Saeed Sharif Intelligent Technologies Research Group University of East London London United Kingdom Dept. Of Architecture and Visual Arts University of East London London United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

The goal of this project is to improve the application of machine learning techniques in the summertime prediction of thermal comfort in residential structures (for both present and future weather situations). Using DesignBuilder's integrated simulation engine and simulated data, the research creates strong prediction models with Random Forest and XGBoost algorithms. Essential factors like building orientation, window-to-floor ratios, U-values, and operating temperatures were examined using exploratory data analysis, feature engineering, and thorough data preparation. Mean Absolute Error (MAE) and R-squared values were applied for the accurate and effective validation of the models. The findings demonstrate significant potential for early-stage decision making on building designs, for reducing risk of overheating and opening the door to more sustainable and comfortable living spaces. Future research endeavors aim to enlarge the dataset, explore different Machine learning modeling techniques, and enhance the models' capability to predict and mitigate overheating in different building kinds and climatic conditions.

关键词： Temperature distribution Technological innovation Accuracy Buildings Predictive models Data models Root mean square Random forests Regression tree analysis Meteorology

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Innovations in shaping the residential and retail buildings. Functional and pro-environmental potential of shipping containers in architecture 13

Innovations in shaping the residential and retail buildings....

引用

13th International Research-Technical Conference on the Problems of Designing, Construction and Use of Low Energy Housing, ENERGODOM 2018

作者： Berbesz, A.M. Szefer, I.M. Wroclaw University of Science and Technology Faculty of Architecture Dept. of Housing and Indust. Arch. Interior Des. Rural and Landscape Plan. and Visual Arts W1/K6 Poland

Shipping containers are currently one of the innovative trends of shaping architectural objects. They are used to build portable and permanently set in the ground residential and public buildings. Realization with the use of shipping containers appear all over the world, i.a. single- and multifamily houses in the Netherlands, Spain, Denmark and France, the bus station in the Netherlands, the art gallery in Tokyo. Particularly, many realization with the use of shipping containers relate to housing environment due to ease of transport, speed of installation and assembly, shorten the construction time and pro-environmental potential. In contrary, conventional housing is responsible for 1/4 forest logging area, absorbs 1/6 of fresh water resources and consumes 2/5 of liquid fuels and materials. The respect for the environment in the aspect of the global direction of shaping residential and retail buildings is particularly important. This paper focuses on pro-environmental, usably and economical aspects of architectural objects with the use of shipping containers on the example of Polish and world-wide realization. © Published under licence by IOP Publishing Ltd.

关键词： Housing

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