Influence of psychological and personal factors on predicting individual’s thermal comfort in an office building using linear estimation and machine learning model

Author:

Nafiz Muhammad1,Zaki Sheikh Ahmad1,Nadarajah Pravin Diliban2,Singh Manoj Kumar2

Affiliation:

1. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

2. Energy and Sustainable Built-Environment Design(ESBD) Laboratory, Department of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India

Funder

Japanese Chamber of Trade & Industry, Malaysia

Universiti Teknologi Malaysia

Publisher

Informa UK Limited

Reference70 articles.

1. Impact of indoor environmental quality on occupant well-being and comfort: A review of the literature

2. Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study);Allamy H.;Computer Science, Communication and Instrumentation Devices,2015

3. American Society of Heating, Refrigerating and Air-Conditioning Engineers. (1992). ASHRAE standard thermal environmental conditions for human occupancy. The Society.

4. Thermal comfort in residential buildings – Failure to predict by Standard model

5. Using machine learning algorithms to predict occupants’ thermal comfort in naturally ventilated residential buildings

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1. Development and Application of EEG Signal Pattern Analysis and Artificial Neural Network for Indoor Comfort Measurement;2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv);2024-06-12

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