Human comfort datasets are widely used for multiple scenarios in smart buildings. From thermal comfort prediction to personalized indoor environments, labelled subjective responses from participants in a experiment are required to feed different machine learning models. However, many of these dataset are small in samples per participants, number of participants, or suffer from a class-imbalanced of its subjective responses. In this work we explore the use of Generative Adversarial Networks to generate synthetic samples to be used in combination with real ones for data-driven applications in the built environment.

It involves analyzing a dataset to predict AMV (thermal comfort classification) and PMV (thermal comfort regression) through preprocessing, feature selection, and the application of regression and classification algorithms.

Prediction of thermal comfort parameter using random forest regressor machine learning model

A machine learning model for Thermal Comfort in Housing using Python, Scikit-Learn, and Pandas. Integrated TensorFlow for predictions and deployed the model using Streamlit for an interactive user interface.