Enhancing Outdoor Comfort: A Machine Learning Framework for Predicting Comfort Level

This paper presents a comprehensive study on predicting the performance of outdoor comfort systems using machine learning (ML) models. The study is based on a dataset collected from various projects in Ras Al Khaimah (RAK), United Arab Emirates (UAE) and focuses on the prediction of temperature and humidity values. The evaluated models include Random Forest Learner, Neural Network, and Support Vector Machine (SVM), with performance assessed using key metrics. The findings of the study provide actionable insights from the data analysis process and the performance of machine learning models. Among the models evaluated, the Random Forest Learner demonstrated superior performance in capturing the variability in the data and generating accurate predictions. The Root Mean Square Error (RMSE) for temperature was 0.838 ℃, and for humidity, it was 1.4%, which are considered relevant for estimating comfort levels by simulating the site’s data before installing the cooling controls. This highlights its potential as a reliable tool for predicting outdoor comfort system performance. Furthermore, the study proposes integrating machine learning models into an enterprise-based decision support system for planning and managing outdoor comfort projects. By leveraging the predictive capabilities of these models, stakeholders can make informed decisions regarding system design and optimization, which helps them boost efficiency and reduce the cost of such costly projects.