Using Machine Learning to Predict Window Opening Position in a Naturally Ventilated Building

Abstract

Abstract Advancements in machine learning have faciliated its use in many domains. In this work we apply it to building sector, where mechanical ventilation systems are prevalent. While natural ventilation still can be suitable in many situations, the difficulty in estimating airflows and long computational simulation times prevents its adoption. Since ventilation rate depends heavily on window opening angle, we employ a computer vision techniques to estimate the states. We train a Fully-Connected Neural Network on images of European-style tilt-and-turn windows set at discrete positions, achieving over 95% average F1-Score. We highlight potential drawbacks with the method and identify steps forward on the path to real-world implementation.