A Novel Occupant-adapted and Fuzzy Logic-ready Visual Comfort Modelling Approach Using Machine Learning Algorithms

In this article we present a novel approach to model visual comfort based on supervised statebased machine learning with Hidden Markov Models and one easy-to-obtain variable (illuminance measurements at the horizontal work-plane; Edesk). Data mining was performed on sensor data recorded for two years in a single-occupant office room and the developed model classifies workplane illuminances into 3 states: comfort; discomfort because of low light; discomfort because of excessive light. Results show that a training period of 4 to 8 months of recorded data leads to a visual comfort identification (classification) accuracy of 100%. When training the model using 4-month data, an overall 92% accuracy can be achieved (75% for the ‘discomfort because of low light’ state). Following further analysis of this occupant-adapted model, we discuss the confidence (‘normalised relative likelihood’) with which the model classifies illuminances in one of three different states as a function of the Edesk. We argue that the resulting metrics are an ideal input which can be readily used into automatic lighting controllers based on fuzzy logic. Last, the model’s performance is compared and validated against state-of-the art classifiers such as Bayesian and k-Nearest Neighbors.