Predicting personal thermal preferences based on data-driven methods

Abstract

One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

“…Another study compared naïve Bayes with artificial neural network (ANN), fuzzy logic (FL), and PMV-based algorithms. Other results show that the naïve Bayes calculation provides a prediction accuracy of 73% [30]. The difference compared with the research conducted in other studies is 1%.…”

“…that the naïve Bayes calculation provides a prediction accuracy of 73% [30]. The difference compared with the research conducted in other studies is 1%.…”