Deep learning models for building window-openings detection in heating season

Abstract

The increasing use of monitoring systems such as Building Management System (BMS) or connected devices bring the opportunity to better evaluate, model or control both occupants' comfort and energy consumed by an operated building thanks to the consequent amount of data provided (e.g., air temperature, CO2 concentration, electricity consumption). Occupants' behavior and more specifically window-openings affect both occupants’ thermal comfort and building energy consumption and are therefore key components to consider. This paper presents a comparison of machine learning models applied on window-openings detection during the heating season such as: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forest Classifier (RFC) and two Recurrent Neural Network (RNN), namely, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). While some applications of Artificial Intelligence (AI) methods applied on window-openings detection exist in the literature, this study proposes a detailed comparison of the main methods and focuses on the impact of feature engineering process considering four different data transformations based on field expertise and more than 800 different combinations built on six indoor and outdoor measurements. Results show that some of the proposed transformations and combinations positively impact all models performances. The best performances on window-openings detection are attained by using indoor temperature and CO2 concentration on RNN models with an average F1-score of 0.78 while LDA, SVM and RFC models tend to provide satisfying but lower performance around 0.70–72. In addition, by using the right transformation, significant results can be achieved by detecting up to 84–88% of window-opening times with the sole use of indoor air temperature measurements.