OccupancySense: Context-based indoor occupancy detection & prediction using CatBoost model

Abstract

Occupancy detection and prediction are two well-established problems which can be improved further to achieve higher accuracy in both cases than the existing solutions. To achieve the desired higher accuracy, proposed OccupancySense model detects human presence and predicts indoor occupancy count by the fusion of Internet of Things (IoT) based indoor air quality (IAQ) data along with static and dynamic context data which is a unique approach in this domain. This data fusion helps us to achieve higher forecasting accuracy along with the integration of state of the art gradient boosting based categorical features supported CatBoost algorithm. For comparison, other commonly used machine learning classification and regression algorithms, e.g., Multiple Linear Regression (MLR), Decision Tree (DT), Random Forests (RF) and Support Vector Machine (SVM) for regression and Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF), Support Vector Machine (SVM) for classification, were also assessed during this experiment. Out of these, CatBoost outperformed other models when considered in terms of accuracy. Hence, CatBoost is used as the core of the OccupancySense design and we have validated the proposed model by a real-world case study with continuous 91 days of indoor data, having 33 unique external features. These features are collected directly as well as derived from the collected data. To handle these features, feature engineering plays a key role in the OccupancySense model. The speciality of this model is, it is non-intrusive one but have high predictive power. It can detect occupancy and predicts headcount along with occupancy density of the room pretty accurately with 99.85%, 93.2% and 95.6% respectively (with 10 fold cross-validation) which outperforms other state of the art models.