Indoor Occupancy Estimation Using Advanced Support Vector Machine Learning Algorithm

Indoor occupancy estimation is crucial in energy management, security and building automation systems. The Machine learning approaches that are normally used to improve the reliability and accuracy of this estimate face challenges in capturing dynamic occupant behavior in diverse environments. This research develops an advanced Support Vector Machine (SVM) learning model to enhance accuracy and efficiency in estimating indoor occupancy. The model was developed using an occupancy dataset, organized, cleaned and pre-processed. Support Vector Machine (SVM) was used as a classifier for feature extraction and classification, enhancing generalization and capturing complex relationships in data. The result shows that the SVM model performs better than the earlier employed machine learning approaches in accuracy and precision. It also performs better in terms of False Positive Rate (FPR), recall and specificity. The algorithm has been used as a low-cost people-counting strategy for real-time applications, making it a valuable tool for estimating indoor occupancy in diverse environments.