Employing Information Theoretic Metrics with Data-Driven Occupancy Detection Approaches: A Comparative Analysis
المؤلف | Sayed, Aya Nabil |
المؤلف | Hamila, Ridha |
المؤلف | Himeur, Yassine |
المؤلف | Bensaali, Faycal |
تاريخ الإتاحة | 2023-04-04T09:09:11Z |
تاريخ النشر | 2022 |
اسم المنشور | 2022 5th International Conference on Signal Processing and Information Security, ICSPIS 2022 |
المصدر | Scopus |
الملخص | Building occupancy data helps increase energy management systems' performance, enabling lower energy use while preserving occupant comfort. The focus of this study is employing environmental data (e.g., including but not limited to temperature, humidity, carbon dioxide (CO2), etc.) to infer occupancy information. This will be achieved by exploring the application of information theory metrics with machine learning (ML) approaches to classify occupancy levels for a given dataset. Three datasets and six distinct ML algorithms were used in a comparative study to determine the best strategy for identifying occupancy patterns. It was determined that both k-nearest neighbors (kNN) and random forest (RF) identify occupancy labels with the highest overall level of accuracy, reaching 97.99% and 98.56%, respectively. 2022 IEEE. |
راعي المشروع | This paper was made possible by the Graduate Assistantship (GA) program provided from Qatar University (QU). The statements made herein are solely the responsibility of the authors. |
اللغة | en |
الناشر | IEEE |
الموضوع | Cross-Entropy Entropy Gini Impurity Index Information Gain Occupancy Detection |
النوع | Conference Paper |
الصفحات | 50-54 |
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