지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.18 No.1
- 발행연도
- 2024.3
- 수록면
- 10 - 18 (9page)
- DOI
- 10.5626/JCSE.2024.18.1.10
이용수
초록· 키워드
오류제보하기
To improve energy efficiency, understanding occupant behavior is crucial for adaptive temperature control and optimal electronic device usage. Our study introduces a room occupancy detection system using machine learning and Internetof-Things sensors to predict occupant behavior patterns. Initially, indoor IoT sensor devices are installed to observe occupant behavior, and datasets are generated from sensor data, including temperature, humidity, light, and CO₂ levels, in both occupied and vacant rooms. The collected dataset undergoes analysis through a machine learning-based model designed to classify room occupancy. First, the timestamp features, extracted from date-time data, such as time of day and part of the day, are extracted. ANOVA feature selection is applied to identify five crucial features. Ultimately, the random forest model is employed to classify room occupancy based on the selected features. Results indicate that our proposed model significantly outperforms other models—achieving improvements of up to 99.713%, 99.467%, 99.676%, 99.676%, and 99.571% in accuracy, precision, recall, specificity, and F1-score, respectively. The trained model holds potential for integration into web-based systems for real-time applications. This predictive model is poised to contribute to the optimization of electronic device efficiency within a room or building by continuously monitoring real-time room conditions.
목차
Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
IV. RESULT AND DISCUSSION
V. CONCLUSION
REFERENCES
참고문헌 (24)
참고문헌 신청
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L. M. Candanedo and V. Feldheim, “Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models,” Energy and Buildings, vol. 112, pp. 28-39, 2016. https://doi.org/10.1016/j.enbuild.2015.11.071
Y. Himeur, K. Ghanem, A. Alsalemi, F. Bensaali, and A. Amira, “Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives,” Applied Energy, vol. 287, article no. 116601, 2021. https://doi.org/10.1016/j.apenergy.2021.116601
D. Yoo, S. Chung, and J. Park, “Analysis and evaluation of channel-hopping-based MAC in industrial IoT environment,” Journal of Computing Science and Engineering, vol. 15, no. 4, pp. 160-174, 2021. http://dx.doi.org/10.5626/JCSE.2021.15.4.160
N. S. Sani, I. I. S. Shamsuddin, S. Sahran, A. H. A. Rahman, and E. N. Muzaffar, “Redefining selection of features and classification algorithms for room occupancy detection,” International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, pp. 1486-1493, 2018. https://doi.org/10.18517/ijaseit.8.4-2.6826
L. M. Candanedo and V. Feldheim, “Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models,” Energy and Buildings, vol. 112, pp. 28-39, 2016. https://doi.org/10.1016/j.enbuild.2015.11.071
Y. Himeur, K. Ghanem, A. Alsalemi, F. Bensaali, and A. Amira, “Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives,” Applied Energy, vol. 287, article no. 116601, 2021. https://doi.org/10.1016/j.apenergy.2021.116601
D. Yoo, S. Chung, and J. Park, “Analysis and evaluation of channel-hopping-based MAC in industrial IoT environment,” Journal of Computing Science and Engineering, vol. 15, no. 4, pp. 160-174, 2021. http://dx.doi.org/10.5626/JCSE.2021.15.4.160
N. S. Sani, I. I. S. Shamsuddin, S. Sahran, A. H. A. Rahman, and E. N. Muzaffar, “Redefining selection of features and classification algorithms for room occupancy detection,” International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, pp. 1486-1493, 2018. https://doi.org/10.18517/ijaseit.8.4-2.6826
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