Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building
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- Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
- He, Zhiyuan & Hong, Tianzhen & Chou, S.K., 2021. "A framework for estimating the energy-saving potential of occupant behaviour improvement," Applied Energy, Elsevier, vol. 287(C).
- Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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- Aya Nabil Sayed & Faycal Bensaali & Yassine Himeur & Mahdi Houchati, 2023. "Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System," Energies, MDPI, vol. 16(5), pages 1-14, March.
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Keywords
occupancy detection; machine learning; CNN-XGboost; residential buildings; mechanical ventilation; indoor climate data;All these keywords.
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