Indoor occupancy detection based on environmental data using cnn-xgboost model: experimental validation in a residential building

HIGHLIGHTS

• who: Abolfazl Mohammadabadi et al. from the Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy have published the research: Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building, in the Journal: Sustainability 2022, 14644 of 20/Dec/2022
• what: The analysis showed that the method had a detection accuracy of 90% in real time and 85% accuracy in occupancy forecast. To fill this gap, the authors propose an approach that is inexpensive, accurate, easy to install, and fast. The authors propose a novel ML model . . .
  

   

  

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