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 . . .

     

    Logo ScioWire Beta black

    If you want to have access to all the content you need to log in!

    Thanks :)

    If you don't have an account, you can create one here.

     

Scroll to Top

Add A Knowledge Base Question !

+ = Verify Human or Spambot ?