PyTorch LSTM occupancy detection model

Long Short Term Memory occupancy detection in smart homes using indoor climate data

Paper

To read about this project: LSTM for occupancy detection in smart homes using indoor climate data

Datasets from the paper

• Occupancy detection dataset
• Room climate dataset

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Result

LSTM predictions on the Occupancy detection dataset

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How to run it

Run code

1. Choose a notebook from Github
2. Press the button then run it

Or

1. Go to Google Colab and sign in
2. Open "Open Notebook" then go to "GitHub" tab and then search for "Linkanblomman" and choose repository "Linkanblomman/LSTM_occupancy_detection"
3. Pick the notebook and run it

Run on a Raspberry Pi 3

1. Download the LSTM_Raspberry_Pi directory to your Raspberry Pi 3
2. Follow this guide: A Step by Step guide to installing PyTorch in Raspberry Pi
3. Get access to "raspberry_pi_files" as in the folder structure (image below): run Raspberry_Pi_3_Occupancy_Detection_Dataset_LSTM_model.ipynb
4. Extract the zip file from step 3 into the LSTM_Raspberry_Pi directory as in the folder structure (image below)
5. Pip install necessary modules
6. Run code with the command "Python3 lstm__for_raspberrypi.py"

NOTE!

Before running the code in Google Colab.

GPU usage (change CPU to GPU): Runtime -> Change runtime type -> Hardware accelerator -> GPU -> Save

If you get errors when running Raspberry_Pi_3_Occupancy_Detection_Dataset_LSTM_model.ipynb:

1. Press the "RESTART RUNTIME"
2. Run again: Runtime -> Run all
3. Still getting errors then repeat step 1 and 2 again.