The number of occupants in a room is a crucial factor in determining the room’s energy consumption. By correctly estimating the number of occupants, energy use can be controlled, resulting in cost minimization. Temperature and light are two different sources of energy that are very useful for human beings and should be utilized properly. Sometimes, these energies are wasted for varying causes. People might forget to turn off the air conditioner or light, leading to waste of this energy. To overcome these problems, a room occupancy-based controlling system can play a vital role. Machine learning algorithms can be useful to correctly predict the number of occupants in a room from various features. Apart from light and temperature, CO2 level, PIR level, and sound can also contribute to finding the number of people present in the room. Machine learning techniques can be used along with IoT devices to automatically control the light or air conditioner. In this work, machine learning was applied in sensor readings to estimate the number of occupants in a room.
- Python
- Numpy
- Pandas
- Matplotlib
Room Occupancy Estimation (https://archive.ics.uci.edu/dataset/864/room+occupancy+estimation). 'Dataset' can also be found at Dataset folder.
You will find the codes of this project inside the "Codes" folder.
You need to download the datasets from corresponding source (please follow the 'Dataset' section for source) and keep them in a folder of your google drive. You will have to rename the folder and set the 'path' value according to your folder location in the drive.