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I2OT_energy

I2OT Hackathon

The goal of this project is to use energy data to display the energy flow in a house. Furthermore, we want to use the data to predict the energy consumption of the house.

Requirements

In order to run the project, you need to install the required packages by running the following command:

pip install -r requirements.txt

Frameworks and libraries

The project is based on the following frameworks and libraries:

  • influxdb_client
  • pandas
  • paho.mqtt
  • logging
  • Prognose

Getting started

In order to run the project, you need to install the required libraries, e.g. set up an environment using python -m venv /path/to/new/virtual/environment. Afterwards, start the influxdb server and the mqtt broker. The mqtt data is stored in the influxdb database and thus, a docker container has to be started to run the mqtt_service.py file. The docker image has to be created by running the following command in the terminal when in this folder: sudo docker build -t mqtt-image . Define the name of the image by replacing mqtt-image. The docker container can be started by running the following command: run sudo docker run --network host -d -e INFLUXDB_TOKEN=TOKEN -it --rm mqtt-image Replace the token of the influxdb database instead of TOKEN. Omit -d if you want to see the output of the mqtt_service.py file. Since the logging library is used, the output is also stored in the log file. The log file is stored in the logs folder (the files may be invisible on IOS systems- use ls -la in the terminal to display them).

Additional information

The data, i.e. the influxdb server, is stored on a Raspberry Pi 4. The repository is pulled to the Raspberry Pi and the project is run, i. e. the docker image is started etc., on the raspberry pi. The raspberry pi is connected to the local network and thus, can be accessed by other devices in the network via ssh pi@IPADDR (replace IPADDR with the IP address of the Raspberry Pi in the local network).

Database

The data of the mqtt broker is stored in the influxdb database. The database is used for time series data.

Visualization

The data is visualized using Grafana.

Prognosis

Both a CNN and an LSTM were used to create a prognosis on future energy data. Some of the results are displayed below: 15min prediction lstm 15min prediction CNN entire day prediction CNN

License

MIT License

Copyright (c) [2023] [IO2T Energy]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.