https://github.com/HanYangZhao/SmartMeterPrediction

Home energy usages prediction based on neural networks and smart meter data
https://github.com/HanYangZhao/SmartMeterPrediction

docker energy-consumption energy-monitor keras machine-learning neural-network tensorflow

Last synced: 3 months ago
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Home energy usages prediction based on neural networks and smart meter data

• Host: GitHub
• URL: https://github.com/HanYangZhao/SmartMeterPrediction
• Owner: HanYangZhao
• Created: 2018-04-21T02:16:15.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-04-21T05:20:09.000Z (about 6 years ago)
• Last Synced: 2024-01-29T17:14:29.332Z (5 months ago)
• Topics: docker, energy-consumption, energy-monitor, keras, machine-learning, neural-network, tensorflow
• Language: Python
• Homepage:
• Size: 8.73 MB
• Stars: 20
• Watchers: 3
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome-advanced-metering-infrastructure - Smart meter Prediction - HydroQuebec Home energy usages prediction based on machine learning and smart meter data (Python / General-Purpose Machine Learning)

README

        

# SmartMeterPrediction - HydroQuebec

Home energy usages prediction based on machine learning and smart meter data

![Demo](https://github.com/HanYangZhao/SmartMeterPrediction/blob/master/screenshot.png)

# Getting Started

  * Go to the docker folder

  * Build the docker containers in the sub-folder

  * In `docker-compose.yml` add the necessary informations

  * `docker-compose up`

  * Graphana setup

    * go to `localhost:3000`

    * add the data source

    * url : http://influxdb:8086

    * Use Proxy setting

    * Database name : e

    * import `energy_usage.json` as a new dashboard

  * A new prediction should be made everyday 

# Training

  ## Pre-reqs

  * `pip install -r requirements.txt` in the training folder

  * tensorflow-gpu is strongly recommended if you have the hardware

  ## Multiple linear regression

  * Download your house's hourly datasets from Hydro-Quebec

  * Put them all in the the `./training/data/hourly` folder

  * Run the `cvs_hour_processing.py` script. This should create a new csv file.

    Make sure that there are no hours with no energy usage in the data set

  * Go to the `multi_linear_regression_hourly.py` and change `line 20` to point the new csv created

  * Run `multi_linear_regression_hourly.py`

  * Uses the temperature, hour of the day and is_workday to make predictions and train the network

  * ![DNN](https://github.com/HanYangZhao/SmartMeterPrediction/blob/master/results/hourly/dnn_3_layers_64_neurons_00_dropout.png)

 ## LSTM network

  * Download your house's hourly datasets from Hydro-Quebec

  * Put them all in the the `./training/data/hourly` folder

  * Run the `cvs_hour_processing.py` script

  * Make sure that there are no hours with no energy usage in the data set

  * Go to the `lstm_hourly.py` and change `line 43` to point the new csv created

  * You can adjust what parameters we want to use for training by changing `line 36` and the numbers of prediction hours in       `line 39 - line 41`

  * Uses the temperature, hour of the day previous predictions and is_workday to generate new predictions and train the network

  * NOTE: I've currently commented out in the LSTM prediction because it's not very accurate. To re-enable you need to remove the comments in `./predictor/main.py` and `./predictor/database.py`

  * ![LSTM](https://github.com/HanYangZhao/SmartMeterPrediction/blob/master/results/hourly/lstm_6_layers_100_neurons_02_dropout.png)