Machine Learning or Predictive Models in IoT - Energy Prediction Use Case

In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
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What will you learn

  • Multiple linear regression,
  • Support vector machine with radial kernel,
  • Random forest and
  • Gradient boosting machines (GBM).
  • Use of statistical models with repeated cross validation and evaluated in a testing set

Project Description

This IoT project presents and discusses data-driven predictive models for the energy use of appliances. Data used include measurements of temperature and humidity sensors from a wireless network, whether from a nearby airport station and recorded energy use of lighting fixtures. The machine learning project discusses data filtering to remove non-predictive parameters and feature ranking. The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru) and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non-predictive attributes (parameters). 

Curriculum For This Mini Project

 
  Introduction to Problem Statement
05m
  Dataset Overview
08m
  Data PreProcessing
00m
  Import Libraries
02m
  Format Date
14m
  Identify Missing Values
02m
  Plotting univariate features
09m
  Plotting bivariate features
06m
  Identify Outliers
12m
  Visualize energy usage
16m
  Summary statistics using DPLYR
10m
  Heat map for usage pattern
33m
  Recap
02m
  Model Data Preparation
03m
  Correlations Table
07m
  Feature Selection using Boruta
09m
  Adding Dummy Variables to model
01m
  Create Model using RFE control
08m
  List chosen Features for prediction
02m
  Training the Model
04m
  Training the SVM and RF Model
03m
  Conclusion
01m