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This is a project for house appliances' load forecasting, using 3 different models:

A simple linear regression A SVM-based regression model A decision trees (random forest)

as data used the house's temperature, humidity and weather conditions from UCI. DOWNLOAD THE DATA SET HERE:

The project includes:

  1. Feature Engineering
  2. Train 3 different models on the data
  3. Model Evaluation & Selection
  4. Parameter Tuning
  5. Model Persistence

Data Set Information

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 (, 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).

Attribute Information

  1. Date time year-month-day hour:minute:second
  2. Appliances, energy use in Wh
  3. Lights, energy use of light fixtures in the house in Wh
  4. T1, Temperature in kitchen area, in Celsius
  5. RH_1, Humidity in kitchen area, in %
  6. T2, Temperature in living room area, in Celsius
  7. RH_2, Humidity in living room area, in %
  8. T3, Temperature in laundry room area
  9. RH_3, Humidity in laundry room area, in %
  10. T4, Temperature in office room, in Celsius
  11. RH_4, Humidity in office room, in %
  12. T5, Temperature in bathroom, in Celsius
  13. RH_5, Humidity in bathroom, in %
  14. T6, Temperature outside the building (north side), in Celsius
  15. RH_6, Humidity outside the building (north side), in %
  16. T7, Temperature in ironing room , in Celsius
  17. RH_7, Humidity in ironing room, in %
  18. T8, Temperature in teenager room 2, in Celsius
  19. RH_8, Humidity in teenager room 2, in %
  20. T9, Temperature in parents room, in Celsius
  21. RH_9, Humidity in parents room, in %
  22. To, Temperature outside (from Chievres weather station), in Celsius
  23. Pressure (from Chievres weather station), in mm Hg
  24. RH_out, Humidity outside (from Chievres weather station), in %
  25. Wind speed (from Chievres weather station), in m/s
  26. Visibility (from Chievres weather station), in km
  27. Tdewpoint (from Chievres weather station), °C
  28. rv1, Random variable 1, nondimensional
  29. rv2, Random variable 2, nondimensional

Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.


Linear, SVR and Random Forest models to predict house's appliances energy Load




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