Univariate-Time-Series-Prediction-using-Deep-Learning_PyTorch/data

Variable Description
====================

date time year-month-day hour:minute:second 
Appliances, energy use in Wh
lights, energy use of light fixtures in the house in Wh
T1, Temperature in kitchen area, in Celsius
RH_1, Humidity in kitchen area, in %
T2, Temperature in living room area, in Celsius
RH_2, Humidity in living room area, in %
T3, Temperature in laundry room area
RH_3, Humidity in laundry room area, in %
T4, Temperature in office room, in Celsius
RH_4, Humidity in office room, in %
T5, Temperature in bathroom, in Celsius
RH_5, Humidity in bathroom, in %
T6, Temperature outside the building (north side), in Celsius
RH_6, Humidity outside the building (north side), in %
T7, Temperature in ironing room , in Celsius
RH_7, Humidity in ironing room, in %
T8, Temperature in teenager room 2, in Celsius
RH_8, Humidity in teenager room 2, in %
T9, Temperature in parents room, in Celsius
RH_9, Humidity in parents room, in %
To, Temperature outside (from Chièvres weather station), in Celsius
Pressure (from Chièvres weather station), in mm Hg
RH_out, Humidity outside (from Chièvres weather station), in %
Windspeed (from Chièvres weather station), in m/s
Visibility (from Chièvres weather station), in km
Tdewpoint (from Chièvres weather station), °C
rv1, Random variable 1, nondimensional
rv2, Rnadom variable 2, nondimensional

Where indicated, data from the nearest airport weather station (Chièvres Airport, Belgium) was downloaded from a public data set from 
Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4 months of data.