Appliances Energy Prediction

Donated on 2/14/2017

Experimental data used to create regression models of appliances energy use in a low energy building.

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Regression

Feature Type

Real

# Instances

19735

# Features

28

Dataset Information

Additional 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 (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). For more information about the house, data collection, R scripts and figures, please refer to the paper and to the following github repository: https://github.com/LuisM78/Appliances-energy-prediction-data

Has Missing Values?

No

Introductory Paper

Data driven prediction models of energy use of appliances in a low-energy house

By L. Candanedo, V. Feldheim, Dominique Deramaix. 2017

Published in Energy and Buildings, Volume 140

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
dateFeatureDateno
AppliancesTargetIntegerWhno
lightsFeatureIntegerWhno
T1FeatureContinuousCno
RH_1FeatureContinuous%no
T2FeatureContinuousCno
RH_2FeatureContinuous%no
T3FeatureContinuousCno
RH_3FeatureContinuous%no
T4FeatureContinuousCno

0 to 10 of 29

Additional Variable Information

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 Chievres weather station), in Celsius Pressure (from Chievres weather station), in mm Hg RH_out, Humidity outside (from Chievres weather station), in % Wind speed (from Chievres weather station), in m/s Visibility (from Chievres weather station), in km Tdewpoint (from Chievres weather station), °C rv1, Random variable 1, nondimensional 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, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.

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Creators

Luis Candanedo

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