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
By L. Candanedo, V. Feldheim, Dominique Deramaix. 2017
Published in Energy and Buildings, Volume 140
Variables Table
Variable Name | Role | Type | Description | Units | Missing Values |
---|---|---|---|---|---|
date | Feature | Date | no | ||
Appliances | Target | Integer | Wh | no | |
lights | Feature | Integer | Wh | no | |
T1 | Feature | Continuous | C | no | |
RH_1 | Feature | Continuous | % | no | |
T2 | Feature | Continuous | C | no | |
RH_2 | Feature | Continuous | % | no | |
T3 | Feature | Continuous | C | no | |
RH_3 | Feature | Continuous | % | no | |
T4 | Feature | Continuous | C | no |
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.
Dataset Files
File | Size |
---|---|
energydata_complete.csv | 11.4 MB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset appliances_energy_prediction = fetch_ucirepo(id=374) # data (as pandas dataframes) X = appliances_energy_prediction.data.features y = appliances_energy_prediction.data.targets # metadata print(appliances_energy_prediction.metadata) # variable information print(appliances_energy_prediction.variables)
Candanedo, L. (2017). Appliances Energy Prediction [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5VC8G.
Creators
Luis Candanedo
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.