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Appliances Energy Prediction

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Experimental data used to create regression models of appliances energy use in a low energy building. 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
Subject Area
Computer Science
Instances
19,735
Features
29
Data Types
Multivariate
Tasks
Regression
Feature Types
Continuous

Features

NameRoleTypeUnitsMissing Values

Introductory Paper

Data driven prediction models of energy use of appliances in a low-energy house
L. Candanedo, V. Feldheim, Dominique Deramaix. 2017.
Energy and Buildings, Volume 140

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Authors
Luis Candanedo
Year Created
2017
License
CC BY 4.0