This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict total reactive power consumption in a household. This dataset contains 1440 time series obtained from the Individual household electric power consumption dataset from the UCI repository. The time series has 5 dimensions. This includes measurements for voltage, current annd 3 sub-metering energy usage.

Please refer to https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption for more details

Source Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France

[v2 update] weather data correction

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal: Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale, Dataset of an energy community's generation and consumption with appliance allocation, Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108590 (https://www.sciencedirect.com/science/article/pii/S2352340922007971)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

End-user profiles:

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households

https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption

https://site.ieee.org/pes-iss/data-sets/

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict total energy usage in kWh of a house. This dataset contains 138 time series obtained from the Appliances Energy Prediction dataset from the UCI repository. The time series has 24 dimensions. This includes temperature and humidity measurements of 9 rooms in a house, monitored with a ZigBee wireless sensor network. It also includes weather and climate data such as temperature, pressure, humidity, wind speed, visibility and dewpoint measured from Chievres airport. The data set is averaged for 10 minutes period and spanning 4.5 months.

Please refer to https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction for more details

Relevant papers
Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788

Citation request
Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788

Context

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

Content

Data Set Characteristics:

Multivariate, Time-Series, Regression

Number of Instances(Rows):

19735

Number of Attributes(Columns):

29

Associated Tasks:

Regression

Source:

Luis Candanedo, luismiguel.candanedoibarra '@' umons.ac.be, University of Mons (UMONS).

Data Set Information: Given in Metadata tab about the sources and collection methodology.

Attribute Information:

date time year-month-day hour:minute:second

Appliances, energy use in Wh (target variable for prediction)

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.

Acknowledgements

Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788, Web Link.

Citation

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Inspiration

1) This is a regression task, You should predict the "appliances" column. Column descriptions are given above. Please read them before proceeding. 2) Appropriate time series analysis with regression is preferred more. 3) Exploratory data analysis with charts and plots.

Have fun!