, Andreas Weigert
[aut] , Nicolai Weinig
[ctb], Thorsten Staake [aut]
| Title: | Residential Energy Consumption Data |
|---|---|
| Description: | Datasets with energy consumption data of different data measurement frequencies. The data stems from several publicly funded research projects of the Chair of Information Systems and Energy Efficient Systems at the University of Bamberg. |
| Authors: | Konstantin Hopf [aut, cre]
, Andreas Weigert
[aut] , Nicolai Weinig
[ctb], Thorsten Staake [aut]
|
| Maintainer: | Konstantin Hopf <[email protected]> |
| License: | CC BY-SA 4.0 |
| Version: | 1.1.0 |
| Built: | 2025-02-28 05:47:10 UTC |
| Source: | https://github.com/cran/ResidentialEnergyConsumption |
Electricity consumption of residential households in Switzerland for seven weeks. The data is provided as *kWh* measurements in 15-min intervals.
elcons_15minelcons_15min
A data frame with two types of variables:
VIDAn pseudonym for the household
V001, ..., V672Electricity consumption trace for one week in kWh
Ground truth data on housing type and heating information for the 15-minute smart meter dataset *elcons_15min*. The data was collected from customers of an electric utility company in Switzerland with a survey in 2018.
heatinginfo_15minheatinginfo_15min
A data frame with the following of variables:
VIDAn pseudonym for the household
household_typeThe housing type: *single family home* (detached house), *multi-family home* (multiple dwellings in one house), *semidetached house* and *teraced house* (multiple houses in a row)
heating_typeType of the heating system, either *electric heating*, *heat pump*, *heat pump and boiler*, or *other* (including gas, central heating in a multi-family home)
survey_WP_typeType of the heat pump, when a heat pump is installed, according to the survey response. Can be either *air*, *geothermal*, or *don't know*.
survey_WP_ageThe age of the heat pump according to the survey response. Can be either *<10 years*, *10-20 years*, *20-30 years*, *>30 years*, or *don't know*
Not all study participants answered the survey, thus, several rows of the table contain only *NA* values.
Data contains information about floor and roof spaces, as well as the energy demand for each individual household. For each household in *elcons_15min*, at least five nearest neighbors are available in this dataset. When there are more than five nearest neighbors, there are at least two core addresses from which the distances were calculated (e.g., 2 adresses means 10 nearest neighbors).
solarcadaster_featuressolarcadaster_features
A data frame with the following of variables:
VIDAn pseudonym for the household
neighbor_distanceEuclidean Distance to the corresponding neighbor
total_revenue_electricityTotal revenue of electricity of the household
floor_spaceThe floor space of the household in m2
roof_spaceThe roof space of the household in m2
roof_space_low_m2The roof space of the household in m2, which is classified as low solar potential
roof_space_medium_m2The roof space of the household in m2, which is classified as medium solar potential
roof_space_good_m2The roof space of the household in m2, which is classified as good solar potential
roof_space_verygood_m2The roof space of the household in m2, which is classified as very good solar potential
roof_space_excellent_m2The roof space of the household in m2, which is classified as excellent solar potential
roof_space_nThe number of different roof spaces of the household.
roof_space_lowThe roof space of the household in m2, which is classified as low solar potential
roof_space_mediumThe number of roof spaces of the household, which are classified as medium solar potential
roof_space_goodThe number of roof spaces of the household, which are classified as good solar potential
roof_space_verygoodThe number of roof spaces of the household, which are classified as very good solar potential
roof_space_excellentThe number of roof spaces of the household, which are classified as excellent solar potential
demand_hotwaterThe ernergy demand of the household for hot water per year
demand_heatingThe ernergy demand of the household for floor heating per year
Klauser, Daniel (2016). Solarpotentialanalyse für Sonnendach.ch - Schlussbericht. Bundesamt für Energie BFE, Schweiz. https://pubdb.bfe.admin.ch/de/publication/download/8196
Weather data from a weather station in a central location of the study region. The data contains hourly measurements over a period of ten weeks, similar to the time span of the dataset *elcons_15min*. Weather data are averaged across all available weather stations in the study area for each unit of time.
weather_dataweather_data
A data frame with the following of variables:
DATE_CETThe date and time of the weather observation in Central European Time
WEEKWeek of the year as decimal number (00–53) using Monday as the first day of week
WIND_DIRECTIONWind direction in compass degrees. *NA* when air is calm (no wind speed)
CLOUD_CEILINGLowest opaque layer with 5/8 or greater coverage
SKY_COVERSky cover: CLR-clear, SCT-scattered (1/8 to 4/8), BKN-broken (5/8 to 7/8), OVC-overcast, OBS-obscured, POB-partial obscuration
VISIBILITYVisibilityin statute miles (rounded to nearest tenth)
TEMPTemperature measured in fahrenheit
SEA_LEVEL_PRESSURESea level pressure measured in millibars (rounded to nearest tenth)
STATION_PRESSUREStation pressure measured in millibars (rounded to nearest tenth)
PCP011-hour liquid precip reportin inches and hundredths, that is, the precip for the preceding 1-hour period
WIND_SPEEDWind speed in miles per hour
This data cannot be used or redistributed for commercial purposes. Re-distribution of these data by others must provide this same notification. (see https://www.ncdc.noaa.gov/)
NOAA National Centers for Environmental Information (2020)
data(elcons_15min, weather_data) #transform 15-minute electricity measurements to hourly consumption values hourly_cons <- colSums(matrix(t(elcons_15min$w44[1,2:673]), nrow=4)) #select temperature observations for week 44 hourly_temp <- weather_data[weather_data$WEEK==44,"TEMP"] #compute correlation cor(hourly_cons, hourly_temp)data(elcons_15min, weather_data) #transform 15-minute electricity measurements to hourly consumption values hourly_cons <- colSums(matrix(t(elcons_15min$w44[1,2:673]), nrow=4)) #select temperature observations for week 44 hourly_temp <- weather_data[weather_data$WEEK==44,"TEMP"] #compute correlation cor(hourly_cons, hourly_temp)