redutils - R Energy Data Utilities

The R-package ‘redutils’ provides frequently used utility functions for the analysis and visualization of comfort and energy data in R. These functions reduce the complexity of the analysis task and allow a fast visualization of the data.

Installation

You can install the package from GitHub with:

#install.packages("devtools")
#library(devtools)

# close first all R Studio Projects
devtools::install_github("hslu-ige-laes/redutils")

The package is not (yet) available on CRAN.

Functions

getSeason()

Get the season name out of a date for filter and grouping purposes.

library(redutils)
x <- as.Date("2019-04-01")
getSeason(x)
#> [1] "Spring"

Default language is English. You can change that by passing the argument seasonlab:

library(redutils)
x <- as.Date("2019-04-01")
getSeason(x, seasonlab = c("Winter","Frühling","Sommer","Herbst"))
#> [1] "Frühling"

getTypEleConsHousehold()

Get a typical electricity consumption of a Swiss household in kWh/year. This is useful to compare a real dataset with a typical consumption value.

# single family house
library(redutils)
getTypEleConsHousehold(occupants=3,
                       rooms=5.5,
                       bldgType="single",
                       waterHeater="heatPump",
                       eleCommon="included")
#> [1] 5370

# flat in a multi family house
library(redutils)
getTypEleConsHousehold(occupants=3,
                       bldgType="multi",
                       freezer="none")
#> [1] 2900

Hint: varoius settings can get changed via function arguments.

Plots

plotEnergyConsBeforeAfter()

Plot a Graph with Energy Consumption per Month before/after an Optimization.

library(redutils)
data <- readRDS(system.file("sampleData/flatHeatingEnergy.rds", package = "redutils"))
plotSeasonalXYBeforeAfter(data, dateOptimization = "2017-09-01")

plotEnergyConsDailyProfileOverview()

Plot a Graph with Daily Energy Consumption Profiles by Weekday and Season.

library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotDailyProfilesOverview(data, locTimeZone = "Europe/Zurich")

plotDailyProfilesDecomposed()

Plot a Graph with Decomposed Daily Energy Consumption Profiles by Weekday. Decomposed means that the trend component (average of 2 week per default) is removed and only the seasonal component is showed. This allows an easier comparison.

library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotDailyProfilesDecomposed(data, locTimeZone = "Europe/Zurich")

plotHeatmapMedianWeeks()

Plot Heatmap of Median Energy Consumption by Hour, Weekdays and Seasons of Year.

library(redutils)
data <- readRDS(system.file("sampleData/eboBookEleMeter.rds", package = "redutils"))
plotHeatmapMedianWeeks(data, locTimeZone = "Europe/Zurich")

plotMollierHxDiagram()

Plot a D3 Mollier hx Diagram with scatter plot and comfort zone.

Hint: varoius settings can get changed via function arguments.

Package creation

Execute:

devtools::check()
devtools::build()

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Disclaimer
The authors decline any liability or responsibility in connection with the published documentation

© Lucerne University of Sciences and Arts, 2020