Generate a standard load profile

Converting the output to kWh

The values returned by slp_generate() represent average electric power during each 15-minute interval. To obtain the energy consumed during that interval in kWh you can wrap slp_generate() once:

slp_generate_kwh <- \(...) {
  out <- slp_generate(...)
  out$kwh <- out$watts / 4 / 1000
  out
}

H0_kwh <- slp_generate_kwh("H0", "2026-01-01", "2026-12-31")
sum(H0_kwh$kwh)
#> [1] 998.1163

Generate a date sequence

In the initial step, a date sequence is created from start_date to end_date based on the user input. Here’s a simple example:

start <- as.Date("2023-12-22")
end <- as.Date("2023-12-27")

(date_seq <- seq.Date(start, end, by = "day"))
#> [1] "2023-12-22" "2023-12-23" "2023-12-24" "2023-12-25" "2023-12-26"
#> [6] "2023-12-27"

Map each day to a period and a weekday

The measured load profiles analysed in the study showed that electricity consumption across all groups fluctuates both over the period of a year and over the days within a week. For the 1999 profiles, the period definition is:

• summer: May 15 to September 14
• winter: November 1 to March 20
• transition: March 21 to May 14, and September 15 to October 31

For the 2025 profiles, each calendar month is treated as its own period (january … december) rather than grouping months into seasons.

The 1999 study also found no significant difference in consumption on weekdays from Monday to Friday for any group. For this reason, the days Monday to Friday are grouped together as workday. December 24th and 31st are considered Saturdays too if they are not Sundays. Public holidays are regarded as Sundays.

Note: The function slp_generate() supports by default nationwide public holidays for Germany. Those were retrieved from the nager.Date API:

• New Year’s (Jan 1)
• Good Friday
• Easter Monday
• Labour Day (May 1)
• Ascension Day
• Whit Monday
• German Unity Day (Oct 3)
• Christmas Day (Dec 25)
• Boxing Day (Dec 26)

State-level holidays are not included by default, as these vary by state and can change over time. Use the optional holidays argument to pass your own vector of dates and take full control over which dates are treated as public holidays; the built-in data are then ignored entirely. See the README for an example of how to fetch state-level holidays from the nager.Date API and pass them to slp_generate().

The result of this second step is a mapping from each date to a so-called characteristic profile day, i.e. a combination of weekday and period:

wkday_period <- standardlastprofile:::get_wkday_period(date_seq)
data.frame(input = date_seq, output = wkday_period)
#>        input          output
#> 1 2023-12-22  workday_winter
#> 2 2023-12-23 saturday_winter
#> 3 2023-12-24   sunday_winter
#> 4 2023-12-25   sunday_winter
#> 5 2023-12-26   sunday_winter
#> 6 2023-12-27  workday_winter

Assign consumption values to each day

The third step is to assign the measurements we know from the slp dataset to each characteristic profile day. This is the job of the slp_generate() function:

G5 <- slp_generate(
  profile_id = "G5",
  start_date = "2023-12-22",
  end_date = "2023-12-27"
  )

This function returns a data frame with 4 columns:

head(G5)
#>   profile_id          start_time            end_time watts
#> 1         G5 2023-12-22 00:00:00 2023-12-22 00:15:00  50.1
#> 2         G5 2023-12-22 00:15:00 2023-12-22 00:30:00  47.4
#> 3         G5 2023-12-22 00:30:00 2023-12-22 00:45:00  44.9
#> 4         G5 2023-12-22 00:45:00 2023-12-22 01:00:00  43.3
#> 5         G5 2023-12-22 01:00:00 2023-12-22 01:15:00  43.0
#> 6         G5 2023-12-22 01:15:00 2023-12-22 01:30:00  43.8

The data analysis revealed that load fluctuations for both commercial and agricultural customers remain moderate throughout the year. Specifically, for customers and customer groups labelled as G0 to G6 and L0 to L2, the standard load profile can be accurately derived from the nine characteristic profile day combinations (3 day types × 3 seasonal periods) available in the dataset slp.

Below is the code snippet from the README, which can be used to reproduce the plot for the G5 profile, showcasing the algorithm’s outcome:

library(ggplot2)
ggplot(G5, aes(start_time, watts)) +
  geom_line(color = "#0CC792") +
  scale_x_datetime(
    date_breaks = "1 day",
    date_labels = "%b %d") +
  scale_y_continuous(NULL, labels = \(x) paste(x, "W")) +
  labs(
    title = "'G5': bakery with bakehouse",
    subtitle = "1/4h measurements, based on consumption of 1,000 kWh/a",
    caption = "data: www.bdew.de",
    x = NULL) +
  theme_minimal() +
  theme(
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    panel.grid = element_line(
      linetype = "12",
      lineend = "round",
      colour = "#FAF6F4"
      )
  ) +
  NULL

As you can see, the values in 2023 for December 24 (a Sunday) and December 25 and 26 (both public holidays) are identical.

Special case: H0, H25, P25, S25

In contrast to most commercial and agricultural businesses, which have a relatively even and constant electricity consumption throughout the year, household electricity consumption decreases from winter to summer and vice versa (at least in Germany). Because of the distinctive annual load profile characteristics of household customers, we contend that these customers cannot be adequately described through a static representation using characteristic days alone. Consequently, the values in the slp dataset for H0, H25, P25, and S25 serve as base values to be scaled by a dynamization factor.

This is taken into account when you call slp_generate(). The study suggested the application of a 4th order polynomial function to the values of these profiles.

\[ w_d = w_s \times(-3.92\mathrm{e}{-10} \times d^4 + 3.20\mathrm{e}{-7} \times d^3 - 7.02\mathrm{e}{-5} \times d^2 + 2.10\mathrm{e}{-3} \times d + 1.24) \] Where:

• \(w_d\) is the resulting ‘dynamic’ value
• \(w_s\) is the ‘static’ value
• \(d\) is the day of the year as integer, starting at 1 on January 1st

The following plot shows how the electrical power develops over the year for profile H0; for a clearer picture, the values are aggregated at daily level:

This dynamization step produces a representative, dynamic load profile. Finally, the following chart compares the dynamic values with their static counterparts.⁶