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Generate a Standard Load Profile for Electricity

Source: R/slp_generate.R
slp_generate.Rd

Generate a standard load profile in watts, normalised to an annual consumption of 1,000 kWh.

Usage

slp_generate(
  profile_id,
  start_date,
  end_date,
  holidays = NULL,
  state_code = deprecated()
)

Source

https://www.bdew.de/energie/standardlastprofile-strom/

https://www.bdew.de/media/documents/1999_Repraesentative-VDEW-Lastprofile.pdf

https://www.bdew.de/media/documents/2000131_Anwendung-repraesentativen_Lastprofile-Step-by-step.pdf

Arguments

profile_id

load profile identifier, required

start_date

start date in ISO 8601 format, greater than or equal to "1990-01-01", required

end_date

end date in ISO 8601 format, no later than "2073-12-31", required

holidays

an optional character or Date vector of dates in ISO 8601 format ("YYYY-MM-DD") that are treated as public holidays (and therefore mapped to "sunday" in the algorithm). When supplied, the built-in holiday data are ignored entirely and only the dates in holidays are used.

state_code

[Deprecated] Use holidays instead.

Value

A data.frame with four variables:

  • profile_id, character, load profile identifier

  • start_time, POSIXct / POSIXlt, start time

  • end_time, POSIXct / POSIXlt, end time

  • watts, numeric, average electric power in watts per 15-minute interval, normalised to an annual consumption of 1,000 kWh

Details

In the German electricity market, a standard load profile is a representative pattern of electricity consumption used to forecast demand for customer groups that are not continuously metered. For each distinct combination of profile_id, period, and day there are 96 quarter-hourly measurements of electrical power, normalised to an annual consumption of 1,000 kWh. This function supports data from 1990 to 2073.

See vignette("standardlastprofile") for more details about the algorithm.

Profile IDs

There are 16 profile IDs across two generations:

1999 profiles:

  • H0: Households

  • G0, G1, G2, G3, G4, G5, G6: Commercial

  • L0, L1, L2: Agriculture

2025 profiles

In 2025, BDEW published an updated set of standard load profiles reflecting changes in electricity consumption patterns since the original 1999 study. Five new profiles are included:

  • H25: households — updated version of H0

  • G25: commerce (general) — updated version of G0

  • L25: agriculture — updated version of L0

  • P25: combination profile for households with a photovoltaic (PV) system

  • S25: combination profile for households with a PV system and battery storage

For descriptions of each profile, call slp_info().

Periods and day types

1999 profiles use three seasonal periods:

  • summer: May 15 to September 14

  • winter: November 1 to March 20

  • transition: March 21 to May 14, and September 15 to October 31

2025 profiles use calendar months (january … december) instead of seasons.

Within each period, days are classified as:

  • workday: Monday to Friday

  • saturday: Saturdays; Dec 24th and Dec 31st are also treated as Saturdays unless they fall on a Sunday

  • sunday: Sundays and all public holidays

Public holidays

By default, the following nine public holidays observed nationwide across all German states are treated as Sundays:

  • New Year's Day (1 January)

  • Good Friday

  • Easter Monday

  • Labour Day (1 May)

  • Ascension Day

  • Whit Monday

  • German Unity Day (3 October)

  • Christmas Day (25 December)

  • Boxing Day (26 December)

State-level holidays are not included by default. These vary by state and can change — for example, Berlin observed a one-time holiday on 8 May 2025 (end of World War II anniversary). Use the holidays argument to supply your own dates instead; the built-in data are then ignored entirely.

Units and conversion

The 1999 source file stores values in watts (W), normalised to 1,000 kWh/a. The 2025 source file stores values in kWh per 15-minute interval, normalised to 1,000,000 kWh/a. To keep all profiles consistent, the 2025 values are converted to watts normalised to 1,000 kWh/a.

To convert to energy consumed per interval in kWh:

kwh <- out$watts / 4 / 1000

Examples

start <- "2026-01-01"
end <- "2026-12-31"

# multiple profile IDs are supported
L <- slp_generate(c("L0", "L1", "L2"), start, end)
head(L)
#>   profile_id          start_time            end_time watts
#> 1         L0 2026-01-01 00:00:00 2026-01-01 00:15:00  68.3
#> 2         L0 2026-01-01 00:15:00 2026-01-01 00:30:00  66.0
#> 3         L0 2026-01-01 00:30:00 2026-01-01 00:45:00  64.3
#> 4         L0 2026-01-01 00:45:00 2026-01-01 01:00:00  63.0
#> 5         L0 2026-01-01 01:00:00 2026-01-01 01:15:00  62.1
#> 6         L0 2026-01-01 01:15:00 2026-01-01 01:30:00  61.4

# supply custom holiday dates (e.g. only treat New Year's Day as a holiday)
H0_custom <- slp_generate("H0", start, end, holidays = "2026-01-01")

# Fetch state-level holidays from the nager.Date API and pass them in.
# Each entry in the API response contains two relevant fields:
#   $global  — logical; TRUE = nationwide holiday, FALSE = state-specific
#   $counties — list of ISO 3166-2 state codes (e.g. "DE-BE" for Berlin)
#               when global is FALSE; NULL otherwise
#
# Berlin (DE-BE) observes International Women's Day (March 8) in addition
# to all nationwide holidays. The example below fetches 2027 holidays,
# keeps entries where global is TRUE or "DE-BE" appears in counties, and
# passes the resulting dates to slp_generate().
if (FALSE) { # \dontrun{
resp <- httr2::request("https://date.nager.at/api/v3") |>
  httr2::req_url_path_append("PublicHolidays", "2027", "DE") |>
  httr2::req_perform() |>
  httr2::resp_body_json()

is_berlin <- \(x) isTRUE(x$global) || "DE-BE" %in% unlist(x$counties)
holidays_berlin_2027 <- as.Date(
  vapply(Filter(is_berlin, resp), \(x) x$date, character(1))
)

H0_berlin_2027 <- slp_generate(
  "H0", "2027-01-01", "2027-12-31",
  holidays = holidays_berlin_2027
)
} # }

# consider only nationwide public holidays (default)
H0_2026 <- slp_generate("H0", start, end)

# when the deprecated state_code and holidays are both supplied, both sets
# of dates are treated as Sundays: user-provided dates from holidays and
# state-specific built-in holidays from state_code are merged
suppressWarnings(
  slp_generate("G0", "2026-04-01", "2026-04-01",
    state_code = "SL", holidays = "2026-04-01") |>
    head()
)
#>   profile_id          start_time            end_time watts
#> 1         G0 2026-04-01 00:00:00 2026-04-01 00:15:00  68.3
#> 2         G0 2026-04-01 00:15:00 2026-04-01 00:30:00  66.5
#> 3         G0 2026-04-01 00:30:00 2026-04-01 00:45:00  64.6
#> 4         G0 2026-04-01 00:45:00 2026-04-01 01:00:00  62.6
#> 5         G0 2026-04-01 01:00:00 2026-04-01 01:15:00  60.3
#> 6         G0 2026-04-01 01:15:00 2026-04-01 01:30:00  57.9

# electric power values are normalised to consumption of ~1,000 kWh/a
sum(H0_2026$watts / 4 / 1000)
#> [1] 998.1163

# convert watts to kWh per interval using a wrapper
slp_generate_kwh <- \(...) {
  out <- slp_generate(...)
  out$kwh <- out$watts / 4 / 1000
  out
}
H0_kwh <- slp_generate_kwh("H0", start, end)
head(H0_kwh)
#>   profile_id          start_time            end_time     watts        kwh
#> 1         H0 2026-01-01 00:00:00 2026-01-01 00:15:00 108.67764 0.02716941
#> 2         H0 2026-01-01 00:15:00 2026-01-01 00:30:00 100.72864 0.02518216
#> 3         H0 2026-01-01 00:30:00 2026-01-01 00:45:00  93.15226 0.02328806
#> 4         H0 2026-01-01 00:45:00 2026-01-01 01:00:00  85.82428 0.02145607
#> 5         H0 2026-01-01 01:00:00 2026-01-01 01:15:00  78.74471 0.01968618
#> 6         H0 2026-01-01 01:15:00 2026-01-01 01:30:00  72.28615 0.01807154