Standard load profiles (SLPs) for electricity and gas, published by the German Association of Energy and Water Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.). SLPs are used by utilities, distribution network operators, and the energy industry to forecast demand for customer groups that are not continuously metered.
install.packages("standardlastprofile")slp_info()— descriptions for all electricity and gas profile IDs
Electricity
slp_electricity_profiles— dataset of BDEW electricity SLPs in tidy formatslp_electricity()— generate a 15-minute profile for any date range
Gas
slp_gas()— generate daily gas consumption via the SigLinDe methodslp_gas_coefficients()— retrieve SigLinDe coefficients for gas SLPsslp_gas_kundenwert()— derive the customer value (German: “Kundenwert”) from a reference temperature seriesslp_gas_siglinde()— low-level SigLinDe function, can be useful for custom or region-specific SigLinDe coefficientsslp_gas_weekday_factors()— retrieve weekday factors for gas SLPs
The dataset slp_electricity_profiles contains 26,784 observations
across 5 variables:
profile_id: load profile identifierperiod:"summer","winter", or"transition"for 1999 profiles; a lowercase month name for 2025 profilesday:"workday","saturday", or"sunday"timestamp: quarter-hour start time in"%H:%M"formatwatts: average electric power, normalised to 1,000 kWh/a
str(slp_electricity_profiles)
#> 'data.frame': 26784 obs. of 5 variables:
#> $ profile_id: chr "H0" "H0" "H0" "H0" ...
#> $ period : chr "winter" "winter" "winter" "winter" ...
#> $ day : chr "saturday" "saturday" "saturday" "saturday" ...
#> $ timestamp : chr "00:00" "00:15" "00:30" "00:45" ...
#> $ watts : num 70.8 68.2 65.9 63.3 59.5 55 50.5 46.6 43.9 42.3 ...Based on an analysis of 1,209 load profiles of low-voltage electricity consumers in Germany1:
H0: householdsG0–G6: commercialL0–L2: agriculture
An updated set published by BDEW in 2025, reflecting changes in consumption patterns since the original study. Unlike the 1999 profiles (three seasonal periods), the 2025 profiles provide values for each calendar month:
H25,G25,L25: updated household, commercial, and agriculture profilesP25: households with a photovoltaic (PV) systemS25: households with a PV system and battery storage
The chart below compares cumulative energy consumption of the 2025
household profiles against H0 over a full year. H25 tracks H0
closely; P25 and S25 flatten from spring through summer as solar
generation and storage reduce grid draw.
slp_electricity() returns a data frame with one row per 15-minute
interval:
G5 <- slp_electricity(
profile_id = "G5",
start_date = "2023-12-22",
end_date = "2023-12-27"
)
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
Both slp_electricity() and slp_gas() use the same holiday logic:
nine nationwide German public holidays are treated as Sundays by default
(New Year’s, Good Friday, Easter Monday, Labour Day, Ascension Day, Whit
Monday, German Unity Day, Christmas Day, Boxing Day). State-level
holidays are not included because they vary by state and can change. Use
the holidays argument in either function to supply your own dates —
the built-in data are then ignored entirely:
library(httr2)
resp <- request("https://date.nager.at") |>
req_url_path_append("api", "v3", "PublicHolidays", "2027", "DE") |>
req_perform() |>
resp_body_json()
# Berlin observes International Women's Day (8 March) in addition to all
# nationwide holidays; global == TRUE means observed in all states
is_berlin <- \(x) isTRUE(x$global) || "DE-BE" %in% unlist(x$counties)
holidays_berlin_2027 <- as.Date(
vapply(Filter(is_berlin, resp), \(x) x$date, character(1))
)
# electricity
slp_electricity("H0", "2027-01-01", "2027-12-31",
holidays = holidays_berlin_2027)
# gas — same holidays argument, same semantics
slp_gas("HEF", dates_2027, temps_2027, kundenwert = kw,
holidays = holidays_berlin_2027)slp_gas() implements the BDEW/VKU/GEODE synthetic procedure (SigLinDe
method)
for daily gas consumption. It takes daily mean temperatures and a
customer value (kundenwert, kWh/day), and supports all 15 gas profile
IDs.
Take a single-family home (profile HEF) in Düsseldorf with a
kundenwert of 55.1 kWh/day. First grab the daily mean temperatures for
the period of interest — here the 2025/26 heating season — from the DWD
open-data archive via rdwd
(no API key required; TMK is the daily mean temperature in °C):
library(rdwd)
dates <- seq.Date(as.Date("2025-10-01"), as.Date("2026-04-30"), by = "day")
# Düsseldorf = DWD station 1078. The "recent" file is a rolling ~550-day
# window, so combine it with "historical" to cover any period; force = TRUE
# avoids reusing a stale cached download.
link <- selectDWD("Duesseldorf", res = "daily", var = "kl",
per = c("historical", "recent"))
raw <- do.call(rbind, readDWD(dataDWD(link, read = FALSE, force = TRUE),
varnames = FALSE))
temps <- raw$TMK[match(dates, as.Date(raw$MESS_DATUM))]
stopifnot(!anyNA(temps)) # fail loudly if the period isn't fully coveredThen pass dates and temps to slp_gas() together with the
kundenwert:
HEF <- slp_gas("HEF", dates, temps, kundenwert = 55.1)
head(HEF)
#> profile_id date kwh
#> 1 HEF 2025-10-01 40.95047
#> 2 HEF 2025-10-02 32.50304
#> 3 HEF 2025-10-03 31.91795
#> 4 HEF 2025-10-04 27.32729
#> 5 HEF 2025-10-05 32.50304
#> 6 HEF 2025-10-06 30.17767
# total gas consumption over the heating season (1 Oct 2025 – 30 Apr 2026)
sum(HEF$kwh)
#> [1] 12191.35The Kundenwert of 55.1 kWh/day is itself derived once, from the customer’s annual consumption and a reference temperature series. See the gas article for that step and the full method.
Let’s assume that same customer (i.e. fixed kundenwert) would live in
another place with a different climate. The chart below uses temperature
data for the same heating season (October ’25 to April ’26) to compare
their daily gas consumption in: - Chemnitz - Freiburg im Breisgau -
Hamburg against Düsseldorf.
Points above the 45° line mean that customer would have consumed more gas than in Düsseldorf due to different climate and temperatures. We see that in that winter all three cities ran colder than Düsseldorf, so every cloud sits above the line — most for Chemnitz (~35% more gas over the season), least for Freiburg (~11% more), with Hamburg in between (~25% more):
For a detailed walkthrough of the SigLinDe parameters and the full climate zone comparison, see the gas articles on the package website.
- Electricity SLPs: https://www.bdew.de/energie/standardlastprofile-strom/
- Gas SLPs: https://www.bdew.de/energie/standardlastprofile-gas/
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