standardlastprofile

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.

Small multiple line chart of 11 electricity standard load profiles
from 1999 published by the German Association of Energy and Water Industries
(BDEW). Lines compare three seasonal periods across different day types.

Installation

install.packages("standardlastprofile")

Included features

slp_info() — descriptions for all electricity and gas profile IDs

Electricity

slp_electricity_profiles — dataset of BDEW electricity SLPs in tidy format
slp_electricity() — generate a 15-minute profile for any date range

Gas

slp_gas() — generate daily gas consumption via the SigLinDe method
slp_gas_coefficients() — retrieve SigLinDe coefficients for gas SLPs
slp_gas_kundenwert() — derive the customer value (German: “Kundenwert”) from a reference temperature series
slp_gas_siglinde() — low-level SigLinDe function, can be useful for custom or region-specific SigLinDe coefficients
slp_gas_weekday_factors() — retrieve weekday factors for gas SLPs

Electricity

The dataset slp_electricity_profiles contains 26,784 observations across 5 variables:

profile_id: load profile identifier
period: "summer", "winter", or "transition" for 1999 profiles; a lowercase month name for 2025 profiles
day: "workday", "saturday", or "sunday"
timestamp: quarter-hour start time in "%H:%M" format
watts: 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 ...

1999 profiles

Based on an analysis of 1,209 load profiles of low-voltage electricity consumers in Germany¹:

H0: households
G0–G6: commercial
L0–L2: agriculture

2025 profiles

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 profiles
P25: households with a photovoltaic (PV) system
S25: households with a PV system and battery storage

Small multiple line chart of five electricity standard load profiles
published by BDEW in 2025. Lines are coloured by calendar month and faceted
by profile and day type.

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.

Faceted line plot with four panels for H0, H25, P25, and S25.
Each panel shows cumulative energy consumption in kWh over 2026.
A grey reference line shows H0. H25 tracks H0 closely, while P25 and S25
diverge from spring onwards due to photovoltaic generation and battery storage.

Generate a profile

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

Line plot of the electricity standard load profile 'G5' (bakery
with a bakehouse) from December 22nd to 27th 2023, normalised to 1,000 kWh/a.

Public holidays

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)

Gas

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 covered

Then 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] 12191.35

The 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.

Line chart of daily gas consumption in kilowatt-hours for a
single-family home in Düsseldorf across the 2025/26 heating season. Demand
peaks in the cold winter months and is lowest in the mild shoulder months of
October and April.

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):

Faceted scatterplot grid: columns are Chemnitz, Freiburg, Hamburg;
rows are months October to April. Each point is a day; the x-axis is daily
gas consumption in Düsseldorf, the y-axis in the comparison city, with a
45-degree reference line. All three cities' point clouds sit above the line
in winter, most clearly for Chemnitz.

For a detailed walkthrough of the SigLinDe parameters and the full climate zone comparison, see the gas articles on the package website.

Sources

Electricity SLPs: https://www.bdew.de/energie/standardlastprofile-strom/
Gas SLPs: https://www.bdew.de/energie/standardlastprofile-gas/

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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