Gas Standard Load Profiles (Standardlastprofile Gas) • standardlastprofile

A standard load profile reflects the assumed pattern of gas consumption. It is used for forecasting and balancing purposes for various customer types or customer groups for which no continuous measurement data is available. In this respect, standard load profiles represent a simplification.

In this article we explain how slp_gas() implements the algorithm of the synthetic procedure (German: “synthetisches Verfahren”), following the definitions by BDEW, VKU, and GEODE in the Leitfaden Abwicklung von Standardlastprofilen Gas (2026).¹

Synthetic procedure

The synthetic procedure takes a bottom-up approach: the gas consumption is explained by the sum of the individual gas consumers.² The gas consumption on day \(D\) is the result of three factors:

outside temperature
preferences and habits
the day of the week

Mathematically, this is expressed as:³

\[ Q(D) = h(\vartheta_D) \times KW \times F_{WT, D} \tag{1}\label{eq:qdaily} \] where:

\(h(\ldots)\) is a profile function evaluated at the daily temperature \(\vartheta_D\).
\(KW\) is a customer-specific scaling factor in kWh/day (German: “Kundenwert”).
\(F_{WT}\) is a weekday factor that adjusts for consumption differences across days of the week.

Weekday factors \(F_{WT}\)

Weekday factors are scaling factors applied for every day from Monday to Sunday.⁴

For the residential profiles HEF, HMF, and HKO, however, all weekday factors are equal to 1, that is, gas consumption in households is assumed not to differ significantly by day of the week. Commercial profiles on the other hand show visible weekday patterns, the range of the weekday factors is 0.3877 - 1.2707.

The weekday factor values are returned by slp_gas_weekday_factors() and illustrated by the chart below:

Public holidays are treated as a Sunday.⁵ 24 December and 31 December are treated as Saturday unless they fall on a Sunday; this convention mirrors the established rule from the BDEW electricity SLP procedure and is applied here by analogy.

Profile function \(h(\vartheta_D)\)

Gas consumption is strongly correlated with outdoor temperature; the colder it gets, the more gas is consumed — at least when gas is used for space heating. The relationship between outside temperature and gas consumption is captured by the profile function \(h(\vartheta)\):

\[ h(\vartheta) = \underbrace{\frac{A}{1 + \left(\dfrac{B}{\vartheta - \vartheta_0}\right)^C} + D}_{\text{sigmoid part}} + \underbrace{\max\!\left(m_H \vartheta + b_H,\; m_W \vartheta + b_W\right)}_{\text{linear part}} \]

This function was initially published in 2003 as an S-shaped sigmoid function:⁶

\[ h(\vartheta) = \frac{A}{1 + \left(\dfrac{B}{\vartheta - \vartheta_0}\right)^C} + D \]

Since then, it has been revised as part of an ongoing debate. In a research paper from 2015 it was found that a pure sigmoid approach systematically underestimated consumption at very cold temperatures. The authors proposed an improved formulation called SigLinDe (Sigmoid + Linear + Deutschland) and published SigLinDe coeffeciants for 15 different customer groups, i.e. profile IDs.⁷ SigLinDe adds a linear component to that sigmoid equation representing space-heating demand (German: Heizgas-Gerade) and hot water demand (German: Warmwasser-Gerade).

The SigLinDe approach was adopted by BDEW and has since been the binding standard.⁸

slp_gas() implements the SigLinDe approach.

Profile IDs

The 15 gas profile IDs defined, split into residential and commercial / industrial types.

Residential profiles

Residential profiles share two important characteristics: all weekday factors equal 1 (see above), and heating load is the dominant driver of demand variability.

Profile	Description	Notes
`HEF`	Single-family home (Einfamilienhaus)	Strongest seasonal swing; includes space heating + hot water
`HMF`	Multi-family home (Mehrfamilienhaus)	Lower per-unit demand than `HEF` due to shared building envelope
`HKO`	Cooking and hot water only (Kochen / Warmwasser)	No space heating; TUM profile — sigmoid only, linear part is zero

HKO is appropriate for customers whose gas connection serves only a cooker or instantaneous water heater. Its flat, temperature-insensitive shape is visible in the SigLinDe curve plot above.

Commercial and industrial profiles

Commercial profiles have profile-specific weekday factors that reflect the operating hours and schedules of each sector. They are grouped here by consumption pattern:

Process-heat dominated (relatively flat seasonal pattern, strong weekday structure):

Profile	Description
`GWA`	Laundries — large hot-water demand, insensitive to outdoor temperature
`GBA`	Bakeries — continuous baking process, pronounced weekday factors
`GPD`	Paper and printing — industrial process heat

Space-heating dominated (strong winter peak, moderate weekday variation):

Profile	Description
`GKO`	Small commercial (Kleinstgewerbe)
`GHA`	Trade and commerce (Handel)
`GBD`	Services (Dienstleistung)
`GBH`	Accommodation (Beherbergung)
`GGA`	Gastronomy (Gastronomie)
`GMK`	Metal and automotive (Metall / Kfz)
`GGB`	Mixed commercial (gemischtes Gewerbe)
`GMF`	Large multi-family / mixed use (Mehrfamilienhaus groß)

Aggregate / summary profile:

Profile	Description
`GHD`	Trade, commerce and services aggregate (GHD-Stützpunkt) — weighted mean across all G-types

GHD is used when a customer cannot be assigned to a specific sector. Its parameters are a weighted average of the individual commercial profiles and its weekday factors are close to but not equal to 1.

All 15 gas profile IDs share the same SigLinDe function — but they differ in their parameter values. The parameters \(A\), \(B\), \(C\), \(D\), \(\vartheta_0\), \(m_H\), \(b_H\), \(m_W\), \(b_W\) are profile-specific constants.⁹

You find the complete set in the SigLinDe Parameters and Weekday Factors article, and those values are returned by slp_gas_coefficients().

The result of \(h(\vartheta)\) is a number > 0 without a dimension:

# profile "HEF", Ausprägung: 34
slp_gas_siglinde(
  theta  =   8.0,
  A      =   1.381966,
  B      = -37.41242,
  C      =   6.172318,
  D      =   0.0396284,
  theta0 =  40.0,
  mH     =  -0.0672159,
  bH     =   1.116714,
  mW     =  -0.0019982,
  bW     =   0.135507
)
#> [1] 0.9999997

For theta = 8 the result above is ~1 — which is no coincidence, and brings us to the kundenwert.

Kundenwert \(KW\)

kundenwert accounts for a customer’s individual consumption behaviour. It answers the question: how much gas does a customer consume on a day when the outside temperature is 8°C? It is a scaling factor in kWh/day — 8°C is the temperature at which \(h(\vartheta) = 1\) for every profile, with one exception: HKO, whose linear parameters (\(m_H\), \(b_H\), \(m_W\), \(b_W\)) are all 0. With no linear component, the HKO profile function is a plain sigmoid rather than a SigLinDe (sigmoid-plus-linear) function, and so is not normalised to 1 at 8°C.

kundenwert is derived from the customer’s annual consumption and the year’s temperature and weekday profile. Starting with \(\eqref{eq:qdaily}\), the annual total consumption is the sum of all daily values:

\[ Q_a = \sum_{D} Q(D) = \sum_{D} h(\vartheta_D) \times KW \times F_{WT,D} \]

Rearranging to solve for \(KW\):

\[ KW = \frac{Q_a}{\displaystyle\sum_{D} h(\vartheta_D) \times F_{WT,D}} \tag{2}\label{eq:kw} \]

Where

\(Q_a\) is the annual gas consumption (German: Jahresverbrauch)
\(\vartheta_D\) is the daily temperature for day \(D\)
\(F_{WT,D}\) is the weekday factor for day \(D\)

Using the example of a single-family home (HEF) in Düsseldorf with an annual consumption of 15,000 kWh — the same customer as in the README — the table below shows how the denominator is built up, day by day, from the Düsseldorf long-term daily mean temperatures (2004–2024):

Date	°C	h(ϑ)
Jan 1	4.8	1.3962
⋮	⋮	⋮
Jun 30	19.2	0.1727
Jul 1	18.9	0.1765
Jul 2	19.2	0.1727
⋮	⋮	⋮
Dec 31	5.7	1.2833

The sum in the denominator across all 365 days gives \(\sum_D h(\vartheta_D) = 272.32\). And since for all three residential profiles the weekday factors are 1, \(\eqref{eq:kw}\) simplifies to:

\[ KW = \frac{Q_a}{\displaystyle\sum_{D} h(\vartheta_D)} \]

So \(KW \approx\) 15,000 / 272.32 = 55.1 kWh/day.

A working example: Düsseldorf

With the algorithm covered, let us see how to use slp_gas() with real-world temperature data.

slp_gas() takes:

a profile_id
a dates vector, and a matching vector of temperatures, and
a kundenwert.

Deriving the Kundenwert

To derive kundenwert we need an annual consumption and temperature data. We can feed this information into the function slp_gas_kundenwert() together with any of the 15 gas profile IDs. To produce a meaningful result the temperature series should cover at least a full year. We show you how to fetch measured data from a trustworthy institution like the German Meteorological Service, or DWD for short.

The rdwd package provides a straightforward interface to the open-data portal of the DWD (Deutscher Wetterdienst).¹⁰ No registration or API key is required.

rdwd package

This section gives you a quick introduction to the rdwd package, read more about its features at brry.github.io/rdwd.

Our workflow has three steps:

selectDWD() — find the download URL for a station and dataset.
dataDWD() — download and unzip the file.
readDWD() — parse the file into a data frame.

We need the daily climate summary (var = "kl", res = "daily"). The column TMK contains the daily mean temperature in °C. To find the exact station name to pass to selectDWD(), use findID():

# returns matching station names and IDs
rdwd::findID("Duesseldorf", exactmatch = FALSE)

For the reference temperatures we use the long-term daily mean over 2004–2024 rather than a single year, so that no individual-year anomaly distorts the scaling factor¹¹.

library(rdwd)

# Full daily climate record for Düsseldorf (DWD station 1078)
links <- selectDWD(
  "Duesseldorf",
  res = "daily",
  var = "kl",
  per = c("historical", "recent"),
  current = TRUE
)

raw <- do.call(
  rbind,
  readDWD(dataDWD(links, read = FALSE, force = TRUE), varnames = FALSE)
)

# drop historical/recent overlap
raw <- raw[!duplicated(raw$MESS_DATUM), ]   

# Long-term mean daily temperature over 2004-2024, one value per calendar day
ref       <- raw[format(raw$MESS_DATUM, "%Y") %in% as.character(2004:2024), ]
ref$mmdd  <- format(ref$MESS_DATUM, "%m-%d")
clim      <- aggregate(TMK ~ mmdd, data = ref, FUN = mean)
clim      <- clim[clim$mmdd != "02-29", ]          # drop the leap day
dates_ref <- as.Date(paste0("2023-", clim$mmdd))   # any non-leap year
temps_ref <- clim$TMK

Now that we have a reference temperature series, we can call slp_gas_kundenwert():

# kundenwert for a 15,000 kWh/a HEF customer
slp_gas_kundenwert("HEF", dates_ref, temps_ref, annual_consumption = 15000)
#>      HEF 
#> 55.08305

That value is what the README example passes to slp_gas() to generate the 2025/26 heating season — and it is what we use below to ask how the same customer would fare in a different climate.

Same customer preferences, different climate

We keep that kundenwert of 55.1 kWh/day fixed and ask how the gas consumption changes in another place — same customer, same KW, different climate. Germany spans several distinct climate zones; we compare three:

Chemnitz — continental climate in the Ore Mountains foothills of Saxony; colder winters and the greatest temperature amplitude of the three.
Freiburg im Breisgau — mild oceanic-continental climate in the sheltered Upper Rhine Plain; the warmest, sunniest large city in Germany.
Hamburg — maritime climate on the North Sea coast; mild but cloudy, windy winters.

The grid below has one column per comparison city and one row per month of the 2025/26 heating season (the same period as the README example). Each point is a day, plotting the city’s daily gas consumption (y-axis) against Düsseldorf (x-axis); the 45° line marks equal consumption.

That winter all three cities ran colder than Düsseldorf, so every point cloud sits above the line — the question is by how much:

Chemnitz is highest, about 35 % more gas over the season (≈ 16,400 vs 12,200 kWh);
Freiburg im Breisgau is closest to Düsseldorf at about 11 % more;
Hamburg follows at about 25 % more.

The gap is widest on the cold January and February days at the top-right of each panel, and narrows in the mild shoulder months of October and April where the clouds approach the line.