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A survey-based approach to estimate residential electricity consumption at municipal level in Germany

: Huang, Charlotte; Elsland, Rainer

Volltext urn:nbn:de:0011-n-5693271 (1.1 MByte PDF)
MD5 Fingerprint: 6afeba92ba3d392401f8617dde923500
Erstellt am: 17.12.2019

Karlsruhe: Fraunhofer ISI, 2019, 41 S.
Working Paper Sustainability and Innovation, S 10/2019
Bericht, Elektronische Publikation
Fraunhofer ISI ()
regional analysis; techno-economic modelling; survey-based approach; residential sector; Iterative Proportional Fitting (IPF); synthetic population; electricity consumption

In the context of the German Energiewende (energy transition), energy system modelling is used to investigate possible future scenarios of the national energy system. These models depend on regionally disaggregated input data to adequately capture interdependencies in the energy system at high resolution. In Germany, official energy consumption statistics are only published at the national (AGEB, 2019a) and federal state (LAK, 2019) levels – there are no official statistics on residential electricity consumption with higher regional resolution. So far, energy system modelling has typically relied on specific consumption values or constant per-capita estimators (see Beer (2012) and Hartel et al. (2017)) to approximate residential electricity consumption with a regional resolution be-yond that of federal state. The use of primary data on electricity consumption at household level has so far been limited. Yet, primary data, e.g. from the German Residential Energy Consumption Survey (GRECS), displays heterogeneity in household-level electricity consumption, which cannot be captured by constant per capita distribution keys. This study aims to investigate whether integrating primary data into quantitative modelling of energy systems contributes to a more realistic representation of regional electricity consumption by accounting for het-erogeneity in household electricity consumption. A synthetic population is generated via the Iterative Proportional Fitting (IPF) algorithm based on primary data on household electricity consumption taken from the German Residential Energy Consumption Survey (GRECS), as well as region-specific data at municipal (Gemeindeebene) level taken from the 2011 German census. Total residential electricity consumption at municipal level is then inferred from the synthetic population. Estimates of total residential electricity consumption were derived for 2011 for all municipalities of the German state Rhineland-Palatinate and evaluated against benchmark values from the Netzentwicklungsplan 2030 (Fraunhofer ISI, 2017) of the same year, which is avail-able at the regional resolution of German urban and rural districts, referred to here as “counties” (Stadt- und Landkreise). The derived estimates achieved an R² of around 0.99 with respect to benchmark values. Overall, the estimates were 6.8% below the benchmark value for Rhine-land-Palatinate. It can be concluded that Iterative Proportional Fitting (IPF) constitutes a viable approach to integrate primary data into deriving regional estimates of residential electricity consumption at municipal level.