The found results are first discussed with regard to both profile assignment methods. Subsequently, sector-specific characteristics are examined.
The demand allocations for both electricity and heat mainly base upon data sets from Census and OSM. As examined in “Census data preparation”, the household-related attributes from Census cover most of the cells. Yet, in 28.1% of the cells (Table 2) it is partially incomplete or inconsistent which leads to systematic errors in our results. The induced error in Germany-wide demand, however, is less significant as those cells account for only 5.5% of the total population. Moreover, the Census data set is outdated and applied statistical methods for spatial and demographic extrapolation (Statistisches Bundesamt 2015) affect the quality of our results to a non-quantifiable degree.
OSM also has several shortcomings: we extracted 29.3 million residential buildings in total (Table 4). This number deviates significantly by +51.8% from the latest official data of 19.3 million (Statistisches Bundesamt 2020) and +58.4% from the 2011 Census data of 18.5 million (Statistisches Bundesamt 2011a). This discrepancy is likely driven by the inaccurate tagging aside from different definitions of residential buildings, as well as OSM users’ susceptibility to mapping errors. However, as we incorporate Census data, the number of mapped and synthetically created buildings that are assigned electricity and heat profiles by our methods is smaller (Table 4) and deviates by only +10.4% (electricity) and +6.7% (heat) from official data (Statistisches Bundesamt 2020). Assuming that all buildings from the official data are inhabited and therefore have demand, our results are in reasonable accordance in terms of the total number of buildings. Nevertheless, this does not indicate a similar level of agreement on higher resolution levels. As we did not compare building counts on other levels, we cannot quantify errors compared to official statistics or ground truth. Another shortcoming in OSM data are missing building data, which lead to further deviations. Therefore, these data gaps are filled with synthetic buildings using Census data as described in “OSM building assignment”. This results in shares of 5.4% (electricity) and 4.8% (heat) synthetic buildings with demand. While their real locations cannot be determined, the population-induced building demand is retained by our methodology.
The distribution of annual electricity demands from DemandRegio bases on a forecast of population per NUTS 3-region. Our distribution methods, on the other hand, utilize Census data from 2011. This results in a mismatch between the electricity demand and the Census population. However, the population forecast just differs slightly and only in a few regions (Gotzens et al. 2020). Peta’s annual heat demands are based on yet other population input data, requiring additional assumptions. Overall, this leads to 3% of residential buildings with electricity but without heat demand, which restricts the usability of the corresponding profiles in the affected cells.
The only linkage of both allocation methods are the derived residential buildings in step (e11) shown in Fig. 1. A temporal linkage of electricity and heat consumption is not considered. It is thus possible that the heat demand curve of a specific building indicates that it is occupied and shows a peak load whereas the electricity demand indicates differently. This influences profiles on a high spatial resolution, but is leveled out when aggregating multiple profiles. In addition, the individual electricity demand profiles had been created for the year 2016 whereas the heat demand profiles had been generated using the weather year 2011. Since the electricity profiles are less weather-sensitive and differentiation of weekdays and weekend days is not considered in the assignment of heat demand profiles, the error is considered to be small.
The resulting profiles are only analyzed for an exemplary region on selected aggregation levels and compared with SLPs on a higher aggregation level. Further validations, e.g. statistical analyses of all profiles on different aggregation levels, are not part of this study.
Demand profiles for single buildings were validated in Drauz (2016) and Von Appen et al. (2014). In order to validate the suitability of this methodology, aggregated measurement data, e.g. from electricity or district heating grid operators on HH demand are necessary. To our knowledge, these data are currently not freely accessible or not acquired—while e.g. the regional electricity grid operator is obliged to publish an annual, aggregated load curve according to §17 StromNZV (Die Bundesregierung 2021), the data on low voltage level (Stadtwerke Flensburg 2021) are not provided per grid and sector and most likely include demands of other sectors such as commercial, trade and services. Therefore, we can not use it as a reference and draw a comparison to the SLP. The resilience of the resulting profiles is higher on aggregated levels. Profiles for a single building can be biased due to the random selection. To increase robustness, multiple samples could be taken. Further validations are easily possible due to the applied open source principles.
Electricity demand profiles
Revisiting the quality of the Census data, there are more shortcomings to be discussed specific to the electricity sector. Due to differences in HH categories and the corresponding methodology, the total HH distribution obtained by Census differs from the one used in Von Appen et al. (2014), which first verified the used demand profiles. This leads to a predominance of single HHs and a less dominant share of multi HHs in the aggregated HH distribution by Census. On building level, this is of high impact but might also affect the smoothness of profiles at aggregated levels. Since there is no alternative data with a sufficient spatial resolution, we can neither correct nor quantify this error.
The further applied imputation methods presented in “Census data preparation”
lead to errors regarding the shares of HH types in 11.8% of the cells (Table 2). Moreover, 16.3% of the cells do not hold HH information at all. While we fill those data gaps, this results in wrongly assigned profiles with respect to HH types. This bias is again profound on building level, yet less significant on higher aggregation levels. As the imputation is based on the population density, mixing rural and urban cells is mostly avoided. Assuming the same HH distribution for cells with identical population density reduces the variation in HH types to some extend but can not be quantified as we lack reference data.
With regard to the individual demand profile pool, it is worth mentioning that the HHs type distribution differs compared to the national one obtained from Census. The pools of HH types have equal size (8.3% each), which leads to disproportionately small pools for the predominating types (cf. Table 5). Thus, at high aggregation levels, profiles are assigned more than once as mentioned in “Demand profile assignment and scaling”. The profile pool could be enlarged and shares adjusted to the national HH distribution. This may have an effect on the smoothness of the aggregated profile. However, as in the examined MVGD (40,300 HH), profiles mainly occur once (50%), twice (31%), or three times (15%), the influence is considered to be small. An in-depth analysis of the supplied input profiles is not in the focus of this work as we aim at attaining a spatial distribution of the profiles. However, it should be noted that the profiles base upon today’s user behavior, occupancy hours and device efficiencies which are subject to change in the future, which leads to forecast errors in our data.
The assumptions made in “Census data preparation” as well as the random assignment of profiles to cells in “Demand profile assignment and scaling”
and subsequently to buildings in “OSM building assignment” inevitably lead to systematic errors. The data quality might increase by using additional OSM data such as buildings’ ground area or number of storeys during the assignment process.
Analyzing the results in the time domain, the significantly steeper gradients, higher peaks and smaller base loads are key differences seen in Fig. 4a, but also fit in with the technological development that has taken place since the development of these profiles in the 1980s. Nowadays, there are more electrical appliances in use with lower stand-by consumption. Hence, higher peaks in occupancy hours during the day and lower consumption during the night time seem plausible. As the degree of aggregation increases, a clear smoothing behavior can be seen which can be explained by the increasing variance of the profile types used (cf. Table 5). Although the MVGD contains many more HH then the SLP’s acceptable lower bound, significant deviations occur especially during peak time hours where the load variation is maximal (cf. Fig. 4b). The authors of Von Appen et al. (2014) state, that aggregated profiles from different years would be necessary to be even more similar to the SLP. Since we are not interested in aligning our data to the SLP but in profiles for specific years and of great regional heterogeneity, the deviation is acceptable and even beneficial to the investigation of low voltage grids.
Overall, the large regional heterogeneity of the resulting electricity demand profiles and observed smoothing effects for higher aggregation levels make the presented methodology better suited for the purpose of modeling load flows over all voltage levels than the usage of SLPs. Another advantage is the adaptability of the presented method to model electricity demand for different future scenarios.
Heat demand profiles
Input data as well as the method influence the quality of the resulting HH heat demand profiles. As described in “Discussion”
, there are mismatches in the main input data sets Census and Peta. However, due to the small amount of affected cells, profiles are available for most buildings. The effects on aggregated profiles is therefore considered to be small. In addition, the distribution of annual heat demands to buildings could be improved. The annual heat demands per Census cell are evenly distributed to buildings. In case of cells with SFHs and MFHs, this leads to an overestimation of SFHs profiles.
Besides, the number of individual heat demand profiles was substantially limited by computational resources. Therefore, neither data on HH nor day types are considered when distributing the heat demand to buildings and over time, reducing the consistency to electricity profiles. Using intra-day profiles enlarged the pool of profiles and their variation. Composing the intra-day profiles could potentially lead to inappropriate steps at midnight. However, because of the lower heat demands at night, this is not observed in the resulting annual profiles.
The resulting heat demand profiles are plausible on different aggregation levels. Looking at the load curve of single buildings there are high peak demands during occupancy hours. These peaks are caused by hot water demand, which is characterized by abrupt random instantaneous rise and fall (Drauz 2016). In summer, the heat demand profiles are dominated by hot water demand, which causes higher peak values than in winter where the profile shape is an overlay of the abrupt hot water and more constant space heating demand. These individual variations in the curve shapes of single buildings caused by occupancy are absent in the SLPs.
When aggregating the profiles of buildings, the peak demands are lower and the similarity of the generated profiles with the SLP is increased. This is caused by the fluctuation of profiles at building level. Single building profiles include peaks due to hot water demand in every occupancy hour, so the peaks balance out. Hence the implemented methodology provides a means of increasing the spatial resolution of the existing SLPs. Also, the similarity of the aggregated profiles with SLPs shows the validity of the presented methodology. The aggregated annual demand curves show a similar seasonal dependency as the SLP. This indicates reasonable assumptions of temperature data and the selected climate zones. In the presented method, the heat demand in the morning rises slightly later and more abruptly than in the SLPs method. Since the SLPs are created using gas profiles, the more abrupt rise can be caused by the inertia of the gas system which is not influencing the bottom-up heat demand profiles of the presented methodology. In contrast to SLPs the presented methodology includes a decrease in heat demand curves at midday. This could be caused by the individual bottom-up profiles because hot water demands at midday are unlikely according to the occupancy model used in the load profile generator.
All in all, the drawbacks associated with SLP are addressed to a great extent by this methodology. Heat demand peaks have a better representation along with a correct representation of the summer demand. The consideration of newer building classes makes the future demand forecast possible, but upcoming changes in the behavior and future building characteristics (e.g. low-energy houses) could not be considered. Finally, it can be stated that even with these additional features, the methodology can develop a final aggregated output in line with the commonly used SLP methodology, thus justifying its possible future application.