This paper describes a dataset of residential electricity household and heat pump load profiles, measured in 38 single-family houses in Northern Germany. We provide data per household of apparent, active and reactive power (W), voltage (V), current (A) and the power factor (no unit) in 10 seconds to 60 minutes temporal resolution from May 2018 to the end of 2020. We validated the dataset both in itself, comparing different measurements that should produce the same results, and externally to standard load profiles and found no major inconsistencies. We identified an average consumption per single-family house with 2.38 inhabitants of 2829 kWh for the household and an additional 4993 kWh for the heat pump. The dataset can support the understanding of patterns in electrical load curves and can help to estimate the additional load on distribution networks induced by heat pumps.

A fast and extensive build‐up of green hydrogen production is a crucial element for the global energy transition. The availability of low‐cost renewable energy at high operating hours of the electrolyzer is a central criterion in today's choice of location for green hydrogen production. It is analyzed how decreasing electrolyzer costs that are expected by many may influence this choice. The energy system optimization framework ESTRAM is used to find the optimum configuration of wind turbine, photovoltaic (PV), and electrolyzer capacity for covering a given hydrogen demand by locally produced green hydrogen in different European locations. It is found that PV is part of the cost‐optimal solution in 96% of 1372 statistical regions in Europe. Decreasing electrolyzer costs are favoring the utilization of PV in wind–solar hybrid plants. At low electrolyzer costs, pure solar hydrogen outperforms the hybrid variant in many places if hydrogen storage is available, even with few full operating hours per year. At the same time, production costs are converging significantly. The article adds a new perspective to the discussion, as it is systematically shown how further technology development may lead to a shift in locational advantages for green hydrogen production, what should be considered to avoid stranded assets when building infrastructure.

End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.