Rising demand for individual carbon footprints
Individual initiative in the fight against climate change is possible in many areas. It ranges from reduced meat consumption to the renunciation or reduced use of cars with combustion engines to sustainable financial investments. Both environmentally conscious consumers and companies are increasingly becoming aware of the carbon footprint and want to contribute to a solution. Sustainability also plays a central role in the energy supply sector.
Data on greenhouse gas emissions
Dynamic Greenhouse Gas (GHG) emission factors combined with electrical load profiles enable the calculation of individual GHG footprints. The particular amount of electric energy purchased from the grid multiplied by the emission factor for the respective region (e.g., a country) is a reasonable indicator for the emissions caused by power consumption. In this way, the individual GHG footprint of consumers can be determined ex post and, if necessary, settled via service providers like atmosfair.
The GHG emissions can already be reduced in the electricity consumption using the day-ahead forecast. The data can be integrated into smart home applications (e.g. Home Assistant), for example, to control high-consumption appliances such as washing machines and dryers so that they are used when the GHG emissions in the electricity mix are lowest.
To enable and promote sustainable behavior, two datasets are provided here on the FfE open data platform. The ex post values are calculated using actual energy production by energy source. In contrast, the day-ahead forecast is generated by utilizing (among others) data on the day-ahead production of renewables. Figure 1 displays both data sets for three days. Both data sets are updated daily between 8 and 10 pm (CET/CEST).
The code implementing this method is open-source and publicly available on GitLab.
Emission factors (ex post) are calculated by offsetting the amount of electricity produced by an energy source 1 against the specific emissions of that energy source 2. The particular emissions already account for the pre-chain like wind energy, for example, also has a particular carbon footprint.
The forecast of GHG emissions in the electricity mix is performed using machine learning techniques. For this purpose, a Random Forest Regressor has been trained using historical day-ahead estimates of wind and photovoltaic generation as well as electricity prices and temporal features like the day of the week. Provided with up-to-date forecasts from the ENTSO-E Transparency Platform, the pre-trained model is able to predict GHG-emissions in the german electricity mix for the next day.
To account for the uncertainty of the prediction, two additional values are included besides the actual predicted value. The upper and lower bounds serve as an indicator of how accurate the prediction of the model is.
The software used as an interface to the ENTSO-E Transparency Platform is based on freely available code from the project electricitymap.org.
Since the datasets are the result of an automatically performed calculation based on data from the ENTSO-E Transparency Platform, completeness and correctness cannot be guaranteed, since the input data may be incomplete or incorrect.
The datasets as well as this article can be accessed on the opendata platform provided by the FfE via these links: