"Quarter-Hourly Electricity Generation Data for Germany (2015\u20132026)"
Abstract
"This dataset provides quarter-hourly actual electricity generation data for Germany covering the period from 1 January 2015 to 25 February 2026. The data are derived from the SMARD electricity market platform operated by the Bundesnetzagentur and reported by German Transmission System Operators (TSOs) in accordance with Commission Regulation (EU) No 543\/2013.The dataset represents net electricity generation, defined as electricity fed into the general supply network minus the electricity consumed by generation units for their own operation. It includes generation from renewable and conventional energy sources and excludes electricity produced within industrial networks, closed distribution networks, and the Deutsche Bahn network.The dataset contains quarter-hour resolution time-series data (15-minute intervals) for the following energy sources:Renewable energy sources: biomass, hydropower (run-of-river and hydro storage), offshore wind, onshore wind, photovoltaics, geothermal energy, landfill gas, sewage gas, and pit gas.Conventional energy sources: nuclear, fossil brown coal (lignite), fossil hard coal, fossil gas (natural gas), hydro pumped storage, mineral oil, waste, refinery gas, blast furnace gas, oxygen steel furnace gas, hydrogen-rich gas mixtures, and other production by-products.All yearly CSV files from 2015\u20132026 were consolidated into a single structured ZIP archive to facilitate long-term energy transition analysis. The dataset supports research in power system modeling, renewable integration studies, generation forecasting, energy economics, volatility assessment, grid stability analysis, and machine learning applications in power engineering.The data are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license and are freely reusable with proper attribution to SMARD."
Fields of Study & Subjects
Renewable Energy and Sustainability
Integrated Energy Systems Optimization
Energy Load and Power Forecasting
Publication details
- Journal / source
- IEEE DataPort
- Publication date
- 25 February 2026
- Language
- EN
- Document type
- Dataset