Forecasted and Residual Electricity Consumption Dataset (Quarter-Hourly) – Germany (2015–2026)

Published: 2 March 2026| Version 1 | DOI: 10.17632/t83jdc65xh.1
Contributor:
Shahinur Rahman

Description

Abstract: This dataset contains quarter-hourly forecasted electricity consumption and forecasted residual load data for Germany covering the period from 1 January 2015 to 3 February 2026. The data represent day-ahead total load forecasts calculated by Transmission System Operators (TSOs) in accordance with Commission Regulation (EU) No 543/2013 and published via the ENTSO-E Transparency Platform and the SMARD Electricity Market Data Platform. Forecasted consumption is derived using empirical historical load profiles, meteorological inputs, and socio-economic factors. The forecasted residual load is computed as total forecasted consumption minus forecasted feed-in from photovoltaic, onshore wind, and offshore wind generation. The dataset is provided in CSV format at quarter-hour resolution and merged into a single ZIP archive for structured access. All values are expressed in MWh per quarter hour as published on SMARD. The dataset is suitable for research in short-term load forecasting, renewable integration analysis, residual load modeling, smart grid analytics, energy market studies, and AI-based energy demand prediction. The data are distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Description Quarter-hourly day-ahead forecasted electricity consumption and residual load data for Germany from 2015 to 2026. Data were obtained from the SMARD Electricity Market Data Platform and ENTSO-E Transparency Platform. The dataset includes total forecasted load and forecasted residual load derived by subtracting renewable feed-in (PV, onshore wind, offshore wind) from total forecasted consumption. Data Format CSV (compressed as ZIP archive) Temporal Coverage 01 January 2015 – 03 February 2026 Spatial Coverage Country: Germany Resolution Quarter-hour (15 minutes) License Creative Commons Attribution 4.0 International (CC BY 4.0)

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Electric Power, Energy Utility, Energy Forecasting, Smart Grid, Electric Utility

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