Adds EPEX Spot data to Home Assistant.
Provides helper sensors for EPEX Spot integration.
Charge your Tesla in the cheapest hours, automatically. A small ESP32 board by the car reads day-ahead prices and your grid fees, then starts and stops charging over Bluetooth. No cloud, no Tesla account, no subscription. 26 European countries.
SQL and PostgreSQL pipeline for exploratory analysis of negative day-ahead electricity prices in the DE-LU bidding zone using SMARD data.
End-to-end data analytics pipeline analyzing German electricity market pressure, prices and renewable generation using Python, SQL, DuckDB and Data Studio.
Ready-to-use Home Assistant, Node-RED and smart-home examples for the free, keyless InfraNode API (open data for 84 German cities)
German day-ahead electricity prices per quarter hour with explanations, sensors, actions and dashboard cards for Home Assistant (strompreis-verstehen.de)
End-to-end German electricity-market data pipeline - Python API ingestion into DuckDB, dbt transformations (6 models, 20 tests), quarter-hourly solar source reconciliation, negative-price and solar capture-price analysis.
Stromdaten aus dem SMARD-Portal analysiert mit Python, PostgreSQL und Power BI
Build an easy-to-operate IoT-device that displays the current proportion of green electricity in the grid.
Day-ahead electricity price forecasting for Germany/Luxembourg from public SMARD data: auction-safe features, rolling-origin backtest, gradient boosting 26% better than the naive forecast.
AI-Powered German Energy Market Analytics | Electricity Price Forecasting, Renewable Energy, Python, Machine Learning & Data Engineering
German power prices, load and renewables from SMARD, ingested daily, kept correct through revisions and market changes, and served through a public API.
Day-ahead electricity price forecasting for the German bidding zone, under a strict gate-closure information constraint. The most accurate model turns out to be one that cannot run.
A reproducible historical negative-price risk view for the DE-LU day-ahead power market
SwarmGrid überträgt Schwarmtheorie auf Energiezeitreihen. Statt einzelne Messwerte gegen Grenzwerte zu prüfen, beobachtet jede Zeitreihe ihre gelernten Nachbarn im Netz — und meldet sich wenn sie sich vom kollektiven Verhalten löst. Die Netztopologie wird nicht vorgegeben sondern aus den Daten gelernt. Jede Anomalie ist durch SHAP erklärbar.
Python analysis of Germany's electricity generation and consumption using SMARD data, with renewable share, residual load, statistics and interactive Plotly figures.
Production XGBoost quantile forecasters for the German day-ahead market. The load model beats the TSO's published forecast by 21 % across a 14-month holdout.
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