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SFML is the first fully local AI solar forecast for Home Assistant, powered by a local Attention Transformer. No external AI — such as ChatGPT, Gemini, or Grok — required. Runs entirely on your device for complete privacy.
An R-based solar power forecasting framework utilizing Support Vector Regression (SVR). Features include One-Class SVM outlier removal, diurnal cyclic encoding, and a robust CLI interface via Docker.
Benchmark of persistence, BLEND, AR-OLS, and Extreme Learning Machine (ELM) variants for photovoltaic power forecasting across multiple sites, with skill scores (NICE) against persistence.
Feasibility study and machine learning forecasting pipeline (XGBoost) for a 1.08 MWp rooftop solar PV microgrid at École Centrale Casablanca. Integrates live NASA POWER meteorology and physical flat-roof spacing constraints.
Autonomous Sigen inverter controller for home solar and battery systems. Very lightweight (can run on RPi), making mode decisions every 5 minutes based on solar forecasts and live battery SOC — optimising between, self-consumption, grid export, and overnight TOU charging. Using Irish ESB Solar forecasts but portable to other providers.