This is like a smart weather-and-power crystal ball: it looks at recent weather and production data and uses machine learning to predict how much solar and wind power will be generated in the next few hours.
Energy companies and grid operators need accurate short-term forecasts of solar and wind output to balance supply and demand, reduce reserve margins, and operate the grid reliably despite renewable variability.
Domain-specific model design and feature engineering for solar/wind time-series, plus access to high-quality historical SCADA and meteorological data can create a defensible performance edge over generic forecasting models.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Training and serving latency on high-frequency, multi-site time-series data; data quality and gaps in historical SCADA and weather data can limit accuracy.
Early Majority
Positions itself as a novel short-term forecasting approach tailored to solar and wind generation, likely improving accuracy over standard ARIMA or naive persistence baselines, which is valuable for high-renewables grids.