Electricity Forecasting Frameworkv1.0.0

Energy grid operators use STLF models (XGBoost, LSTM) to predict electricity demand 1-48 hours ahead. This enables real-time generation scheduling, unit commitment, and balancing supply-demand to prevent blackouts.

Traders and energy companies leverage medium-term forecasts (1 week to 1 month) to optimize day-ahead and intraday trading strategies. Accurate predictions reduce financial risk from price volatility and enable arbitrage opportunities.

Facility managers use the framework to forecast building-level consumption patterns, integrating weather and occupancy data. This supports peak shaving, load shifting, and automated HVAC scheduling to reduce energy costs.

Renewable plant operators combine load forecasting with generation forecasts to manage battery storage dispatch. Predictions help decide when to charge/discharge batteries based on net demand and price signals.

Provide a cloud-based API that serves real-time and batch electricity forecasts. Customers pay based on forecast horizon, frequency (hourly/daily), and number of data feeds.

Offer end-to-end consulting to utilities and large energy consumers, including data pipeline setup, model selection, hyperparameter tuning, and deployment. Deliver tailored solutions with ongoing maintenance contracts.

License the forecasting engine as an embedded module within existing energy management software (EMS) or building management systems (BMS). Revenue from per-instance licensing or royalty per device.