Energy Prediction Models

BuildingLogiX uses machine-learning models trained on historical building telemetry — weather, occupancy, schedules, metered loads, and equipment state — to forecast campus and building-level energy consumption. The models support short-horizon operational forecasts (hour / day ahead) as well as long-horizon budgeting and Measurement & Verification (M&V) baselines.

Output

Illustrative chart — synthetic data shown for demonstration.

What we use it for

• Day-ahead and week-ahead load forecasting to support demand response, peak shaving, and thermal storage dispatch.
• Weather-normalized baselines for M&V on energy conservation measures (IPMVP Option C).
• Budget and utility-bill forecasting at the building and portfolio level.
• Anomaly detection — deviations from predicted load flag metering errors, off-schedule operation, or equipment drift.

Approach (high level)

• Feature engineering from BDXpy time-series data: outdoor air temp, enthalpy, degree-day bins, time-of-week, occupancy proxies, rolling lags.
• Model families: gradient-boosted trees for robustness on mixed-feature sets, regularized regression for interpretable M&V baselines, and recurrent / temporal-fusion models where sub-hourly dynamics matter.
• Automated retraining and drift monitoring via BDXpy data services.

Proprietary implementation

The specific feature pipelines, model architectures, and tuning procedures are part of BuildingLogiX's proprietary analytics stack. Reach out if you'd like to discuss applying these models to your portfolio.