EnergyLens

Adaptive Foundation Model-Driven Short-Term Load Forecasting and Anomaly Intelligence for Indian Buildings using IBM’s Time Series Foundation Models, multi-horizon forecasting, calibrated uncertainty

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EnergyLens: Adaptive Foundation Model-Driven Short-Term Load Forecasting and Anomaly Intelligence for Indian Buildings

Inspiration

India’s building sector faces rising energy demand variability from heterogeneous usage, growing HVAC adoption, and rooftop solar integration. Traditional statistical and single-building ML models fail to generalize, leading to poor forecasts and delayed anomaly detection.

What it does

EnergyLens leverages IBM’s Time Series Foundation Models (TSFMs) for multi-horizon probabilistic forecasting (15-min to 24h ahead) with calibrated uncertainty. It integrates a dual anomaly detection engine (residual + reconstruction methods) and uses Granite-powered natural language explainers to turn model attributions into actionable insights. With IBM’s Agent Development Kit (ADK), autonomous agents handle retraining, anomaly triage, and demand response recommendations.

How we built it

  • Parameter-efficient fine-tuning (LoRA/adapters) across diverse Indian building cohorts
  • Exogenous signals: weather, calendar features, occupancy proxies
  • Probabilistic outputs via quantile regression + conformal calibration
  • Anomaly consensus logic for robust fault detection
  • Agentic workflow for retraining, drift monitoring, and reporting

Challenges we ran into

  • Handling sparse, noisy, and heterogeneous building data
  • Balancing generalization with site-specific adaptation
  • Designing explainability that is actionable, not just technical

Accomplishments

  1. Adapted foundation models to Indian building contexts
  2. Integrated uncertainty-aware forecasting with explainable anomaly detection
  3. Built an autonomous agentic pipeline for scalable deployment

What we learned

  • Foundation models significantly reduce cold-start issues
  • Dual-method anomaly detection improves robustness
  • Natural language explanations make ML outputs actionable for operators

What’s next

  • Expand to larger cohorts of commercial and residential sites
  • Incorporate demand response optimization at scale
  • Extend to renewable integration and microgrid management

Impact: ≥10–15% improvement in forecasting accuracy, faster anomaly discovery, risk-aware scheduling, and scalable support for India’s grid stability and decarbonization.

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