AI Demand Forecasting — EnerFlow Utilities
Visual: short-term load forecast, feeder-level maps, and peak management recommendations.
Client Overview
EnerFlow Utilities is a regional distribution utility serving multiple municipalities. Rapid urbanization, variable industrial loads, and new distributed generation (rooftop solar) complicated load forecasting and distribution planning. EnerFlow sought accurate short- and medium-term forecasts to optimize dispatch, reduce losses, and defer expensive capacity upgrades.
Challenge
Conventional heuristics and seasonal averages were insufficient for feeder-level accuracy in the presence of rooftop PV, EV charging, and industrial variability. EnerFlow needed models that incorporated weather, calendar effects, behavioural signals, and distributed generation forecasts to minimize balancing costs and avoid unnecessary capex.
Solution — Multi-horizon Load Forecasting & Peak Tools
We built a forecasting platform that produces rolling forecasts from 5-minute horizons up to 30 days, aggregated to feeder and substation levels, and coupled with actionable peak management playbooks (demand response, storage dispatch suggestions, and targeted tariff signals).
Core features
Approach
- Ingest SCADA, smart-meter aggregates, weather forecasts, calendar/events and mobility proxies.
- Train probabilistic models (quantile regressors / ensembles) and validate on holdout windows including holiday/promo events.
- Integrate PV forecasting pipelines and generate net-load projections at feeder level.
- Run operator-in-the-loop simulations for demand response triggers and storage dispatch recommendations.
Technology stack
Implementation — Phases
Phase 1 — Data & Pilot Feeders (Weeks 1–10)
Select pilot feeders, align meter/SCADA signals, and run baseline backtests across seasonal windows.
Phase 2 — Model Build & Validation (Weeks 11–20)
Train multi-horizon ensembles, validate quantiles, and stress-test against PV and EV scenarios.
Phase 3 — Operational Integration (Weeks 21–30)
Expose forecasts to control room dashboards, integrate mitigation playbooks and storage dispatch recommendations.
Phase 4 — Scale & Policy (Weeks 31–40)
Scale across feeders, embed into planning cycles to defer investments and operationalize demand response.
Impact & Results
20%
Reduction in balancing cost during peak windows
15%
Improvement in feeder-level forecast accuracy (MAPE)
Deferred
Capacity upgrades due to better utilization & DR
10 months
From pilot to operational utility forecasts
Qualitative outcomes
- Control-room teams gained confidence to act on probabilistic forecasts paired with suggested mitigations.
- Better PV aggregation forecasting reduced unexpected net-load swings.
- Planners used scenario outputs to prioritize cost-effective investments and DR programs.
Client Testimonial
Key Highlights & Learnings
- Probabilistic forecasts with scenarios are more useful for operations than point estimates alone.
- Net-load forecasting depends critically on reliable distributed generation estimates.
- Operator simulations and suggested mitigations are required to make forecasts operationally useful.