Low-accuracy demand forecasting
Load predictions based on simple historical data without incorporating temperature, holidays, events, or flexible demand signals. Error paid in capacity reserves or real-time imbalances.
Utilities, distributors, and energy operators manage critical assets with operational data that is rarely fully exploited. Forecasting that sharpens dispatch, anomalies detected before failure, non-technical losses located before they escalate — business first, telemetry second.
The energy sector has spent a decade investing in SCADA, smart meters, asset sensors, and grid management systems. The data is there — the problem is that operation, maintenance, and planning decisions are still made with tools that do not cross those sources or predict what is about to happen.
Load predictions based on simple historical data without incorporating temperature, holidays, events, or flexible demand signals. Error paid in capacity reserves or real-time imbalances.
Transformers, lines, and substation equipment maintained on schedule or failing before the maintenance cycle arrives. Unplanned downtime cost multiplies the cost of advanced preventive maintenance.
Gap between injected and billed energy living in the "losses" line without diagnosis of cause or zone. Fraud, illegal taps, or metering errors assumed as structural cost.
Control room acting on alarms — reactive by design. The ability to anticipate incipient overloads, voltage anomalies, or risk conditions in assets is in the data but not in the systems.
Five cases with demonstrable ROI at 12-18 months for operators with structured operational data. The entry condition is not having everything digitized — it is having some clean data and a critical process to improve.
Models combining consumption history, temperature, holidays, weather forecast, and flexible demand signals to predict load by zone or node with hourly and daily horizons. Dispatch adjusts reserves and scheduling before the imbalance occurs.
Forecasting error reduced 20-35%. Capacity reserves optimized. Fewer real-time imbalances to resolve at high marginal cost.
Models on telemetry data from critical transformers, switches, and lines that estimate failure probability within a time window. Maintenance is scheduled before the asset fails — not after.
Unplanned downtime on monitored assets reduced 25-40%. Total maintenance cost falls by eliminating emergencies and optimizing the preventive calendar.
Continuous analysis of injection-versus-billing differences by zone, correlated with historical consumption patterns, meter behavior, and reading variables. The system flags priority zones and customers for inspection with a risk score.
Revenue recovery from localized non-technical losses. Targeted inspections with 3-5x higher success rate than random inspection.
Continuous SCADA measurement monitoring to detect out-of-range behavior: incipient overloads, voltage asymmetry, insulation degradation. Prioritized alerts to the control room before they become an alarm or failure.
Incident response time reduced. Cascade failures avoided by early action. Control room with less alarm noise and more actionable information.
An assistant managing customer communications about outages, readings, and billing: reports outage status in real time, opens incidents, requests data, and transfers to a technician when needed. No queues at a saturated call center during mass outages.
60-70% reduction in repetitive call center calls during incidents. Average complaint handling time drops. Customer NPS during outages improves from proactive information.
We work with the sector's operational systems (OSIsoft PI/AVEVA, ABB Ability, Schneider EcoStruxure, SAP IS-U, proprietary systems) in read-only mode — without intervening in grid operation. Models are deployed on operator-controlled infrastructure with architectures respecting OT/IT segregation.
Audit of available data (SCADA, meters, maintenance, billing) and critical processes. We identify the 2-3 cases with the best ROI for your asset type and operating area.
Implementation of forecasting, predictive maintenance, or loss detection on a defined perimeter (geographic zone, asset fleet, or customer type).
For utilities wanting an AI layer across all operations: forecasting, maintenance, losses, and customer service on a common architecture.
A 30-minute call about your assets, data systems, and specific operational pain points. We leave with 2-3 candidate cases and an 18-month ROI estimate.