AI for energy and utilities that sees asset risk before it becomes an outage.

Predictive maintenance models for energy assets use time-series sensor data to detect the early signatures of equipment degradation before failure. For a transformer, the relevant signals include oil temperature, dissolved gas analysis readings, load current, and ambient temperature over time. For a rotating machine such as a turbine or pump, vibration frequency spectra, bearing temperatures, and oil pressure are the primary signals. The model learns the normal operating signature of each asset class and identifies deviations from that baseline that correlate with historical failure events in your maintenance records. Output is a ranked list of assets by current failure probability, the contributing sensor signals, and a recommended inspection or maintenance action. The model operates on a rolling window of sensor data, typically daily or hourly, and updates the risk ranking continuously. For assets where failure causes significant outage cost or safety risk, a 2-4 week prediction horizon gives maintenance teams enough time to plan and execute the intervention before failure occurs.

A grid demand forecasting model for distribution-level management needs historical load data at the granularity you want to forecast, typically hourly or 30-minute interval data at the feeder or substation level, going back 2-3 years. Beyond historical load, the model improves significantly with weather data: temperature is the strongest external driver of electricity demand, but humidity, wind, and solar irradiance are also relevant. Calendar features, day of week, public holidays, school terms, capture regular demand patterns that weather alone doesn't explain. For distribution utilities serving industrial customers, industrial production schedules and shift patterns are significant inputs. Output is a day-ahead or week-ahead load forecast by feeder or zone with confidence intervals. The forecasts feed procurement decisions (how much reserve to commit), dispatch scheduling, and network switching decisions. We assess your metering infrastructure and historical load data availability in discovery to determine the achievable forecast granularity and accuracy.

Pipeline anomaly detection uses the continuous sensor readings from your SCADA system, pressure at multiple points along the pipeline, flow rates, temperature, and valve positions, to detect deviations from the expected operating envelope. A baseline model learns the normal relationship between these sensor readings under different operating conditions: flow rate, ambient temperature, product type, and pressure profile. When a sensor reading or a combination of readings deviates from the predicted baseline by more than a threshold, an alert is generated. The alert includes the sensor IDs, the magnitude of the deviation, and the time window over which it developed. This is designed to surface two types of events: slow leaks that develop gradually over hours or days (a gradual pressure drop below the model's expected value for the current flow conditions) and rapid events such as a rupture or valve failure. The detection threshold is calibrated to minimize false positives, reducing alert fatigue for control room operators, while maintaining sensitivity to genuine anomalies.

Energy consumption optimization for commercial buildings uses building management system data, HVAC set points, occupancy sensors, sub-metering data by zone, and external weather, to reduce energy use while maintaining comfort targets. A model learns the thermal dynamics of the building: how long it takes to cool or heat each zone given the current weather, occupancy, and equipment settings. This allows the model to pre-cool or pre-heat a building during off-peak tariff periods rather than running HVAC at full load during peak tariff hours. For buildings with demand charges (billed on peak 15-minute demand), the model manages load across the building to shave the demand peak. For a commercial building with annual energy costs above USD 200,000, consumption optimization typically reduces energy cost by 10-20%. We assess your BMS data access and metering infrastructure in discovery.

Cost depends on the scope: a focused predictive maintenance model for a single asset class (transformers, for example) with data you already have in a historian typically runs USD 40,000-80,000 for the first build. A multi-system AI platform covering demand forecasting, anomaly detection, and field service routing for a distribution utility is a larger engagement priced in discovery. Every project is scoped at a fixed price before development starts. You see the cost breakdown, the deliverables, and the timeline in week 1 before any development begins.

Yes. We sign NDAs before any discovery conversation that involves operational data, SCADA architecture, or proprietary asset performance records. For US utilities, we are familiar with NERC CIP data handling requirements. For Australian and UK energy clients, we apply the same data classification and access controls we use for HIPAA-regulated healthcare data. Data used to train models is never retained beyond the engagement unless you explicitly authorize it.