AI for Energy and Utilities

Time-series models trained on your historical sensor data, vibration, temperature, oil analysis, current draw, combined with your maintenance records and failure history. Scores each asset by current failure probability and surfaces the contributing sensor signals. Gives maintenance teams a prioritised work list based on actual failure risk rather than calendar-based schedules. Applicable to transformers, switchgear, rotating machines, pumps, and pipeline equipment. Prediction horizon is typically 2-4 weeks before failure onset.

ML models trained on historical interval load data, weather signals, and calendar features to forecast demand at the feeder, substation, or zone level. Day-ahead and week-ahead forecasts with confidence intervals feed procurement, dispatch, and switching decisions. Reduces reserve procurement cost by narrowing the uncertainty band around the forecast. For distribution utilities managing renewable intermittency, demand forecasting combined with generation forecasting improves real-time dispatch quality.

Models trained on your SCADA sensor data that learn the normal operating envelope for each pipeline segment or grid zone. Flags pressure, flow, and temperature deviations that fall outside the model's expected range for the current operating conditions. Alerts surface to control room operators with the contributing sensor signals and the time window of the deviation. Detects slow leaks and equipment degradation events days before they become reportable incidents. Calibrated to reduce false positive alert volume for operators.

AI routing that schedules and sequences field technician work orders by asset failure probability, geographic proximity, crew skill, and vehicle availability. High-risk assets get scheduled first. Travel time and fuel cost are minimised across the day's work list. Re-routes dynamically when emergency callouts arrive. Integrates with your existing work order management system. Reduces technician drive time and increases the number of work orders completed per crew per day without adding headcount.

The optimisation model runs as a mixed-integer programming solver (Google OR-Tools or similar) over the full day's work order set, balancing asset criticality weights against travel distance matrix derived from real road network data. When a priority-1 callout arrives mid-shift, the model re-solves the remaining schedule in under 30 seconds and dispatches re-route instructions via the field mobile app. Crew skill matrices ensure a transformer oil sampling work order is only assigned to a technician certified for high-voltage proximity work. Integration connects to your CMMS (IBM Maximo, SAP PM, or Infor EAM) via REST API to pull open work orders and push completion status without duplicating data entry. Fleet telemetry from vehicle GPS feeds actual position data rather than assumed depot locations for real-time re-routing accuracy.

Short-term and day-ahead output forecasts for solar and wind assets using weather model inputs, historical production data, and equipment performance curves. Forecasts at the asset and portfolio level with uncertainty bounds. Feeds dispatch decisions, grid scheduling submissions, and imbalance cost reduction. For operators managing curtailment decisions, output forecasting at 15-minute resolution improves the accuracy of curtailment scheduling and reduces revenue loss from over-curtailment.

Solar output models combine NWP (Numerical Weather Prediction) irradiance forecasts from ECMWF or NOAA GFS with sky camera imagery (where available) and historical clear-sky index data to estimate plane-of-array irradiance, then apply panel efficiency curves and inverter loss factors to produce AC output forecasts. Wind models use hub-height wind speed and direction forecasts combined with turbine power curves and wake loss corrections for multi-turbine sites. For both asset classes, a gradient boosting model (XGBoost or LightGBM) is trained on 12-24 months of historical production data to correct systematic NWP bias at your specific site location. Forecast horizon: 15-minute resolution out to 6 hours (intra-day), hourly resolution out to 72 hours (day-ahead and two-day-ahead). Forecast uncertainty is quantified as prediction intervals (P10/P50/P90) rather than point estimates, giving your dispatch team the information to make risk-adjusted scheduling decisions.

Models trained on smart meter interval data and customer billing history that detect readings inconsistent with a customer's consumption pattern. Flags potential meter faults, non-technical losses (theft), and billing errors before they accumulate across billing cycles. For large commercial customers, consumption anomalies surface in near real time. For residential customers, anomaly flags trigger a review cycle before the bill is issued. Reduces revenue leakage and reduces the volume of billing dispute calls that reach your contact centre.

The detection model builds a consumption baseline per customer using their 12-24 months of interval meter data, segmented by day type (weekday, weekend, holiday) and season. Isolation Forest is used to identify consumption windows that deviate from the expected distribution for that customer's segment and tariff class, reducing false positives that fire on legitimate consumption spikes (heatwaves, industrial production ramp-ups). Z-score thresholds applied to rolling 7-day and 30-day consumption windows catch slow drift anomalies (meter running fast or slow) that point anomaly detection misses. For smart meters transmitting interval data via AMI (Advanced Metering Infrastructure) over DLMS/COSEM or ANSI C12.22 protocols, near-real-time anomaly detection runs on the data stream within minutes of meter transmission. Anomaly flags are categorized: suspected meter fault (consistently low readings), suspected non-technical loss (reads drop to near-zero on an active commercial account), and billing discrepancy (billed consumption inconsistent with interval data sum). Each category triggers a different downstream action: field inspection, fraud investigation workflow, or billing correction, respectively.