Useful intelligence.
Clear operational purpose.

Grid intelligence

Forecast demand before committing capacity

Combine historic load, weather and known customer changes to compare baseline and stress scenarios. Evaluate forecast errors by location and time horizon, rather than reporting a single headline accuracy figure.

Decision: where further network studies or flexibility could be needed. Required inputs: validated load histories, network constraints and planned connections. Limit: a forecast cannot establish connection approval or system security.

Make flexibility visible

Explore when distributed generation, storage or demand response might shift the timing of net load. A credible deployment needs asset availability, constraints, settlement rules and operator approval, alongside time-series modelling.

Energy operations

Find equipment issues earlier

Use condition signals and maintenance histories to flag unusual behaviour for engineering review. Measure false alarms and missed failures. A flagged anomaly should lead to investigation, not an automatic equipment shutdown.

Improve industrial energy decisions

Compare energy intensity across products, shifts and operating conditions. Validate apparent savings against production output, weather and other changes. A proposed reduction in a model is not evidence of achieved savings.

Bring renewable forecasts into planning

Use weather and generation history to estimate renewable output. Express uncertainty and reserve requirements explicitly. Annual renewable volumes alone cannot prove hour-by-hour supply or carbon-free operation.

Powering AI infrastructure

Translate IT power, utilisation and PUE into annual facility-energy estimates. Use those estimates to frame discussions about tariffs, connection capacity, cooling and carbon accounting. Actual sites need measured load profiles, redundancy and power-quality studies.