Demand forecasting
Compare forecasts with a seasonal baseline and measure errors during the periods that matter operationally.
Sector intelligence
Energy & climate
Better forecasts are useful when operations can respond.
Where to put AI to work
Practical opportunities to investigate. Results depend on your data, process and evaluation.
Asset monitoring
Prioritise maintenance from sensor signals while tracking missed faults and unnecessary interventions.
Resource accounting
Include the energy and infrastructure cost of the AI service in any claimed efficiency benefit.
What matters in this sector
Energy savings depend on what is being measured. Cooling electricity is different from the total electricity bill, and a quieter production period can look like an improvement. Define the boundary, operating conditions and people authorised to change settings before a trial.
A sensible first test
Start with one building or process. Compare consumption under similar occupancy, weather or output conditions. Keep safety limits and manual control in place. Count monitoring and support costs as well as possible savings.
Cases worth learning from
2024–2025
TITAN
Catch equipment problems earlier and help a cement plant use less energy.
What happened and what to learn ↗
20 July 2016
Google DeepMind · cooling
Help cooling equipment use less energy while keeping a data centre within operating limits.
What happened and what to learn ↗Measure
Forecast error in critical periods, avoided outages and net resource consumption.
Watch
Extreme conditions outside the training sample and savings calculated without system costs.
Make the first move concrete
One workflow. One baseline. One accountable owner.
Choose a repeatable task. Record its current performance and the failures that matter. Test on a controlled sample, compare with the existing process, and decide what evidence would justify extending the trial.
Compare your opportunities