What matters
The problem comes before the model.
- Costs and break-even analysis for hydrogen, ammonia and fertilisers.
- Renewable generation forecasts and energy-stress indicators.
- Experimental utility optimisation and analysis of the AI supply chain’s energy demand.
Lab Intelligence approach
Sources, assumptions, checks, versions, metrics and limitations remain visible. A more complex technique is used only when it adds value over an understandable baseline.
How the method works →Related workflows
Examples already documented.
These pages describe implementations or experiments from the laboratory; they do not imply that the same result transfers automatically to another context.
Hydrogen Route Observatory
Compares grey SMR, SMR+CCS and electrolysis using public/configured energy prices, chemical balances, emissions boundaries and break-even thresholds.
Open project →WF-12Ammonia & Fertilizer Chain
Propagates gas cost through the NH₃→urea chain while separating the physical calculation, nowcast and falsifiable M+1 forecast.
Open project →WF-13Italy Variable Renewable Forecast
Forecasts the Europe/Rome D+1 civil day for solar, wind and total VRE, normalising by capacity and archiving point-in-time vintages.
Open project →WF-6Energy Crisis Thermometer
Combines energy, logistics and financial stress into a traceable 0–100 index with drivers, forecast and state machine.
Open project →WF-16Energy & Steam Optimizer
Optimises a modelled HP/MP/LP steam network with utility demand based on synthetic data (simulated data) and real/configured gas, power and carbon prices against a frozen reference dispatch policy.
Open project →WF-4AI Supply Chain
Reads the AI economy as a network of semiconductors, memory, data centres, power and cooling.
Open project →Want to discuss a similar case?
Describe the problem and expected result in non-confidential terms. The first step is to identify the data, criteria and limitations that would make the case verifiable.