Forecasting and balancing
Demand and generation forecasts sharpened by machine learning, so balancing decisions, trading positions, and reserve holdings start from a better number.
Sector programmes · Energy and utilities
AI in energy and utilities,
reliable when it matters.
Independent, standards-led support for energy companies, network operators, and water utilities putting AI to work: grounded in Ofgem and Ofwat expectations and the obligations of critical national infrastructure, free of vendor incentives.
— State of play —
The grid got harder
to run by hand.
A system built around a few dozen predictable power stations now balances millions of variable inputs: rooftop solar, wind fleets, batteries, and electric vehicles, all moving with the weather. Forecasting and balancing at that complexity is exactly the problem machine learning is good at, and the system operator already uses it to predict supply and demand more accurately. Across water, the same techniques are finding leaks that have evaded detection for decades.
Utilities also carry obligations most sectors never face. This is critical national infrastructure, regulated for security and resilience, where a model failure can mean a missed balancing decision or a vulnerable customer wrongly chased for debt. Regulators have made clear that accountability for AI-influenced decisions stays with the licensee. Adopting at pace while honouring that bar is where most organisations need help.
~33%
improvement in solar forecasting accuracy reported by the system operator
2030
clean power target the grid must hit
~3bn
litres of water lost to leakage every day across England and Wales
— Use cases —
Where AI is already
earning its keep.
The strongest energy and utilities use cases improve forecasts and asset decisions while engineers keep their hands on the controls.
Renewables output
Wind and solar production predicted against live weather, cutting curtailment and making variable generation easier to commit with confidence.
Asset health
Condition monitoring and inspection imagery analysed at scale, so maintenance budgets flow to the assets most likely to fail, not just the oldest.
Leakage and networks
Acoustic and flow data turned into leak detection that finds losses decades of walking the pipes never could.
Customer operations
Billing queries, outage updates, and debt pathways handled faster, with vulnerable customers identified for protection rather than pursuit.
Resilience and security
Anomaly detection across operational networks that strengthens the security posture critical national infrastructure is required to maintain.
— How the Institute helps —
One institute,
three kinds of help.
Advice, engineering, and professional standards from a single independent body, with no software to sell.
Consultancy
Strategy, assurance, and regulation
Independent advisory for executives and boards setting an AI position that satisfies the regulator, the security authorities, and their own engineers.
- AI strategy aligned to licence obligations
- Assurance frameworks for critical systems
- Readiness assessment and a costed roadmap
Solution design
Working systems, handed over
The Institute's engineering practice designs and builds AI systems around your operational estate and resilience obligations, then hands them to your team.
- Forecasting, asset, and customer tools
- Integration with control and field systems
- Knowledge transfer to your own engineers
Professional body
Standards, training, and research
As the UK's professional body for AI, the Institute trains and accredits staff, publishes research, and maintains the UK AI Readiness Charter.
- Staff training and accreditation pathways
- The UK AI Readiness Charter, free to sign
- Published research and sector guidance
— Sector considerations —
What we watch
most closely.
01
Resilience
Failure modes matter more than accuracy
On critical national infrastructure, the question is not how good the model is on an average day but what happens on the worst one. Every operational deployment has a degraded mode and a manual fallback that gets exercised, not just documented.
0
critical decisions without a manual fallback
02
Engineering
Models defer to engineers
A forecast or asset-health score is an input to an engineering judgement, not a replacement for one. Operational models are reviewed by the engineers who own the assets, and overriding the model is always a button, never a battle.
100%
of asset models reviewed by engineers
03
Customers
Vulnerability is not a segment to optimise
Models that touch billing, debt, or disconnection pathways operate under consumer protection duties and real human stakes. Customer-facing models are checked for their impact on vulnerable households before launch and audited after.
0
enforcement actions driven by a model alone
04
Data
Smart meter data reveals lives
Half-hourly consumption data shows when a household wakes, sleeps, and goes away. Using it for forecasting and tariffs is legitimate; the governance has to be tight enough that it could never be used for anything else.
30min
granularity of data handled under strict purpose limits
05
Transition
AI must serve the transition
The case for AI in this sector is ultimately the case for a cheaper, cleaner, more reliable system. Every deployment should be able to show its contribution to that, including an honest account of its own energy footprint.
2030
the deadline every deployment serves
Set your organisation's
AI position.
A short call with the energy and utilities team. Plain answers, a clear next step, and no software to sell.
— Go deeper
— Consultancy
Independent advice
Standards-led advisory for boards and leaders, with no software to sell.
— Solutions
Need it built?
Working AI systems designed and built by the Institute's engineering practice.
— Sectors
Explore other sectors
The full directory of IoAI sector programmes, grounded in UK regulation.