AI system
- System
-
Quartz Solar (Open Climate Fix machine-learning solar PV forecast, formerly PVNet)
- Model
-
PVNet.
- Release
-
Exact release not established.
- Application developer
-
Open Climate Fix
- Role
-
The model forecast electricity generation from solar panels.
- Configuration
-
Not assessed
- Inputs and outputs
-
Not assessed
- Autonomy
-
Not assessed
- Workflow
-
Satellite imagery, numerical weather data and historical generation data for a national PV fleet. A system operator innovation program (NIA) that funded testing before operational integration. A forecast refresh cycle faster than the operator's existing six-hourly weather updates.
- Human oversight
-
Not assessed
- Relation to AI
-
The machine-learning forecast is the system in use. But both figures (£30m, 300,000 t CO2) come from Open Climate Fix's announcement reproduced by trade press. NESO's own quoted statement confirms deployment and 36-hour accuracy only. No NESO-authored page or regulator document with a savings figure was found.
- AI contribution
-
Not assessed
Benefits
- Outcome identifiers
-
Not assessed
- Benefit
-
at least £30m a year in avoided imbalance costs
- Category
-
Improved Organizational Efficiency
- Status
-
Not assessed
- Beneficiary groups
-
Organizations. Society.
- Beneficiaries
-
Not assessed
- Reach
-
Not assessed
- Distribution and access
-
Not assessed
- Duration
-
Not assessed
- Expected?
-
Yes
- Costs and harms
-
Not assessed
Measurement
- Studies
-
Not assessed
- Study design
-
Not assessed
- Measurement
-
Not assessed
- Setting and sample
-
Not assessed
- Measures
-
at least £30m a year in avoided imbalance costs
- Comparison
-
NESO's solar forecasts before Quartz Solar was adopted
- Results
-
Reference: forecasting errors before adoption (not quantified). Result: halved forecasting errors.
- Additional value from AI
-
Not assessed
- Limitations
-
Not assessed
- Denominators
-
Not assessed
- Materials for replication
-
Not assessed
Money
- Kind of value
-
imbalance cost avoided per year
- Amount
-
Estimate: GBP 30 million
- Currency and prices
-
GBP, stated in the source. Nominal source amount; price year not separately stated.
- Period and unit
-
Annualized estimate published November 6, 2025; no completed measurement year is specified. Annual electricity imbalance costs.
- Realization
-
Annualized estimate.
- Recipients and payers
-
Recipient: Great Britain’s electricity system and its cost bearers. Payer: Electricity balancing and imbalance settlement participants.
- Gross or net
-
Estimated annual gross operating-cost avoidance, without implementation and ongoing service costs.
- Included costs
-
Not assessed
- Comparison
-
Previous NESO forecasting tools; the monetary calculation is not provided.
- Method
-
Not assessed
- Return and payback
-
Not assessed
People and places
- Participants
-
Developer: Open Climate Fix. Operator: National Energy System Operator (NESO).
- Jurisdictions
-
Benefit:
United Kingdom. Developer: United Kingdom. Operator: United Kingdom. - Ownership and control
-
Not assessed
- Dependencies
-
Not assessed
- Cross-border connections
-
Not assessed
- Geopolitical implications
-
Not assessed
Follow-up
- Conditions for replication
-
Access to live satellite and generation data. An operator willing to integrate a third-party forecast into reserve setting. Independent measurement of imbalance-cost changes, which is absent here.
- Replication attempts
-
Not assessed
- Later findings
-
No development dated after the November 2025 go-live reports was found.
- Availability
-
Not assessed
- Institutional decision
-
Not assessed
- Grounds
-
Not assessed
- Action and result
-
Not assessed
- Success criterion
-
Not assessed
Evidence
- Source numbers
-
AIBD-0163-source-0001–0004
- Citations
-
Numbered references below.
- Language
-
Not assessed
- Source relationships
-
Independent, Developer
- Original evidence
-
Not assessed
- Access and versions
-
Not assessed
- Supported findings
-
Not assessed
- Evidence links
-
Not assessed
- Evidence strength
-
Not assessed
- Missing evidence
-
Not assessed
Grades
- Assessed benefits
-
Organizations. Society.
- Depth
-
Not assessed
- Reasons
-
Organizations: NESO confirms operational use and 36-hour forecasting. The £30m saving is OCF's estimate, so no measured outcome is documented. Society: The 300,000-tonne CO2 figure is the maker's estimate. No independent measurement is documented.
- Observed or intended
-
Not assessed
- Rubric
-
Not assessed
- Assessor and date
-
Not assessed
- Alternative assessments
-
Not assessed
- Fill
-
Independently reported
Entry details
- Kind
-
Not assessed
- Related entries
-
Not assessed
- Field
-
Energy. Electricity system operation and balancing.
- Purpose
-
Quartz Solar is built to forecast national and regional solar PV output for grid balancing. Operational use for reserve setting is the intended purpose.
- Dates
-
Date basis unassessed: 2025 (precision unassessed). Updated: 21 October 2026.
- Stage
-
Not assessed
- Version
-
SHA-256: 762f56e9d617…
References
- Other identifiers
-
Not assessed
- Category mappings
-
Not assessed
- Brief
-
Not assessed
- Revisions
-
6 revisions. Latest: 21 October 2026.
Sources
-
News report Independent author
Tim McManan-Smith, 'National Energy System Operator adopts AI solar forecasting to save £30m', The Energyst, 6 November 2025
developed a cutting-edge tool that can accurately predict solar generation up to 36 hours in advance—revolutionising our solar forecasts in the control room.
helping to avoid at least £30 million in imbalance costs each year and cutting around 300,000 tonnes of CO₂ emissions.
-
News report Independent author
Matthew Lynas, 'AI powered solar forecasting helps UK grid operator reduce balancing costs', pv magazine, 7 November 2025
Quartz Solar says its tool saves Great Britain's grid operator GBP 30 million ($39 million) per year through more accurate forecasting
-
Press release Developer
Open Climate Fix website, product spotlight, GB National Solar Forecast
Used daily by the National Energy System Operator, as well as some of the top power trading desks across the UK.