{ "id": "AIBD-0163", "title": "Open Climate Fix's Quartz Solar goes live in NESO's control room and may avoid £30m a year in imbalance costs", "summary": "Open Climate Fix's machine-learning solar forecast is live in NESO's control room; OCF claims at least £30m a year in avoided imbalance costs, a figure NESO's quoted statement does not give.", "date_observed": "2025-11-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "On 6 November 2025 Open Climate Fix (OCF), a not-for-profit, announced that Quartz Solar was live in the National Energy System Operator's (NESO) control room. OCF's AI-powered solar PV forecasting tool combines machine learning, live satellite imagery and weather data. It is fully integrated from day-ahead scheduling to real-time control after starting as a Network Innovation Allowance project (The Energyst, 6 November 2025). The announcement says the tool helps avoid at least £30 million in imbalance costs a year and cuts around 300,000 tonnes of CO2. NESO's Carolina Tortora, Head of AI, Digitalisation and Innovation, is quoted saying it can accurately predict solar generation up to 36 hours in advance. She says it is revolutionizing solar forecasts in the control room. She does not state the £30m figure. pv magazine (7 November 2025) attributes the £30 million (US$39 million) per year saving to Quartz Solar itself. It reports OCF's claim of halving forecasting errors across about 300 grid supply points. OCF's website says the forecast is used daily by NESO. The savings are the maker's estimate, not an operator-reported or audited figure.", "category": "general benefit", "unexpected": false, "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.", "system": "Quartz Solar (Open Climate Fix machine-learning solar PV forecast, formerly PVNet)", "model_version": "PVNet", "developer": "Open Climate Fix", "deployer": "National Energy System Operator (NESO)", "location": "United Kingdom", "domain": "Energy", "segment": "Electricity system operation and balancing", "benefit_type": "Improved Organizational Efficiency", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": null, "reason": "NESO confirms operational use and 36-hour forecasting. The £30m saving is OCF's estimate, so no measured outcome is documented." }, { "group": "societal", "depth": null, "reason": "The 300,000-tonne CO2 figure is the maker's estimate. No independent measurement is documented." } ], "ai_role": { "status": "unassessed", "statement": "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." }, "evidence": [ { "kind": "news report", "citation": "Tim McManan-Smith, 'National Energy System Operator adopts AI solar forecasting to save £30m', The Energyst, 6 November 2025", "title": "National Energy System Operator adopts AI solar forecasting to save £30m", "url": "https://theenergyst.com/national-energy-system-operator-adopts-ai-solar-forecasting-to-save-30m/", "date": "2025-11-06", "written_by": "independent", "quote": "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." }, { "kind": "news report", "citation": "The Energyst, 6 November 2025 (claim from Open Climate Fix's announcement)", "title": "National Energy System Operator adopts AI solar forecasting to save £30m", "url": "https://theenergyst.com/national-energy-system-operator-adopts-ai-solar-forecasting-to-save-30m/", "date": "2025-11-06", "written_by": "independent", "quote": "helping to avoid at least £30 million in imbalance costs each year and cutting around 300,000 tonnes of CO₂ emissions." }, { "kind": "news report", "citation": "Matthew Lynas, 'AI powered solar forecasting helps UK grid operator reduce balancing costs', pv magazine, 7 November 2025", "title": "AI powered solar forecasting helps UK grid operator reduce balancing costs", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/", "date": "2025-11-07", "written_by": "independent", "quote": "Quartz Solar says its tool saves Great Britain's grid operator GBP 30 million ($39 million) per year through more accurate forecasting" }, { "kind": "press release", "citation": "Open Climate Fix website, product spotlight, GB National Solar Forecast", "title": "Open Climate Fix", "url": "https://openclimatefix.org/", "date": null, "written_by": "makers", "quote": "Used daily by the National Energy System Operator, as well as some of the top power trading desks across the UK." } ], "replication": { "produced_by": [ "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" ], "must_hold": [ "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" ], "echoes": [] }, "paired_incident": null, "headline": "Quartz Solar goes live at NESO and may avoid £30m a year in costs", "metric": "at least £30m a year in avoided imbalance costs", "comparator": { "against": "NESO's solar forecasts before Quartz Solar was adopted", "before": "forecasting errors before adoption (not quantified)", "after": "halved forecasting errors", "basis": "measured", "quote": "Since adoption, Quartz Solar has halved forecasting errors, according to Open Climate Fix, equivalent to saving at least GBP 30 million in imbalance cost per year, with potential to increase to GBP 150 million ($197.0 million) per year in 2035 if government solar capacity targets are met.", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/" }, "later": { "status": "unknown", "date": null, "note": "No development dated after the November 2025 go-live reports was found.", "quote": null, "url": null }, "value": { "amount": "GBP 30 million", "what": "imbalance cost avoided per year", "who": "Great Britain's electricity system operator NESO and, through balancing costs, consumers", "estimate": true, "quote": "Since adoption, Quartz Solar has halved forecasting errors, according to Open Climate Fix, equivalent to saving at least GBP 30 million in imbalance cost per year, with potential to increase to GBP 150 million ($197.0 million) per year in 2035 if government solar capacity targets are met.", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/" }, "developer_country": "United Kingdom", "deployer_country": "United Kingdom", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version, the value as stated from the cited sources.", "source": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/; https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/; https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://find-and-update.company-information.service.gov.uk/company/11833152; https://en.wikipedia.org/wiki/National_Energy_System_Operator", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently 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The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0163", "catalog_version": "1.0.0", "source_fingerprint": "762f56e9d61746a84c50f2b1819e0cdc4e735befec7df93f8a91c6565106df5f", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0163-source-0001", "AIBD-0163-source-0002", "AIBD-0163-source-0003", "AIBD-0163-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0163-legacy-value", "DIV-0032" ], "role": "Existing monetary observations and Fructus statements with matched AIBD case links; each statement retains its own review and eligibility status" }, "provenance": { "kind": "curatorial", "basis": "Linked to the current Fructus dataset by its validated case identifier. 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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. 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No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "PVNet" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Quartz Solar (Open Climate Fix machine-learning solar PV forecast, formerly PVNet)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Open Climate Fix" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Open Climate Fix's Quartz Solar goes live in NESO's control room and may avoid £30m a year in imbalance costs", "headline": "Quartz Solar goes live at NESO and may avoid £30m a year in costs" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "762f56e9d61746a84c50f2b1819e0cdc4e735befec7df93f8a91c6565106df5f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "On 6 November 2025 Open Climate Fix (OCF), a not-for-profit, announced that Quartz Solar was live in the National Energy System Operator's (NESO) control room. OCF's AI-powered solar PV forecasting tool combines machine learning, live satellite imagery and weather data. It is fully integrated from day-ahead scheduling to real-time control after starting as a Network Innovation Allowance project (The Energyst, 6 November 2025). The announcement says the tool helps avoid at least £30 million in imbalance costs a year and cuts around 300,000 tonnes of CO2. NESO's Carolina Tortora, Head of AI, Digitalisation and Innovation, is quoted saying it can accurately predict solar generation up to 36 hours in advance. She says it is revolutionizing solar forecasts in the control room. She does not state the £30m figure. pv magazine (7 November 2025) attributes the £30 million (US$39 million) per year saving to Quartz Solar itself. It reports OCF's claim of halving forecasting errors across about 300 grid supply points. OCF's website says the forecast is used daily by NESO. The savings are the maker's estimate, not an operator-reported or audited figure.", "replication": { "produced_by": [ "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" ], "must_hold": [ "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" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 37, "reviewed": 0, "record_mapped": 37, "not_assessed": 54, "complete": false, "by_state": { "value_present": 37, "not_reported": 0, "unresolved": 0, "not_assessed": 54, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }