By the end of 2023, wind supplied more than 55% of Denmark's annual electricity demand. In Germany, wind and solar together exceeded 60% of net generation. For transmission and distribution operators in these markets, forecast accuracy is a cost driver, not merely a technical preference. It affects reserve capacity procurement, emergency balancing frequency, and exposure during periods of high but uncertain generation.
The structural issue is clear. Variable renewables make grid operations dependent on forecasts at a scale absent when thermal generation dominated. Less often recognised in procurement is that forecast error is not fixed by region or weather system. It differs materially between providers, with measurable cost consequences.
The Forecast Procurement Gap in Northern Europe
Grid operators commonly buy weather services through long-term contracts with established suppliers. Tenders, when used, tend to assess specifications, coverage claims, and price. They seldom test provider skill scores against the operator's own actuals and control-area data.
This is understandable. Until recently, outcome evaluation demanded data engineering capacity that most procurement teams lacked. A validation pipeline had to retain forecasts at issue time, align them with metered actuals, calculate skill metrics, and run continuously rather than only during a tender. Few organisations had the time or internal resource to build it.
Providers are therefore often renewed for continuity rather than proven performance. Changing supplier brings operational risk through new formats and integrations, yet that risk is rarely compared with the cost of retaining a weak provider. Incumbency wins, whatever the measured skill.
What Reserve Costs Show About Forecast Error
Transmission operators activate reserves when realised generation departs materially from day-ahead and intraday schedules. Activation costs arise from the reserve premium and from unexpected calls during stressed, high-price periods. ENTSO-E balancing data indicates that, in high-VRE years, reserve activation is a material part of total system costs in Northern European markets.
Forecast error is not behind every activation. Outages, demand spikes, and interconnector failures matter too. Still, many events coincide with misses in wind and solar forecasts. Limiting large errors in the dominant variable, often offshore wind along Danish and Dutch corridors, lowers the chance of avoidable reserve calls.
The chain is straightforward: stronger skill 6h and 12h ahead improves dispatch scheduling; better schedules reduce unplanned deviations; fewer deviations lower reserve activation costs. The relationship is not deterministic, but it is consistent enough to inform procurement.
Outcome-Based Benchmarking in Practice
The procurement gap can be addressed with three inputs: each provider's forecast files saved at original issuance, not retrospective hindcasts; metered actuals for relevant control-area variables; and a standard pipeline calculating MAE, RMSE, and skill scores against a baseline.
For a distribution operator overseeing 2.5 GW of offshore wind in the southern North Sea, evaluation should use wind speed at 10-minute intervals and turbine-cluster locations across at least 12 months. The period must contain a winter and summer shoulder season, exposing seasonal bias, which differs structurally from random error.
The output is a table of skill scores by horizon, 6h, 12h, 24h, and 48h, plus season and meteorological regime. A provider with strong annual results but cold-season bias presents a different risk from one performing consistently year-round. The table shows both.
A Representative Case
An early-access pilot participant, a mid-size grid services operator in northern continental Europe, manages connections to several offshore wind clusters. Across an 18-month validation exercise on the Climingo platform, they compared three forecast providers with metered actuals at 6h and 24h for wind speed and direction at operational sites.
The providers showed a meaningful spread in 24h skill scores. The lowest average RMSE over the period had a systematic underforecast bias during frontal passages, coinciding with the operator's costliest reserve days. The provider ranked third by average RMSE calibrated better in those passages, making it preferable for the operator's cost structure despite the lower aggregate score.
Aggregate skill scores are therefore a starting point, not the decision. Measure performance in the conditions that drive operating costs, rather than weighting every condition equally.
Benchmark Data to Action
A skill table does not choose a provider by itself. Procurement must weigh the performance gap against switching costs, including data-format integration, contractual flexibility, and operational change management. Better skill over 18 months may warrant the move; a marginal gap may not.
Benchmarking replaces vendor assurance with outcome evidence and creates an audit trail. Grid operators in several Northern European jurisdictions increasingly need to explain critical operational-data procurement to regulators and internal governance boards. A comparison of skill scores against verified actuals supports that record better than a vendor capability statement.
What Benchmarking Cannot Deliver
Outcome-based benchmarking lowers the chance of selecting a systematically weak provider. It does not remove uncertainty and should not be presented as doing so. Even the top provider in a benchmark period will make material errors. Frontal transitions, summer convection, and coastal sea-breeze dynamics remain genuine meteorological challenges that no current NWP or machine learning system fully resolves.
Rigorous procurement aims to minimise avoidable error, including error from weak provider methods or insufficient regional model resolution, not to reach zero error. This distinction sets realistic expectations internally and with regulators. We state it clearly when presenting benchmark results to platform users.