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Studies

This section holds the findings of experiments we have run, written up so the conclusion survives the code that produced it. It complements the other sections: background describes the problem, techniques explains the methods, the roadmap says what we plan to build, and architecture documents what is already built.

A page lands here when a study answers a question that outlives its code. The code behind a page here is deliberately lighter than production code — no Dagster asset, no data contract, and no maintenance promise — and lives in studies/, which merges so that the measurement can be audited and re-run. What the project keeps is the answer, which is why each page states its numbers, the checks the result survived, and the limits on how far it generalises.

Every study here uses only Flexpectation's own data, and its purpose is to inform Flexpectation's choices. Each study scores its weather products or methods only at the generators in Flexpectation's trial area in Lincolnshire. No study here compares results across many regions or climates, so a result may not hold elsewhere.

  • Does a weather product's beam/diffuse split help a PV forecast? — on a 5 km satellite retrieval the published direct-beam field cuts photovoltaic (PV) power error by 1.8% beyond what a separation model recovers from the total irradiance; on a 31 km reanalysis no effect is detected at the setting named before the run; and choosing the better irradiance product matters about 34 times more than the split does. The page also measures the two irradiance products against each other, a gradient-boosted tree against a fitted five-parameter physical PV model, what calibrating that physical model with a tree recovers, and what the per-site error drift says about estimating a generator's effective capacity. It reads NGED's two records of active network management against each other and against the telemetry, and finds the setpoint history the one to build on.

Past weather

Three studies score how well each weather product describes weather that has already happened. The Past-weather overview summarises them, and the Methods page states the methods they share.

  • Which weather product best describes past sunshine? — a satellite retrieval describes past sunshine far better than any weather model tested; among the models, ICON-D2 is best as served but its advantage shrinks within hours of each run, and ICON-EU no longer beats UKV once UKV's hour is rebuilt from its own snapshots. The page says which product each offline consumer — capacity estimation, training history, historical features, and disaggregation — should read.
  • Which weather product best describes past wind? — at the three metered wind farms, the Met Office's UKV and the German weather service's ICON-D2 describe past hub-height wind best of the five products tested and both beat the ERA5 reanalysis, by more from April to September in intervals estimated separately for each half of the year; ICON-EU also beats ERA5; and ICON global has the largest error of the five, about half of its gap to ICON-EU a pair of steps in the wind Open-Meteo's archive serves for it at one generator.
  • Does blending weather products beat the best single weather product? — at the six solar farms and three wind farms, an XGBoost model given several weather products at once beats one given the best single product with its neighbouring hours: by 0.13 points of capacity for solar and 0.48 for wind. The gain comes from the other products' weather rather than from the extra columns, a blend of UKV and ICON-EU that a live service could read beats UKV alone, and an XGBoost blend beats a linear stack of single-product predictions. An XGBoost model given CAMS's split plus SARAH-3's global irradiance, the two satellite retrievals the past-solar study compared, beats CAMS's split with its neighbouring hours by 0.18 points and plain CAMS's split by 0.20 points, on a longer row set from January 2021.

Forecasts

Three studies score power forecasts driven by weather forecasts.

  • How accurate is a power forecast driven by ECMWF ENS at each horizon? — at the 6 solar farms and 3 wind farms, an XGBoost model given the ENS ensemble mean beats every forecast that reads no weather forecast to day 5 for solar and day 7 for wind; from day 7 for solar and day 10 for wind it no longer beats climatology, and by day 14 climatology is ahead, with the ensemble mean adding no statistically significant skill, in a post hoc check, over the same model given no weather at all. The ensemble mean beats the control member and beats training on every member, and rebuilding solar radiation through the clear-sky index beats the straight-line resample the live service uses today at days 0 to 3.
  • Do other weather forecasts beat ECMWF's ensemble at day-ahead lead? — at the 6 solar farms and 3 wind farms, and reading each product as the study does, ECMWF's ensemble mean beats UKV, ICON-EU, and GEFS at matched lead for solar; for wind it beats UKV and GEFS, and ICON-EU cannot be separated from it. A blend of ENS, ICON-EU, and IFS 0.25° lowers the wind error but shows no detectable solar gain at a lead a 09:00 UTC service could use.
  • How do Open-Meteo's ensemble-mean products compare for solar and wind power? — over 88 summer days at the 6 solar farms and 3 wind farms, ICON-D2-EPS's ensemble mean gave the lowest power error of the four Open-Meteo ensemble means, 7.4% of capacity for solar against 7.7% for ECMWF ENS's mean, and 5.75% for wind at 10 m, level with ECMWF ENS's mean. The comparison is descriptive and uses stitched series that carry no run time.