An ensemble is a set of forecasts rather than a single forecast trajectory. Its members may represent different starting conditions, different model assumptions or different weather models. Renewable energy forecasting uses ensembles to explore several plausible outcomes for wind, sunlight and power production. A multi-model average and a calibrated probability forecast are related ideas, but they are not interchangeable. Averaging several predictions can improve a point forecast without producing reliable probabilities.
Why a single number is incomplete
A trader may need a central expectation of generation, while an operator also needs to know how wide the plausible range is. Weather uncertainty changes with lead time and meteorological conditions. A stable high-pressure situation may be easier to predict than a rapidly moving front. For solar generation, cloud timing can matter more than a small error in daily radiation totals. For wind generation, a modest wind-speed error can produce a substantial power difference in the steep part of the turbine power curve.
From weather members to power members
Each weather member can be converted into a power scenario using consistent plant characteristics. This transformation is nonlinear. Converting the average wind speed into power is therefore not generally the same as averaging power produced by individual wind-speed members. Operational limits, clipping, outages and curtailment must also be treated consistently. The scenario set should describe the quantity the user trades or operates, such as available generation or actual expected feed-in.
Read intervals and quantiles correctly
A prediction interval describes a range with a stated intended coverage. A quantile is a threshold associated with a probability level. These statements concern a defined variable, location, valid period and forecast issue time. They do not promise that every observation will fall inside an interval. A forecast labelled as an 80% interval should be checked over many comparable cases: does the observed value fall inside it approximately as often as intended? Coverage alone is insufficient because an excessively wide interval can achieve coverage while offering little decision value.
Calibration and sharpness both matter
Calibration checks whether stated probabilities agree with observed frequencies. Sharpness describes how concentrated the predictive distribution is. The aim is useful concentration subject to adequate calibration. Evaluate different lead times and operating conditions separately. A forecast can appear well calibrated in aggregate while failing during high-output periods or particular seasons. Scoring rules and reliability diagnostics help assess distributions; point-error metrics alone cannot establish probability quality.
Portfolio uncertainty is not the sum of site ranges
Weather errors can affect many assets together. Adding independent site uncertainties can understate the risk of a widespread weather event. Conversely, geographical diversity may reduce some local errors. Portfolio scenarios need to preserve relevant spatial and temporal relationships. Traders should also distinguish power uncertainty from price uncertainty: generation scenarios do not establish the price paid for an imbalance or the availability of a trading opportunity.
Use uncertainty in a concrete workflow
Start with a decision, such as defining a reserve margin or reviewing a trading position. Agree which quantiles, intervals or scenarios the workflow needs, and whether the forecast provider supplies them. Archive each issued forecast before the outcome is known. Compare decisions using consistent historical information and realistic operational constraints. The original temperature-ensembling article introduced combining weather models; the broader practical lesson is to verify the uncertainty information itself, rather than assuming a larger ensemble is automatically better.
A practical first review
Ask for one archived issue containing the central forecast and its uncertainty information. Check that every scenario uses the same assets and valid intervals, and that the observed series matches that definition. Review how intervals were calculated and how calibration was assessed before connecting probabilities to automated decisions.