Considering and explicitly modeling forecast uncertainty is at the heart of many scientific and applied enterprises.
The ever-increasing accuracy of weather forecasts, for instance, has been driven by the development of ensemble forecasts: Here, a large number of forecasts are generated either by generating forecasts from different models or by repeatedly perturbing the initial conditions of a single forecast model. The approach provides robust estimates of forecast uncertainty, which can support human judgment and decision-making.
Although weather forecasts and their uncertainty are also crucial for the weather-to-power conversion of renewable energy system forecasting, so far the industry has been reluctant to adopt ensemble methods and other new technologies that can help managing highly variable and uncertain power feed-ins under varying weather conditions.
Energy traders, for instance, can use uncertainty forecasting to optimize the amount of power generation that they bid into the market. And transmission system operators can define the reserve required to account for the uncertainty in generation, and are prepared for grid congestion much earlier than with the currently used deterministic forecast.
By not adopting state-of-the art uncertainty forecasts, the renewable energy sector thus forgoes critical potentials to reduce its vulnerabilities, build more robust prediction models, and improve judgment and decision-making of the involved parties.
Two intertwined challenges are responsible for the limited adoption of uncertainty forecasts in the renewable energy sector:
1. The technical factor: The current energy management systems simply cannot operate with probabilistic forecasts.
2. The human factor: New methods and approaches are bound to fail if end-users and agents do not know how to harness new forecast methods and systematically integrate uncertainty in their decision-making processes.