Wind Energy: Forecasting Challenges for Its Operational Management

TLDR

Wind energy is increasingly integral to electricity supply worldwide, with widespread adoption in Europe, the US, and emerging markets, yet its integration faces technological, environmental, political, and variability‑prediction challenges. The study aims to enable mathematicians and statisticians to develop probabilistic wind‑power forecasts that address operational decision problems and to tackle future challenges in forecast improvement, verification, and user value. The authors model wind power as a stochastic process, describe operational decision problems for market participants and system operators, and review forecasting approaches—including probabilistic, single‑valued, marginal density, and space–time trajectory methods.

Abstract

Renewable energy sources, especially wind energy, are to play a larger role in providing electricity to industrial and domestic consumers. This is already the case today for a number of European countries, closely followed by the US and high growth countries, for example, Brazil, India and China. There exist a number of technological, environmental and political challenges linked to supplementing existing electricity generation capacities with wind energy. Here, mathematicians and statisticians could make a substantial contribution at the interface of meteorology and decision-making, in connection with the generation of forecasts tailored to the various operational decision problems involved. Indeed, while wind energy may be seen as an environmentally friendly source of energy, full benefits from its usage can only be obtained if one is able to accommodate its variability and limited predictability. Based on a short presentation of its physical basics, the importance of considering wind power generation as a stochastic process is motivated. After describing representative operational decision-making problems for both market participants and system operators, it is underlined that forecasts should be issued in a probabilistic framework. Even though, eventually, the forecaster may only communicate single-valued predictions. The existing approaches to wind power forecasting are subsequently described, with focus on single-valued predictions, predictive marginal densities and space–time trajectories. Upcoming challenges related to generating improved and new types of forecasts, as well as their verification and value to forecast users, are finally discussed.