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.Comment: Published in at http://dx.doi.org/10.1214/13-STS445 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org