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November 2013 Wind Energy: Forecasting Challenges for Its Operational Management
Pierre Pinson
Statist. Sci. 28(4): 564-585 (November 2013). DOI: 10.1214/13-STS445


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.


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Pierre Pinson. "Wind Energy: Forecasting Challenges for Its Operational Management." Statist. Sci. 28 (4) 564 - 585, November 2013.


Published: November 2013
First available in Project Euclid: 3 December 2013

zbMATH: 1331.91140
MathSciNet: MR3161588
Digital Object Identifier: 10.1214/13-STS445

Keywords: decision-making , electricity markets , forecast verification , Gaussian copula , linear and nonlinear regression , parametric and nonparametric predictive densities , power systems operations , Quantile regression , renewable energy , space–time trajectories , stochastic optimization

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.28 • No. 4 • November 2013
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