Wind Energy: Forecasting Challenges for Its Operational Management

By Pierre Pinson. 2013

374 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Electrical and Electronic Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cde06b29db23e05e2a9723aaa47ac0416dd58bc7

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Citations in the corpus

Energy Forecasting: A Review and Outlook (2020, background): “The wind forecast uncertainty was also discussed in [13], as an effort to introduce wind forecasting to the statistics community.”
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms (2013, methodology): “Alternative methods for high-dimensional calibration assessment have thus been called for (Pinson, 2013; Schefzik et al., 2013).”
Potential applications of subseasonal‐to‐seasonal (S2S) predictions (2017, background): “S2S wind speed forecasts could help address the challenge of intermittency by enabling transmission service operators to plan operations further ahead and increase grid efficiency (Pinson, 2013), although at present only mean wind values (zonal and meridional) are available on the S2S timescale.”
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms (2015, neutral): “…wind forecasting is beyond the focus of this article; it is a subfield of its own; for details on wind forecasting, interested readers should refer to Gneiting et al. (2006), Monteiro et al. (2009), Hering and Genton (2010), Zhu and Genton (2012), Pinson (2013), and Zhu, Bowman, and Genton (2014).”
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond (2016, background): “, 2014), and one to three papers for each of the tracks, including three for load forecasting (Hong& Fan, in this issue; Hong, Wilson, & Xie, 2014; Hyndman & Fan, 2010); one for price forecasting (Weron, 2014); two for wind forecasting (Pinson, 2013; Zhang et al., 2014); and two for solar forecastin”