# Wind Energy: Forecasting Challenges for Its Operational Management

arXiv: https://arxiv.org/abs/1312.6471 · HTML: https://mlsanity.com/paper/1312.6471/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2013.

## Built on by (most cited first, 11 of 11)

- [Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability](https://mlsanity.com/paper/2201.08671/) (2022)
- [Truthful Elicitation of Imprecise Forecasts](https://mlsanity.com/paper/2503.16395/) (2025)
- [DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting](https://mlsanity.com/paper/2410.20166/) (2024)
- [Time Series Foundation Models for Energy Load Forecasting on Consumer Hardware: A Multi-Dimensional Zero-Shot Benchmark](https://mlsanity.com/paper/2602.10848/) (2026)
- [Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification](https://mlsanity.com/paper/2211.16708/) (2022)
- [Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features](https://mlsanity.com/paper/2502.08376/) (2025)
- [Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters](https://mlsanity.com/paper/2601.19674/) (2026)
- [Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids](https://mlsanity.com/paper/2603.07899/) (2026)
- [Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting](https://mlsanity.com/paper/2512.12154/) (2025)
- [Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles](https://mlsanity.com/paper/2602.13010/) (2026)
- [Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity](https://mlsanity.com/paper/2609.26959/) (2026)

## Abstract (arXiv)

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.