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Poster Communications Year : 2024

IEA Wind Task 51: Forecasting for the Weather Driven Energy System

Abstract

As the world’s fastest-growing technology, Artificial Intelligence (AI) is rapidly shaping industries such as Energy and Meteorology. To help address stakeholders’ concerns about the impacts of increasingly incorporating AI and Machine Learning into weather and power prediction models, the International Energy Agency’s (IEA) Wind Task 51 “Forecasting for the Weather Driven Energy System” invites you to a webinar in Deep Learning for Weather-Based Power Prediction. In this Webinar we will bring together the Energy Meteorology and Machine Learning / Deep Learning (ML/DL) communities to showcase the latest advancements in ML/DL for weather prediction. This event also provides an opportunity to discuss future directions for the integration of these new generation models in the Energy systems. Forecasts of wind and clouds are important inputs for the control and value of renewable power plants. The forecasts on a time resolution of minutes or seconds are typically data driven, looking at upstream plants, all-sky images of clouds or direct measurements of wind by lidars. Therefore, to facilitate the dissemination of information about minute scale forecasting products, skill, applications, issues, and best practices to members of the electric energy community, we invite you to a Minute Scale Forecasting workshop with the goal of gathering information about methods used to produce the forecasts, the current state-of-the-art skill and uncertainty in forecasting for variables on high temporal resolution, current and planned research activities intended to improve the current level of skill, types of public and private sector operational forecasting products, the range of minute scale applications in the energy community and the quantified or perceived value obtained from those applications, the sensitivity of user’s application performance to variations in forecast skill, and the unmet minute-scale forecasting-related needs or desires of the energy user community
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Dates and versions

hal-04533404 , version 1 (04-04-2024)

Identifiers

  • HAL Id : hal-04533404 , version 1

Cite

Gregor Giebel, Caroline Draxl, Helmut Frank, John Zack, Jethro Browell, et al.. IEA Wind Task 51: Forecasting for the Weather Driven Energy System. WindEurope Annual Event 2024, Mar 2024, Bilbao, Spain. 2024. ⟨hal-04533404⟩
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