Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
On 9 April at 17:15, we’ll also hold the main poster session and distinguish the 7 best posters of this year’s edition with our traditional Poster Awards Ceremony. Join us at the poster area to cheer and meet the laureates, and enjoy some drinks with all poster presenters!
We look forward to seeing you there!
PO107: Leveraging seasonal forecasts to anticipate and mitigate wind drought impacts.
Yazmina Zurita Martel, R&D Data Scientist, Nebbo Weather Solutions S.L.
Abstract
We present a case study demonstrating wind droughts can be predicted months before they occur using seasonal power forecasts, allowing wind energy users to plan better and offering the potential for significant economic savings. Electricity systems are increasingly weather-dependent, raising the need for high-quality meteorological forecasts. Long-range power forecasts specifically enable wind park operators and owners, energy traders and grid operators to prepare for impacts on energy returns and grid stability, enhancing wind energy integration. Our analysis focuses on the wind drought that happened during the first semester of 2024 in Costa Rica, which reduced substantially the power generation of their wind farms. The methodology consists in transforming seasonal model outputs into actionable information. We use machine learning techniques for model calibration, followed by the combination of models and the construction of informative prediction intervals. We found that the resulting seasonal power forecast predicted the event with high confidence when comparing it against reference power production data. Our findings demonstrate that stakeholders could have used these predictions to anticipate a decrease in wind power production months in advance, highlighting the potential of integrating seasonal forecasts into energy planning processes to better manage climate variability risks and optimize decision-making for renewable energy operations.
No recording available for this poster.
