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 provide an opportunity for delegates 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 the academic community. We look forward to seeing you there!
PO191: Short-term prediction of wind power production using different machine learning models
Minh-Thang DO, Head of Energy Division, Meteodyn
Abstract
Wind power is a sustainable, renewable energy source with a small impact on the environment. However, fluctuations of wind farm power output resulting from intermittent winds in the atmosphere remains a challenge to the integration of wind energy into power systems. As the share of wind energy grows globally, the question of reliability of wind power generation needs to be better addressed. Power forecasting based on machine learning methods is a technique to predict power output of a wind farm related to different horizons from several days to several hours ahead. The forecast is useful for wind farm operators to estimate energy yield and detect underperforming of wind turbines. In this study, we examine the performance of machine learning models based on neural networks on predicting the short-term wind power output of a wind farm. We define the forecasting task as a single-step, two-hour ahead forecast using available input data during the time lag.
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