Bias correction of wind power forecasts with SCADA data and continuous learning

S. Jonas*, K. Winter, B. Brodbeck, A. Meyer

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, electricity trading, or maintenance scheduling. In this work, we present, evaluate, and compare four machine learning-based wind power forecasting models. Our models correct and improve 48-hour forecasts extracted from a numerical weather prediction (NWP) model. The models are evaluated on datasets from a wind park comprising 65 wind turbines. The best improvement in forecasting error and mean bias was achieved by a convolutional neural network, reducing the average NRMSE down to 22%, coupled with a significant reduction in mean bias, compared to a NRMSE of 35% from the strongly biased baseline model using uncorrected NWP forecasts. Our findings further indicate that changes to neural network architectures play a minor role in affecting the forecasting performance, and that future research should rather investigate changes in the model pipeline. Moreover, we introduce a continuous learning strategy, which is shown to achieve the highest forecasting performance improvements when new data is made available.

Original languageEnglish
Article number092061
Number of pages10
JournalJournal of Physics: Conference Series
Volume2767
Issue number9
DOIs
Publication statusPublished - 2024
Event2024 Science of Making Torque from Wind, TORQUE 2024 - Florence, Italy
Duration: 29 May 202431 May 2024

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