Electricity, Volume 6, Issue 3
2025 September - 19 articles
Cover Story: In this study, we introduce a hybrid SDE-Neural Network model for interpretable wind power prediction, leveraging SCADA data from a Senvion MM92 turbine. By integrating Stochastic Differential Equations with Neural Networks, the approach effectively captures complex, non-Gaussian fluctuations in wind energy. It demonstrates comparable or superior performance to traditional RNN, LSTM, and CNN-LSTM models, offering a valuable balance between predictive accuracy and physical interpretability for grid management. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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