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\begin{document}

\title{Short-term Wind Power Prediction based on Combined LSTM}



\author[1]{Zhao Yuyang Zhao}%
\author[1]{Li Lincong Li}%
\author[1]{Guo Yingjun Guo}%
\author[1]{Shi Boming Shi}%
\author[1]{Sun Hexu Sun}%
\affil[1]{Hebei University of Science and Technology}%


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  \date{May 17, 2024}


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\begin{abstract}
Wind power is an exceptionally clean source of energy, its rational
utilization can fundamentally alleviate the energy, environment, and
development problems, especially under the goals of ``carbon peak'' and
``carbon neutrality''. A combined short-term wind power prediction based
on LSTM artificial neural network has been studied aiming at the
nonlinearity and volatility of wind energy. Due to the large amount of
historical data required to predict the wind power precisely, the
ambient temperature and wind speed, direction, and power are selected as
model input. The CEEMDAN has been introduced as data preprocessing to
decomposes wind power data and reduce the noise. And the PSO is
conducted to optimize the LSTM network parameters. The combined
prediction model with high accuracy for different sampling intervals has
been verified by the wind farm data of Chongli Demonstration Project in
Hebei Province. The results illustrate that the algorithm can
effectively overcome the abnormal data influence and wind power
volatility, thereby provide a theoretical reference for precise
short-term wind power prediction.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Short-term Wind Power Prediction based on Combined LSTM.docx`  available at \url{https://authorea.com/users/625011/articles/647127-short-term-wind-power-prediction-based-on-combined-lstm}

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