Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM

Banteng Liu, Yangqing Xie, Ke Wang (), Lizhe Yu, Ying Zhou and Xiaowen Lv
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Banteng Liu: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Yangqing Xie: School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
Ke Wang: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Lizhe Yu: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Ying Zhou: College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Xiaowen Lv: Zhejiang Economic Information Center, Hangzhou 310006, China

Sustainability, 2023, vol. 15, issue 15, 1-18

Abstract: Accurate and reliable wind direction prediction is important not only for enhancing the efficiency of wind power conversion and ensuring safe operation, but also for promoting sustainable development. Wind direction forecasting is a challenging task due to the random, intermittent and unstable nature of wind direction. This paper proposes a short-term wind direction prediction model based on quadratic decomposition and long short-term memory (LSTM) to improve the accuracy and efficiency of wind direction prediction. Firstly, the model adopts a seasonal-trend decomposition procedure based on the loess (STL) method to divide the wind direction series into three subsequences: trend, seasonality and the remainder, which reduces the impact of the original sequence’s complexity and non-stationarity on the prediction performance. Then, the remainder subsequence is decomposed by the optimal variational mode decomposition (OVMD) method to further explore the potential characteristics of the wind direction sequence. Next, all the subsequences are separately input into the LSTM model, and the prediction results of each subsequence from the model are superimposed to obtain the predicted value. The practical wind direction data from a wind farm were used to evaluate the model. The experimental results indicate that the proposed model has superior performance in the accuracy and stability of wind direction prediction, which also provides support for the efficient operation of wind turbines. By developing advanced wind prediction technologies and methods, we can not only enhance the efficiency of wind power conversion, but also ensure a sustainable and reliable supply of renewable energy.

Keywords: wind direction prediction; STL; OVMD; LSTM; quadratic decomposition integration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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