Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

Wang, Shijun; Li, Chun; Liang, Kui; Cheng, Ziyun; Xue, Kaiping; Gao, Shuang

doi:10.3390/su14148705

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…The VMD is an adaptive data decomposition method that is suitable for handling non-stationary sequence data [26,27]. It decomposes a sequence into a specific number of subsequences, and each subsequence holds its own center frequency and finite bandwidth.…”

Section: Optimal Variational Mode Decompositionmentioning

confidence: 99%

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

Liu,

Xie,

Wang

et al. 2023

Sustainability

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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.

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Section: Optimal Variational Mode Decompositionmentioning

confidence: 99%

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

Liu,

Xie,

Wang

et al. 2023

Sustainability

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“…Most of these papers proposed models for locations in China, such as [21][22][23][24][25][26][27][28]. Six works proposed models for locations in the USA [29][30][31][32][33][34] and nine works for locations in Europe, such as Spain and Belgium [35][36][37][38][39][40][41][42][43]. Only one work was found that targeted the hot desert climate in which data from a wind farm in Pakistan was used [44].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

Alkhayat,

Hasan,

Mehmood

2023

Sustainability

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Advancements in technology, policies, and cost reductions have led to rapid growth in wind power (WP) production. One of the major challenges in wind energy production is the instability of WP generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning (DL) are delivering competitive performance versus traditional methods, like numerical weather prediction (NWP), statistical models and machine learning (ML) models. This is truer for short-term prediction. Since there is a relationship between methods, climates and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper presents a novel model that combines the variational mode decomposition (VMD) method with a long short-term memory (LSTM) model for next-hour wind speed (WS) prediction in a hot desert climate, such as the climate in Saudi Arabia. The proposed model performance is compared to two other hybrid models, six DL models and four ML models using different feature sets. Also, the proposed model is tested on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill (FS) between 61% and 74% based on mean absolute error (MAE), 64% and 72% based on root mean square error (RMSE), and 59% and 68% based on mean absolute percentage error (MAPE) for locations in Saudi Arabia.

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A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects

Qiu,

Shi,

Wang

et al. 2024

Renewable Energy

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Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

Cited by 7 publications

References 41 publications

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

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

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects

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