Ultra-short-term wind power prediction based on double decomposition and LSSVM

Qin, Bin; Huang, Xun; Wang, Xin; Guo, Lingzhong

doi:10.1177/01423312231153258

“…By using the VMD technique to reduce the volatility of the original sequence, the model effectively improves the prediction accuracy. Bin Qin et al [10] combined WD, VMD, and Least Squares Support Vector Machine (LSSVM) prediction models, and proposed the WD-VMD-LSSVM model. Through comparative experiments, they demonstrated that WD and VMD decomposition techniques significantly improve prediction accuracy.…”

Section: Vmd Technique Can Decompose Non-stationary Signals Into Diff...mentioning

confidence: 99%

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Shu,

Qin,

Wang

2024

JPEE

Self Cite

0

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Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model; a BPNN model was utilized to predict highfrequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

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“…The forecasting accuracy of short-term WPF is enhanced by correcting NWP data. Various data preprocessing methods for a WPG system model have been investigated, such as singular value decomposition, from the system perspective [147,148]. Real-time WTP measurements are added to the reconstructed state space during the forecasting process, making the forecast more flexible.…”

Section: Future Studies and Developmentmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

¹

,

Hong

²

,

Tu

³

et al. 2023

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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“…The forecasting accuracy for short-term WPF is enhanced by correcting NWP data. Various data preprocessing methods for a WPG system model have been investigated, such as singular value decomposition, from the system perspective [87][88].…”

Section: Future Studies and Developmentmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

6

0

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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Ultra-short-term wind power prediction based on double decomposition and LSSVM

Cited by 9 publications

References 31 publications

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

A Review of Modern Wind Power Generation Forecasting Technologies

A Review of Modern Wind Power Generation Forecasting Technologies

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