Short-term Wind Power Prediction Method Based on Wavelet Packet Decomposition and Improved GRU

Zu, Xiangrong; Song, Ruya

doi:10.1088/1742-6596/1087/2/022034

Cited by 12 publications

(1 citation statement)

References 7 publications

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“…Zu and Song [41] introduced the short term wind power forecasting model in which a wavelet packet decomposition is applied for decomposing the time series wind power sequences into a number of sub sequences. An improved GRU method with Scaled Exponential Linear Unit (SELU) activation function is utilized for predicting the wind power for each subsequence.…”

Section: Development Of Deep Learning Based Rnn In Wind Energy Forecastingmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Paramasivan¹

2021

RIA

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In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.

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Section: Development Of Deep Learning Based Rnn In Wind Energy Forecastingmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Paramasivan¹

2021

RIA

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An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

Chen,

Hu,

Liu

et al. 2024

Energy Science & Engineering

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Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

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Comparing the Renewable Energy Technologies via Forecasting Approaches

Gökgöz

Filiz

2021

Applied Operations Research and Financial Modelling in Energy

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Short-term Wind Power Prediction Method Based on Wavelet Packet Decomposition and Improved GRU

Cited by 12 publications

References 7 publications

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

Comparing the Renewable Energy Technologies via Forecasting Approaches

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