Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction

Wu, Xinhua; Chen, Nan; Du, Qianyun; Mao, Shuangshuang; Ju, Xiaoming

doi:10.1088/1742-6596/2427/1/012028

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…With its unique architecture, the GRU excels at capturing long-term dependencies and patterns within time series. In reference [28], the GRU model was employed to forecast wind power sub-sequences after applying decomposition techniques. In reference [29], the authors demonstrated that the GRU exhibits superior predictive accuracy and offers faster training and lower sensitivity to noise.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

Huang,

Zhang,

Liu

et al. 2023

Sustainability

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Enhancing the accuracy of short-term wind power forecasting can be effectively achieved by considering the spatial–temporal correlation among neighboring wind turbines. In this study, we propose a short-term wind power forecasting model based on 3D CNN-GRU. First, the wind power data and meteorological data of 24 surrounding turbines around the target turbine are reconstructed into a three-dimensional matrix and inputted into the 3D CNN and GRU encoders to extract their spatial–temporal features. Then, the power predictions for different forecasting horizons are outputted through the GRU decoder and fully connected layers. Finally, experimental results on the SDWPT datasets show that our proposed model significantly improves the prediction accuracy compared to BPNN, GRU, and 1D CNN-GRU models. The results show that the 3D CNN-GRU model performs optimally. For a forecasting horizon of 10 min, the average reductions in RMSE and MAE on the validation set are about 10% and 11%, respectively, with an average improvement of about 1% in R. For a forecasting horizon of 120 min, the average reductions in RMSE and MAE on the validation set are about 6% and 8%, respectively, with an average improvement of about 14% in R.

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Section: Introductionmentioning

confidence: 99%

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

Huang,

Zhang,

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

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A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

Garg

Krishnamurthi

2023

Artif Intell Rev

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Deep Learning Wind Power Prediction Model Based on Attention Mechanism-Based Convolutional Neural Network and Gated Recurrent Unit Neural Network

Hou,

Bai,

Ding

et al. 2024

J CIRCUIT SYST COMP

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Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN–GRU–Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model’s performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil’s [Formula: see text], TIC and SPL) and the Diebold–Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model’s superior accuracy and efficiency in predicting wind power.

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Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction

Cited by 3 publications

References 3 publications

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

A survey of long short term memory and its associated models in sustainable wind energy predictive analytics

Deep Learning Wind Power Prediction Model Based on Attention Mechanism-Based Convolutional Neural Network and Gated Recurrent Unit Neural Network

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