A review of wind speed and wind power forecasting with deep neural networks

Wang, Yun; Zou, Runmin; Liu, Fang; Zhang, Lingjun; Liu, Qianyi

doi:10.1016/j.apenergy.2021.117766

Cited by 348 publications

(61 citation statements)

References 197 publications

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“…In the last five years, the following deep learning models have been developed and used: convolutional neural network [29]; recurrent neural network; long short-term memory [30]; deep brief network [31]; stacked auto-encoder; deep neural network [32]; gated recurrent network [33]; and deep hybrid models. In previously research, deep learning-based models performed better than statistical models and physical models [34].…”

Section: Forecasting Of Wind and Wave Energymentioning

confidence: 99%

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Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

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Section: Forecasting Of Wind and Wave Energymentioning

confidence: 99%

“…The other group is outlier detection. The wavelet-based method was used to decompose the raw data into different series to process rather than keep them at the same level [32]. Ensemble EMD [34,52], EMD [65] and mode decomposition (VMD) [77] are decomposition-based methods.…”

Section: Applicationsmentioning

confidence: 99%

Review on Deep Learning Research and Applications in Wind and Wave Energy

2022

Energies

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“…A traditional recurrent neural network cannot effectively learn long-term dependence [48]. LSTM solves this shortcoming through the gating mechanism, while this structure increases the parameters of the network.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

et al. 2022

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The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

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“…Lin et al [34] used a deep learning strategy to forecast wind power on a Supervisory Control and Data Acquisition (SCADA) dataset to maintain maximum forecasting accuracy with lower computational cost. Wang et al [35] further proposed a deep learning neural network for a high-frequency SCADA database for predicting wind power from offshore wind farms. The deep learning model was fine-tuned by removing outlier values without any density measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

et al. 2022

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Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.

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A review of wind speed and wind power forecasting with deep neural networks

Cited by 348 publications

References 197 publications

Review on Deep Learning Research and Applications in Wind and Wave Energy

Review on Deep Learning Research and Applications in Wind and Wave Energy

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

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