Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting

Lin, Wen-Hui; Wang, Ping; Chao, Kuo-Ming; Lin, Hsiao-Chung; Yang, Zhe; Lai, Yu-Huang

doi:10.3390/app112110335

Cited by 36 publications

(18 citation statements)

References 19 publications

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“…Parallel operation with efficient modeling of the dependency relationship between multiple sequence features is shown in Figure 1c. Long-term prediction of wind power with a mean absolute percentage error of 10% was carried out in [9] by using the temporal convolutional network (TCN). Ref.…”

Section: Deep Learning-based Wind Power Forecastingmentioning

confidence: 99%

“…Historically, there have been different wind power forecasting (WPF) methods, which can be divided into four categories: physical, statistical, hybrid, and deep-learning methods. A summary report of these four categories of methods in terms of their features and limitations in application is given in [9]. The physical method is based on a mesoscale weather model or a numerical weather prediction system (NWP).…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid method integrates the physical and statistical models to improve forecast performance by preserving the advantages of each approach [16][17][18], but the hybrid models may not have the capability to achieve stable prediction, as their complex learning architecture may cause low efficiency in training and even underfitting [9].…”

Section: Introductionmentioning

confidence: 99%

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SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle–and–fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model’s sequential modeling capability. Finally, the developed shuffle–and–fusion interaction network model is tested using real-world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software.

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Section: Deep Learning-based Wind Power Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Zhang¹,

Cheng

Zhang

2022

Applied Sciences

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“…Recently, numerous machine learning (ML) algorithms [3][4][5] have been proposed to help engineers distinguish the correlations between climate features and wind power outputs by accumulating historical meteorological information and wind power generation data. For example, artificial neural networks (ANNs), genetic algorithms, fuzzy regression, cluster analysis (K-means), and support vector machines have been introduced to solve the problems of wind-power forecasting in climate change.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

et al. 2022

Self Cite

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Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications. However, improving the training process using proper parameter hyperisations and techniques, such as regularisation and Adam-based optimisation, remains a challenge in the design of DLNs for processing time-series data. Moreover, the most appropriate parameter for the DLN model is to solve the wind-power forecasting problem by considering the excess training algorithms, such as the optimiser, activation function, batch size, and dropout. Reinforcement learning (RN) schemes constitute a smart approach to explore the proper initial parameters for the developed DLN model, considering a balance between exploration and exploitation processes. Therefore, the present study focuses on determining the proper hyperparameters for DLN models using a Q-learning scheme for four developed models. To verify the effectiveness of the developed temporal convolution network (TCN) models, experiments with five different sets of initial parameters for the TCN model were determined by the output results of Q-learning computation. The experimental results showed that the TCN accuracy for 168 h wind power prediction reached a mean absolute percentage error of 1.41%. In evaluating the effectiveness of selection of hyperparameters for the proposed model, the performance of four DLN-based prediction models for power forecasting—TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models—were compared. The overall detection accuracy of the TCN model exhibited higher prediction accuracy compared to canonical recurrent networks (i.e., the GRU, LSTM, and RNN models).

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“…In addition to the approaches above, the modal parameters of structures can be identified in another way by a neural network. The neural network algorithm can approach any function in theory, has a strong nonlinear mapping ability, and the adaptability of the network is strong [31].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

et al. 2022

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To adaptively identify the modal parameters for time-invariant structures excited by non-white noise, this paper proposes a new operational modal analysis (OMA) method using hybrid neural networks. In this work, taking the acceleration response directly as the input data of the networks not only simplifies the data processing, but also retains all the characteristics of the data. The data processed by the output function is the output data of the network, and its peak corresponds to the modal frequency. The proposed output function greatly reduces the computational cost. In addition, a small sample dataset ensures that the hybrid neural networks identify the modal parameters with the highest accuracy in the shortest possible time. Interestingly, the hybrid neural networks combine the advantages of the convolutional neural network (CNN) and gate recurrent unit (GRU). To illustrate the advantages of the proposed method, the cantilever beam and the rudder surface excited by white and non-white noise are taken as examples for experimental verification. The results reveal that the proposed method has a strong anti-noise ability and high recognition accuracy, and is not limited by ambient excitation type.

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Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting

Cited by 36 publications

References 19 publications

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

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