Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting

Wang, Yusen; Liao, Wenlong; Chang, Yuqing

doi:10.3390/en11082163

Cited by 193 publications

(104 citation statements)

References 26 publications

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“…Unlike feedforward neural networks, GRU networks can use their internal memory to process a time series of inputs. This makes them applicable to time series prediction [25]. Therefore, a GRU network was selected as one of the submodels in this paper.…”

Section: Gru Networkmentioning

confidence: 99%

“…The Linear ensemble refers to training MLP, GRU, and XGBoost, respectively, and calculating the mean of the test set. The framework of the ensemble can be seen in reference [25]. Obviously, the Waterfall ensemble and Linear ensemble prediction errors are smaller than those of GRU and XGBoost but larger than that of MLP, which indicates that the traditional ensemble techniques need to be improved.…”

Section: Effect Of Model Order On Accuracymentioning

confidence: 99%

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Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

2019

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Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k-fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k-fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.

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Section: Gru Networkmentioning

confidence: 99%

Section: Effect Of Model Order On Accuracymentioning

confidence: 99%

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

2019

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“…In [27], a novel system called multi-GRU (gated recurrent unit) prediction system was developed based on GRU models for electricity generation's planning and operation. And Wang proposed a novel approach to forecast short-term photovoltaic power based on GRU networks [28]. However, there is not only sequence data in the power system, but also other kinds of high-dimensional data, such as spatiotemporal matrix and image information in the power system.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Chun

Hao

et al. 2020

Mathematical Problems in Engineering

100

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Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

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“…With the development of smart grids, the large-scale integration of advanced power electronic devices, renewable energies, and electric vehicles have brought new challenges to the operation of distribution networks. For example, the uncertainty of photovoltaic output power easily causes a voltage drop in the distribution network [1]. Similarly, the fluctuation and intermittence of wind farms' produced power may cause the voltages of the distribution network to exceed the limits, since the wind power lies on the wind speed that varies from time to time [2].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

Zhu

Gong

et al. 2019

Energies

Self Cite

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The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability.

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Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting

Cited by 193 publications

References 26 publications

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

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