Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Gao, Xin; Li, Xiaobing; Zhao, Bing; Ji, Weijia; Xiang, Jing; He, Yang

doi:10.3390/en12061140

Cited by 101 publications

(52 citation statements)

References 36 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GRU is a variant of LSTM with a gated recurrent neural network structure, and comparing with LSTM, there are two gates (update gate and reset gate) in GRU and three gates (forgetting gate, input gate, and output gate) in LSTM; meanwhile, GRU has fewer training parameters than LSTM, so GRU converges quicker than LSTM during training [34].…”

Section: The Gru-cnn Hybrid Neuralmentioning

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

103

View full text Add to dashboard Cite

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.

show abstract

Section: The Gru-cnn Hybrid Neuralmentioning

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

103

View full text Add to dashboard Cite

show abstract

“…Pearson's correlation coefficient can deal with large datasets and has low complexity and strong generality, making it feasible for feature selection of the input dataset [63]. This method utilizes correlation indicators to determine the appropriate inputs and filters the inputs having scores greater than the threshold [64]. An autocorrelation function (ACF) and partial autocorrelation function (PACF) with twelve lags applied to the observed runoff series are shown in Fig.…”

Section: Input Selection and Model Developmentmentioning

confidence: 99%

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Sibtain¹,

Li²,

Azam³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDAN-GRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.

show abstract

“…GRU: GRU is another type of RNN that eliminates a separate storage unit [46]. There is no conclusive result in comparing performance between LSTM and GRU, and the performances of the LSTM and GRU depend on the task and dataset [47]. In this paper, a GRU network with 3 GRU layers and 1 dense layer is adopted for comparison [35].…”

Section: ) Comparison With State-of-the-art Dnnsmentioning

confidence: 99%

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Li³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the rise in the use of DC distributed energy resources and the growth of DC electricity load, the difficulty in improving DC power quality has become an important research direction. The research on DC power quality has an important impact on the development of DC power distribution theory and technology. In this paper, an evaluation method that combines empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1D-CNN) of DC power quality is proposed. As a method of data preprocessing, EMD decomposes the original electrical signal into several intrinsic mode functions (IMFs). Then, the 1-D CNN with a residual module is used to train the data obtained from EMD and conducts a comprehensive evaluation with different levels. In addition, the proposed network was compared with other state-of-the-art deep neural networks, and the experiment proved its effectiveness. Finally, an example analysis is carried out with the data provided by the Gree Photovoltaic Direct-driven Inverter Multi VRF (variable refrigerant flow) System to show the validity of the proposed method for evaluating DC power quality in a real case. INDEX TERMS DC power quality, photovoltaic direct-driven inverter multi VRF, empirical mode decomposition, one-dimensional convolutional neural network.

show abstract

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Cited by 101 publications

References 36 publications

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

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

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Contact Info

Product

Resources

About