Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network

Li, Chao; Guo, Quanjie; Shao, Lei; Li, Ji; Wu, Han

doi:10.3390/electronics11223834

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Targeting the time-series-prediction task, compared with using RNN and its variants alone, some research work has combined the RNN model prediction output with the attention mechanism in deep learning. These research findings indicate that this approach of rapidly selecting high-value information from a large amount of data can significantly enhance the accuracy of time-series prediction [30][31][32]. However, this method is rarely applied in the field of water-level prediction.…”

Section: Discussionmentioning

confidence: 99%

“…Since the rapid development of deep learning, attention mechanism has been widely used in natural language processing, statistical learning, image detection, speech recognition, and other fields as well as in the processing of regression problems [38]. For the time-series prediction, some research combines the RNN model prediction output with the attention mechanism, and its research results indicate that the addition of the attention mechanism can significantly improve prediction accuracy [30][31][32]. These researchers typically use two dimensions to explain the improvement in prediction accuracy: attention mechanisms based on different times and attention mechanisms based on different characteristics.…”

Section: Spatial-reduction Attentionmentioning

confidence: 99%

“…A point worth emphasizing is that in terms of time-series prediction, compared with using RNN and its variants alone, some research work has combined the RNN model prediction output with the attention mechanism in deep learning. These results show that the introduction of attention mechanism can improve the accuracy of timing prediction to a large extent [30][31][32]. The attention mechanism is a method for rapidly selecting high-value information from huge amounts of information.…”

Section: Introductionmentioning

confidence: 97%

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A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

Bao

et al. 2023

Water

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According to the statistics of ship traffic accidents on inland waterways, potential safety hazards such as stranding, hitting rocks, and suspending navigation are on the increase because of the sudden rise and fall of the water level, which may result in fatalities, environmental devastation, and massive economic losses. In view of this situation, the purpose of this paper is to propose a high-accuracy water-level-prediction model based on the combination of the spatial-reduction attention and bidirectional gate recurrent unit (SRA-BiGRU), which provides support for ensuring the safe navigation of ships, guiding the reasonable stowage of ships, and flood prevention. The first contribution of this model is that it makes use of its strong fitting ability to capture nonlinear characteristics, and it fully considers the time series of water-level data. Secondly, the bidirectional recurrent neural network structure makes full use of past and future water-level information in the mapping process between input and output sequences. Thirdly, and most importantly, the introduction of spatial-reduction attention on the basis of BiGRU can not only automatically capture the correlations between the hidden vectors generated by BiGRU to address the issue of precision degradation due to the extended time span in water-level-forecasting tasks but can also make full use of the spatial information between water-level stations by emphasizing the influence of significant features on the prediction results. It is noteworthy that comparative experiments gradually prove the superiority of GRU, bidirectional recurrent neural network structure, and spatial-reduction attention, demonstrating that SRA-BiGRU is a water-level-prediction model with high availability, high accuracy, and high robustness.

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Section: Discussionmentioning

confidence: 99%

Section: Spatial-reduction Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

Bao

et al. 2023

Water

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“…The GRU is a variant of the RNN that uses gating mechanisms to better capture and remember long-term dependencies in sequence data [42]. There are some similarities between the GRU and the LSTM in that both have gating mechanisms that control the flow of information and the updating of memories.…”

Section: Basic Theory and Techniques For Grumentioning

confidence: 99%

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

Cheng,

Cao,

Liu

et al. 2024

Applied Sciences

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The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

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“…Finally, for more works related to the load forecasting, refs. [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] A dataset can be created to train the deep learning-based application (e.g., the ANN) to forecast the values of the loads. Then, based on Figure 4, the current values and the historical values of the desired inputs can be used to predict the future values of power consumption in a cyber-physical microgrid using the trained ANN.…”

Section: Deep Learning-based Load Forecastingmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network

Cited by 13 publications

References 21 publications

A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

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