A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Xia, Min; Shao, Haidong; Ma, Xiandong; Silva, Clarence W. de

doi:10.1109/tii.2021.3056867

Cited by 197 publications

(57 citation statements)

References 26 publications

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“…The RNN-based FDD has the following advantages: (1) The inputs of the RNN are time-series data and the depth depends on the length of the input sequence, which is suitable for dynamic PV systems monitoring and prediction; (2) RNN are Turing complete, the chain connection mode is conducive to the extraction and representation of the dynamic nonlinear characteristics of PV systems; (3) The RNN is stable when the length of the learning and testing sequence are different (PV system control is often of variable length and the sampling is irregular). For the time-series signals of PV systems, the authors in [77]- [79] used monotonicity and correlation values to select features as the RNN network inputs, and verified experimentally the performance of the proposed RNN-based method. The work in [80] proposed an LSTM-based encoder-decoder architecture.…”

Section: B Recurrent Neural Network Based Fault Diagnosismentioning

confidence: 92%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DLbased FDD for PV systems and provide guidelines for future research in the field.

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Section: B Recurrent Neural Network Based Fault Diagnosismentioning

confidence: 92%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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“…Experiment and analysis In this section, in order to demonstrate the practicality of the method proposed in this paper, data from two different regions, Singapore and the United States, are selected for forecasting in the most recent months. Four models, ARIMA, CEEMD+LSTM, VMD+LSTM and GRU+RNN [22] are selected for comparison with the model proposed in this paper. Since the IMFs decomposed by VMD and CEEMD decomposition have different characteristics, the corresponding LSTM hyperparameters are also different.…”

Section: Performance Indicatorsmentioning

confidence: 99%

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Zhang

et al. 2022

IEEE Trans. Ind. Inf.

130

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As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Variational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

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“…GRU-RNN is an improved form of RNN, which could achieve higher precision and robustness [28][29][30][31]. In a simple way for explanation of RNN, some outputs of its neuron can be used as its input to be transmitted to the neuron again, and historical information can be retained and used, which is very effective for dealing with timing problems.…”

Section: Basics Of Gru-rnnmentioning

confidence: 99%

“…rough a weight matrix, the value of the previous time series neuron can be transferred to the current neuron so that the neural network has the memory. e gated RNN (GRU-RNN) [28][29][30][31] is an improved form of RNN. By introducing different "gating" mechanisms in the hidden layer nodes of the RNN, it can process long-interval time series signals to get more data characteristics and time dependence.…”

Section: Introductionmentioning

confidence: 99%

Inflation Prediction Method Based on Deep Learning

Yang

Guo

2021

Computational Intelligence and Neuroscience

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Forward-looking forecasting of the inflation rate could help the central bank and other government departments to better use monetary policy to stabilize prices and prevent the impact of inflation on market entities, especially for low- and middle-income groups. It can also help financial institutions and investors better make investment decisions. In this sense, the forecast of inflation rate is of great significance. The existing literature mainly uses linear models such as autoregressive (AR) and vector autoregressive (VAR) models to predict the inflation rate. The nonlinear relationship between variables and the mining of historical data information are relatively lacking. Therefore, the prediction strategies and accuracy of the existing literature need to be improved. The predictive model designed in deep learning can fully mine the nonlinear relationship between variables and process complex long-term time series dynamic information, thereby making up for the deficiencies of existing research. Therefore, this paper employs the recurrent neural networks with gated recurrent unit (GRU-RNN) model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the GRU-RNN model has good performance in predicting China’s inflation rate. In comparison, the performance of the proposed method is significantly better than some traditional models, showing its superior effectiveness.

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A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Cited by 197 publications

References 26 publications

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Inflation Prediction Method Based on Deep Learning

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