Wind speed prediction based on singular spectrum analysis and neural network structural learning

Mi, Xiwei; Zhao, Shuo

doi:10.1016/j.enconman.2020.112956

Cited by 77 publications

(15 citation statements)

References 39 publications

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“…Step 5: The signal, we need finally, is reconstructed according to (5). where φ 1 indicates empirical scale function, while W ε f (0, ω) and W ε f (n, ω) are transformed from W ε f (0, t) and W ε f (n, t) using the method of Fourier transform, respectively.…”

Section: A Empirical Wavelet Transformmentioning

confidence: 99%

“…Wind power data changing with various factors, leading to dynamic features and time-varying information, is generally gathered as time-series, which may include the main trend, periodic trend and quasi-periodic trend implicitly [5]. Therefore, volatility of the wind power series impact undoubtedly the prediction effect of predict engine that could not get rid of influence for noises, which are implied in raw wind power series [6].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

Yan

2020

IEEE Access

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As the wind power dates have strong volatility and intermittency, it is difficult to meet the safety and stability of power system operation. To make full use of the characteristics of wind power data and further improve wind power forecasting performance, an innovative hybrid framework is proposed to response to the above challenges, of which the non-stationary weakening technique, sample entropy (SE)-based prediction model allocation, optimized reduced kernel extreme learning machine (RKELM) and a novel deep learning network are integrated befittingly. Initially, the original wind power sequence is decomposed into multiple sub-sequences by empirical wavelet transform (EWT) adaptively, after which SE is employed to analyse the high and low components of sub-sequences. Then, a simplified gate recurrent unit network (SGRU) to achieve the prediction of the component with low SE-value. Besides, for the components with high SE-values, the multiple parameters optimization is implemented for RKELM based on slime mould algorithm (SMA), which possesses superior convergence speed and precision. Ultimately, the final prediction results can be obtained by accumulating the predicted values of the multiple models. In the experiment phase, two datasets are adopted to verify the predictive ability of the proposed hybrid EWT-SMA-RKELM-SGRU model, where the sufficient results indicate that the proposed model has a superior performance.INDEX TERMS short-term wind power forecasting, prediction model allocation, slime mould algorithm, multiple parameters optimization, simplified gate recurrent unit network

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Section: A Empirical Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

Yan

2020

IEEE Access

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“…Nowadays, popular deep learning methods have been commanly prefered in many literature studies for wind speed estimation [20]. Convolutional neural networks (CNN) [21], long-short term memory (LSTM) [22], neural network structural learning [23], recurrent neural network (RNN) [24] Transfer learning [25], etc. These deep learning methods are the most popular ones and used widespread.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Estimation Model (CNN-GRU) Based on Deep Learning for Wind Speed Estimation

Emeksiz¹,

FINDIK²

2022

IJMSIT

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Nowadays, the need for energy is increasing day by day. In order to meet this demand, renewable energy sources that have a more environmentally friendly structure than fossil-based sources come to the fore. In recent years, researchers have been paying great attention to wind energy. Because it has the many economic and environmental advantages. In particular, wind speed is very important parameter for electric energy production form wind energy. Therefore, estimation of wind speed is very important for both investors and manufacturers. A hybrid model for wind speed estimation with deep learning methods is proposed in this study. The proposed model consists two main deep learning methods (Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU)). The proposed model was applied in two case studies (weekly and monthly wind speed estimation). The reliability and accuracy of the proposed model were tested by performance criteria (MAPE, R 2 , RMSE). In order to measure the success of the model, a comparison was made with 5 different deep learning methods (CNN-LSTM, CNN-RNN, LSTM-GRU, LSTM, GRU). It has been observed that the CNN-GRU hybrid model, which was used for the first time in the field of wind speed forecasting, achieved a high percentage of success as a result of comparisons made.

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“…The abovementioned temperature detecting method could obtain the real-time monitoring of the axle temperature, but these methods cannot predict the changing trend of the temperatures, which is more helpful to conduct preventive measures and to avoid unnecessary loss of equipment maintenance. In recent years, researchers have put forward many prediction methods in the research field of fault diagnosis [9,10], temperature forecasting [11][12][13], wind speed forecasting [14], power forecasting [15,16], traffic flow prediction [17,18], air pollutant forecasting [19] and so on. Hence, it is meaningful to apply effective data-driven approaches to the axle temperatures for real-time status detection and prediction.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

Yan

Bai

2021

Machines

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The axle temperature is an index factor of the train operating conditions. The axle temperature forecasting technology is very meaningful in condition monitoring and fault diagnosis to realize early warning and to prevent accidents. In this study, a data-driven hybrid approach consisting of three steps is utilized for the prediction of locomotive axle temperatures. In stage I, the Complementary empirical mode decomposition (CEEMD) method is applied for preprocessing of datasets. In stage II, the Bi-directional long short-term memory (BILSTM) will be conducted for the prediction of subseries. In stage III, the Particle swarm optimization and gravitational search algorithm (PSOGSA) can optimize and ensemble the weights of the objective function, and combine them to achieve the final forecasting. Each part of the combined structure contributes its functions to achieve better prediction accuracy than single models, the verification processes of which are conducted in the three measured datasets for forecasting experiments. The comparative experiments are chosen to test the performance of the proposed model. A sensitive analysis of the hybrid model is also conducted to test its robustness and stability. The results prove that the proposed model can obtain the best prediction results with fewer errors between the comparative models and effectively represent the changing trend in axle temperature.

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Wind speed prediction based on singular spectrum analysis and neural network structural learning

Cited by 77 publications

References 39 publications

A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

A Hybrid Short-Term Wind Power Prediction Model Combining Data Processing, Multiple Parameters Optimization and Multi-Intelligent Models Apportion Strategy

Hybrid Estimation Model (CNN-GRU) Based on Deep Learning for Wind Speed Estimation

A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

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