Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

Chen, Jie; Zeng, Guo‐Qiang; Zhou, Wuneng; Du, Wei; Lu, Kuang Lieh

doi:10.1016/j.enconman.2018.03.098

Cited by 347 publications

(111 citation statements)

References 41 publications

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“…A practical time series prediction-based non-stationarity detection method was proposed [16]. Chen et al [17] introduced a new wind speed prediction method based on LSTMs, SVRM and EO deep learning time series prediction nonlinear and learning integration. Based on the nonlinear learning integration of LSTM, SVRM and EO, the proposed LSTM system achieved satisfactory wind speed prediction performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Guo

Jiang²,

Jiang³

et al. 2019

J Supercomput

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In recent years, bridge health monitoring system has been widely used to deal with massive data produced with the continuous growth of monitoring time. However, how to effectively use these data to comprehensively analyze the state of a bridge and provide early warning of bridge structure changes is an important topic in bridge engineering research. This paper utilizes two algorithms to deal with the massive data, namely Kohonen neural network and long short-term memory (LSTM) neural network. The main contribution of this study is using the two algorithms for health state evaluation of bridges. The Kohonen clustering method is shown to be effective for getting classification pattern in normal operating condition and is straightforward for outliers detection. In addition, the LSTM prediction method has an excellent prediction capability which can be used to predict the future deflection values with good accuracy and mean square error. The predicted deflections agree with the true deflections, which indicate that the LSTM method can be utilized to obtain the deflection value of structure. What's more, we can observe the changing trend of bridge structure by comparing the predicted value with its limit value under normal operation.

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Section: Literature Reviewmentioning

confidence: 99%

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Guo

Jiang²,

Jiang³

et al. 2019

J Supercomput

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“…It is often used in time series problems such as speech recognition, machine translation, handwriting recognition and so on. It also achieves good results in wind speed and power prediction [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Jie Chen [8] and others thought that when it comes to using the integration method to build wind speed series models, it is insufficient to only use the linear combination method. Instead, it should take the structure of non-linear combination into consideration.…”

Section: Introductionmentioning

confidence: 99%

Multi-time scale wind speed prediction based on WT-bi-LSTM

et al. 2020

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The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an autoregression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.

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“…In [16] authors introduced an ensemble classifier for software defect prediction. Ensemble classifiers are also used for multi-class imbalanced data classification [17] and time-series forecasting applications [18][19]. Limited work have been carried out for diabetes prediction using ensemble learning such as voting based classifier [20], SVM based ensemble learning [21] and k-nearest neighbors, naïve Bayes, decision tree, Support Vector Machine, fuzzy decision tree, artificial neural network, and logistic regression based learning approaches [22].…”

Section: Introductionmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

Cited by 347 publications

References 41 publications

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Multi-time scale wind speed prediction based on WT-bi-LSTM

ENN Ensemble based Neural Network method for Diabetes Classification

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