A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM

Chen, Min-Rong; Zeng, Guo‐Qiang; Lu, Kuang Lieh; Weng, Jian

doi:10.1109/jiot.2019.2913176

Cited by 210 publications

(69 citation statements)

References 39 publications

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“…, n is the number of power error samples. Based on the principle of the nonparametric KDE, the PDF of wind power prediction error is estimated as (14).…”

Section: A Nonparametric Kernel Density Estimationmentioning

confidence: 99%

“…Huang et al [13] proposed an enhanced harmony search (EHS) algorithm to implement the selection of support vector regression (SVR) model parameters. Chen et al [14] made correlation research on wind speed prediction, which was based on extreme learning machine (ELM), Elman Neural Network and LSTM Network. Recently, A large number of new methods based on LSTM for renewable energy and load forecasting are proposed.…”

Section: Introductionmentioning

confidence: 99%

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Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

et al. 2019

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Wind energy is a kind of sustainable energy with strong uncertainty. With a large amount of wind power injected into the power grid, it will inevitably affect the security, stability and economic operation of the power grid. High-precision wind power spot prediction and fluctuation interval information can provide more adequate decision-making support for grid scheduling and optimization. Hence, this paper proposes a K-Means-long short-term memory (K-Means-LSTM) network model for wind power spot prediction, and a nonparametric kernel density estimation (KDE) model with bandwidth optimization for wind power probabilistic interval prediction. The long short-term memory (LSTM) network has a strong memory function, which can establish the correlation between the data before and after, so as to improve the prediction accuracy. The K-Means clustering method forms different clusters of wind power impact factors to generate a new LSTM sub-prediction model. The optimization of the bandwidth in the nonparametric KDE is implemented by the mean integrated squared error criterion. In addition, a part of the dataset is deliberately demarcated from the wind power historical dataset to generate reasonable wind power prediction errors. The simulation results show that the proposed K-Means-LSTM network model has higher prediction accuracy than the back propagation (BP) neural networks, Elman neural networks, support vector regression (SVR) and LSTM network models. Compared with the KDE model with random bandwidth and the Gaussian distribution model, the bandwidth optimization model proposed in this paper has more narrow prediction intervals with higher interval coverage rates. INDEX TERMS Wind power prediction, Kernel density estimation, long short-term memory, K-means clustering, probabilistic interval prediction.

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“…, n is the number of power error samples. Based on the principle of the nonparametric KDE, the PDF of wind power prediction error is estimated as (14).…”

Section: A Nonparametric Kernel Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

et al. 2019

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“…Furthermore, the single WS data models are often associated with low computational complexity and are therefore often suggested for short-term WS prediction [24]. Several algorithms have been deployed in designing single WS data models to ensure maximum utilization of antecedent WS data for prediction of WS; such frameworks include signal processing and time series algorithms [25]- [28]. The time series algorithms are classified as the classical algorithms for WS prediction which contain persistence algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

et al. 2020

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Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station 1 ) and Mosul (Station 2 ) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station 1 and Station 2 , respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.INDEX TERMS Wind speed prediction, multivariate empirical mode decomposition, random forest, Kernel Ridge Regression, Iraq region.

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“…Yu, R. et al [19] proposed a LSTM-EFG method based on sequential correlation features for wind power forecasting. Chen et al [20] proposed a method to prediction the wind speed based on ELM, Elman and LSTM network. It can track the changes in wind speed of the wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

et al. 2020

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With the continuous elevation of demand for large-scale wind turbines and operation & maintenance cost an increasing interest has been rapidly generated on CM (Condition Monitoring) system. The main components of wind turbines are the focus on all CM as they overall lead to high repair costs and equipment downtime. Thus, it is difficult to make comprehensive assessment in the assessment. In the present study, intelligent machine learning algorithms are adopted to mine SCADA (Supervisory Control and Data Acquisition) system data of WTs (wind turbines). Besides, based on bidirectional long short-term memory (BiLSTM) neural networks and gaussian mixture model (GMM) algorithm, this study developed a multi-running state health assessment model for the drive system of wind turbines. First, the stateidentification model is built with health data to overcome the effect of the time-varying characteristics of running environment and alterations of running condition during the assessment. Then, in each state, the BiLSTM algorithm is adopted to extract the residual set of valid state variables, and the GMM algorithm is employed to accurately fit the distribution of residual set. The multi-running state benchmark model based on BiLSTM and GMM is built. Subsequently, the drive system of wind turbines health degree is calculated by health index. Lastly, based on multiple driving system faults data of a wind turbine, the feasibility and validity of the model are verified.

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A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM

Cited by 210 publications

References 39 publications

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Multi-Running State Health Assessment of Wind Turbines Drive System Based on BiLSTM and GMM

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