Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Tan, Lian; Han, Jing; Zhang, Hongtao

doi:10.1109/access.2020.2978098

Cited by 73 publications

(38 citation statements)

References 29 publications

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“…In addition to the above research methods, scholars have adopted other predictive methods in the field of wind power. For instance, Tan et al explored and developed the Salp Swarm Algorithm in the iterative process to optimize the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM), in order to improve the adaptability and accuracy of the prediction model [24]. Wang et al build a hybrid PSO-SVM-ARMA prediction model for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights [25].…”

Section: Neural Network In Wind Power Industry Forecastingmentioning

confidence: 99%

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Luo

Zeng

et al. 2021

Journal of Mathematics

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The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

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Section: Neural Network In Wind Power Industry Forecastingmentioning

confidence: 99%

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Luo

Zeng

et al. 2021

Journal of Mathematics

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“…In recent years, scholars worldwide have carried out much research on the realization of wind power forecasting by constructing statistical models. Among them, the two methods of support vector machine (SVM) and neural network have achieved satisfactory prediction results [14]. In terms of realizing wind power prediction based on the SVM method, Ning et al [15] used particle swarm algorithm to optimize the penalty factor and kernel parameters of SVM and then used historical data as training samples to train the optimized SVM model.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [19] used genetic algorithm to optimize the input weights of ELM; Zhai et al [20] and Tan et al [21] used artificial fish swarm algorithm and salp swarm algorithm to optimize the initial input weights and thresholds of the ELM, respectively. Although these optimization algorithms have improved the prediction accuracy of the model to a certain extent, in some cases, there will be over-fitting phenomena, which will make the model fall into the local optimum and affect the generalization ability of the prediction model [21]. The Adaboost algorithm is an integrated learning algorithm proposed by Schapire and Freund [22].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Jiang

Cao

et al. 2021

IEEE Access

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As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

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“…Its own distributed storage and fault tolerance, parallel processing, self-organization, self-learning, self-adaptation, and other characteristics, so that it can be applied for short-term wind power prediction. The representative used neural network for short-term wind power include radial basis function neural network (Dadkhan et al, 2018), fuzzy neural network (Dong et al, 2017; Sharifian et al, 2018), Elman neural network (Xu and Mao, 2016), extreme learning machine (ELM) (Ding et al, 2020; Tan et al, 2020), wavelet neural network (Santhosh et al, 2018; Yao et al, 2013), BP network (Zhang et al, 2020), and so forth. Nevertheless, it is difficult to scientifically determine the network structure, slow learning speed, local optimal problem, memory instability, and other inherent defects, so that the neural network prediction accuracy is difficult to ensure.…”

Section: Introductionmentioning

confidence: 99%

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

Tian

2021

Transactions of the Institute of Measurement and Control

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In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

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Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Cited by 73 publications

References 29 publications

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

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