Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Liu, Zhenling; Hajiali, Mahdi; Torabi, Amirhosein; Ahmadi, Bahman; Simoes, Rolando

doi:10.1007/s12652-018-0886-0

Cited by 39 publications

(15 citation statements)

References 41 publications

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“…are also used both together with ANN and alone. Liu et al (2018) proposed a novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine for wind power forecasting. Zhao Finally, we end the paper with conclusions in Section 7.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

Baş

Eğrioğlu

Yolcu

2020

J Ambient Intell Human Comput

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In the literature, the multiplicative neuron model artificial neural networks are trained by gradient-based or some artificial intelligence optimization algorithms. It is well known that the hybrid algorithms give successful results than classical algorithms in the literature and the use of hybrid systems increase day by day. From this point of view, different from other studies contribute to multiplicative neuron model artificial neural networks, the properties of an artificial intelligence optimization technique, artificial bat algorithm, and a gradient-based algorithm, backpropagation learning algorithm, is used together firstly by using the proposed method in this study. Thus, both a derivative and a heuristic algorithm were used together firstly for multiplicative neuron model artificial neural networks. The proposed method is applied to three well-known different real-world time series data. The performance of the proposed method is both compared with gradient-based optimization algorithms, some artificial optimization algorithms used for the training of artificial neural networks and some popular analyze methods. The analysis results show that the proposed hybrid method has superior performance than other methods.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

Baş

Eğrioğlu

Yolcu

2020

J Ambient Intell Human Comput

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“…At present, a lot of research has been conducted on ultra-short-term wind power prediction around the world. In early years, the Single-hidden Layer Feed Forwarding Networks (SLFNs) [2], the wavelet decomposition algorithm [3], [4], the empirical mode decomposition [5] and the other algorithms combined with traditional neural networks (such as BP neural network, Support Vector Machine, etc.) can process and predict wind power time series data.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy. INDEX TERMS Wind power prediction, salp swarm algorithms, extreme learning machine, optimization algorithm.

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“…Because of the intermittent characteristics of wind power generation, wind power generation will impact the power grid. If wind power is directly connected to the grid, it will affect the voltage and frequency of the power system, thus affecting the stable operation of the power system [8,9]. By forecasting wind power, the power generation plan can be reasonably arranged, which can avoid large fluctuations in the power system.…”

Section: Introductionmentioning

confidence: 99%

“…And The final prediction results are synthesized. The wind power was predicted by the support vector machine (SVM), as in [9]. The parameters of SVM are optimized by the enhanced particle swarm optimization algorithm in this method to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Li²,

Lin

et al. 2019

Sustainability

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Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

Cited by 39 publications

References 41 publications

RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

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

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

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