Advanced method for short‐term wind power prediction with multiple observation points using extreme learning machines

Mahmoud, Tawfek; Zhang, Dong; Ma, Jin

doi:10.1049/joe.2017.0338

Cited by 14 publications

(4 citation statements)

References 77 publications

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“…Artificial neural network is widely used in wind power prediction. The types of these neural networks include back propagation neural network (Hu and Zhang, 2018; Li et al, 2020b; Wang et al, 2015a), radial basis function neural network (Chang, 2013; Ter Borg and Rothkrantz, 2006; Zhang et al, 2016), echo state network (Dorado-Moreno et al, 2017; Gouveia et al, 2018; Wang et al, 2019a; Xu et al, 2015), and extreme learning machine (Mahmoud et al, 2018ba, 2018b; Mohammadi et al, 2015). The artificial neural network has the characteristics of self-adaptive and self-learning, which can deal with complex systems, but it has the problems of slow training speed, difficult to determine the network structure and parameters, and easy to fall into local optimum.…”

Section: The Deterministic Prediction Of Wind Powermentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

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With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

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Section: The Deterministic Prediction Of Wind Powermentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

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“…In ELM. Huang et al have solved this problem using Moore–Penrose pseudo inverse of H matrix to overcome this problem [27, 30, 31].…”

Section: Extreme Learning Machinementioning

confidence: 99%

Extreme learning machine‐based alleviation for overloaded power system

Imen

Djamel

2019

IET Generation, Transmission & Distribution

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“…The clustering method is used to symbolize the time series first, a sequence pattern mining algorithm is used to find the rules in the symbol [35,36]. An evolutionary rule based on expert system was proposed, which combined fuzzy logic and rule inference for the analysis of stock market activities [37,38]. Using the methods of fuzzy logic, ANN, and evolutionary computation, the trend of the Nasdaq-100 index value and the Nasdaq-100 index of six other companies were predicted [39].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence algorithm for optimal time series data model

Wang

2024

IEEE Access

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In order to solve the limitation of a large number of literatures on the study of modeling, simulation and prediction of time series data, there is no model selection, and a certain model is directly used for analysis. For three types of artificial intelligence models often applied to time series analysis: hidden Markov Carrier model, artificial neural network model and autoregressive moving average model are used to study model selection based on simulation comparison method. The study of nonlinear integration methods, using intelligent system methods to learn the weighting mode, has made the model's generalization ability and the degree is of fit to the sample data have been significantly improved. At the same time, numerical simulations are performed on various models, and the characteristics of the time series generated by various models are investigated. Based on the characteristics, the theory and algorithm of model selection are proposed. The model selection theory and algorithm in this paper is used for empirical analysis. For the artificial intelligence models commonly used in time series analysis such as autoregressive moving average model, artificial neural network model, hidden Markov model, etc., when selecting the research model, the method of simulation comparison can be used. The experimental results show that the time series data generated by various models have different mathematical and physical characteristics, which provide a basis for model selection. At the same time, the selection theory is practical. The model selected by the theory has a good fit and prediction effect. The generation of different models has different mathematical characteristics of time series data, which also provides a basis for selecting models.

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Advanced method for short‐term wind power prediction with multiple observation points using extreme learning machines

Cited by 14 publications

References 77 publications

A state-of-the-art review on wind power deterministic prediction

A state-of-the-art review on wind power deterministic prediction

Extreme learning machine‐based alleviation for overloaded power system

Artificial intelligence algorithm for optimal time series data model

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