Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

Zhong, Jian; Dong, Guanpeng; Yimei, Sun; Zhang, Zhaoyang; Wu, Yuqin

doi:10.1088/1674-1056/25/11/110502

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…This method fits a large amount of historical meteorological data and power data by statistical regression, and the commonly used methods are regression analysis and time series method. For example, Wang et al [11] proposed an adaptive robust multicore regression model to solve the problem of inconsistency between the generic Gaussian assumption of the prediction model error term and the real wind power forecasting error distribution; Zhong et al [12] proposed a genetic algorithm nonlinear time series prediction method based on singular spectrum analysis for predicting wind resources at sites in the South China Sea, and the results were better than the persistence model.…”

Section: Classification According To the Modeling Methodsmentioning

confidence: 99%

Coastal Wind Power Forecasting Research Scheme Design

Xu¹

2022

FCIS

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As global resource and environmental problems become increasingly acute and climate change becomes more and more obvious, the large-scale development and utilization of new energy sources are highly valued by all countries in the world. Wind energy is highly valued because of its many advantages. Wind power, especially offshore wind power, has become one of the key development directions of renewable energy. To address the problem that coastal wind farms are in urgent need of accurate wind power forecasting, the authors of this paper investigate and summarize the relevant research work in this field. At the same time, the coastal meteorological micro-scale differences are obvious, and a research program for coastal wind farm power forecasting is designed to lay the foundation for subsequent specific research for the characteristics of this region.

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Section: Classification According To the Modeling Methodsmentioning

confidence: 99%

Coastal Wind Power Forecasting Research Scheme Design

Xu¹

2022

FCIS

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“…However, this kind of methods cannot afford to deal with the non-linear problems [8]. Owning to the ability to recognize the non-linear characters, the intelligent approaches, for instance artificial neural networks (ANNs) [9][10][11], support vector machine (SVM) [12], the genetic algorithm [13] and the general regression neural network (GRNN) [14], have been utilized to forecast wind speed effectively. Due to the superior ability to recognize the non-linear structure, intelligent approach is better at forecasting the wind speed of short period than traditional time series based methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy

et al. 2019

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It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence.

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Fault diagnosis and failure prognosis in hydraulic systems

Liu

2021

Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems

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Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

Cited by 3 publications

References 20 publications

Coastal Wind Power Forecasting Research Scheme Design

Coastal Wind Power Forecasting Research Scheme Design

Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy

Fault diagnosis and failure prognosis in hydraulic systems

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