Short-term wind speeds prediction of SVM based on simulated annealing algorithm with Gauss perturbation

2019 3rd International Workshop on Renewable Energy and Development

能源学;生态环境科学

Chen, Yan^1 ; Chen, Rui^1 ; Ma, Chunyan^1 ; Tan, Peiran^2

College of Electrical and Power Engineering, Taiyuan University of Technology, Shanxi Province, Taiyuan

030024, China^1

State Grid Shanxi Metering Center, Shanxi Province, Taiyuan

030032, China^2

关键词: Dynamic neural networks;    Ensemble empirical mode decompositions (EEMD);    Gaussian disturbances;    Grey relational analyses (GRA);    Large-scale wind power generations;    Nonlinear autoregressive model;    Simulated annealing algorithms;    Wind speed prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/267/4/042032/pdf
DOI  :  10.1088/1755-1315/267/4/042032

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Wind speed prediction is an efficient means to reduce the downside effects of large-scale wind power generation on the grid, but the behaviour of wind speeds is nonlinear and non-stationary, which yields adverse challenge for its prediction. This work proposes a method of prediction for short-term wind speed, which makes Simulated Annealing Fruit fly Optimization Algorithm based on Gaussian Disturbance (GDSAFOA) to optimize the Support Vector Machine (SVM). In the method, Grey Relational Analysis (GRA) is used to select the factors which influence wind speeds prediction. A time series of wind speeds is decomposed by the Ensemble Empirical Mode Decomposition (EEMD). The wind speeds predication is the linear combination of the SVM and the dynamic neural network model based on the nonlinear autoregressive models with exogenous inputs (NARX). This method is applied for the model with wind speeds data measured from a wind farm in China's Shanxi Province, where results exhibit that the proposed method is feasible and competitive.