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

2019 ◽  
Vol 11 (2) ◽  
pp. 512 ◽  
Author(s):  
Chao Fu ◽  
Guo-Quan Li ◽  
Kuo-Ping Lin ◽  
Hui-Juan Zhang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Wind Power ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction ◽  
Svm Model

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.

A Method for Forecasting Short-Term Wind Speed Based on EMD and SVM

2013 ◽  
Vol 392 ◽  
pp. 622-627 ◽  
Author(s):  
Xiao Jing Dang ◽  
Hao Yong Chen ◽  
Xiao Ming Jin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Power Prediction ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Svm Model

In this paper, a method for wind speed forecasting based on Empirical Mode Decomposition and Support Vector machine is proposed. Compared with the approach based on Support Vector machine only, the method in this paper use EMD to decompose the data of wind power into several independent intrinsic mode functions (IMF),then model each component with the SVM model and get the final value of the overall wind power prediction. Experiments show the efficiency of the approach with a higher forecasting accuracy.

scholarly journals Short-term wind power prediction based on hybrid variational mode decomposition and least squares support vector machine optimized by improved salp swarm algorithm model

2021 ◽  
Vol 2125 (1) ◽  
pp. 012012
Author(s):  
Zhongde Su ◽  
Huacai Lu
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Power Prediction ◽  
Short Term ◽  
Salp Swarm Algorithm ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Swarm Algorithm

Abstract To improve the accuracy of wind power prediction, a short-term wind power prediction model based on variational mode decomposition (VMD) and improved salp swarm algorithm (ISSA) optimized least squares support vector machine (LSSVM) is proposed. In the model, the variational modal decomposition is used to decompose the wind power sequence into multiple eigenmode components with limited bandwidth. The improved salp swarm algorithm is employed to tune the regularization parameter and kernel parameter in LSSVM. The proposed wind power prediction strategy using mean one-hour historical wind power data collected from a wind farm located in zhejiang, China. Compared with other prediction models illustrate the better prediction performance of VMD-ISSA-LSSVM.

Wind Power Forecast Based on Artificial Bee Colony Algorithm

2015 ◽  
Vol 738-739 ◽  
pp. 417-422 ◽  
Author(s):  
Xin Zhang ◽  
Guo Chu Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Optimal Parameters ◽  
Power Prediction ◽  
Bee Colony ◽  
Forecast Precision ◽  
Wind Power Forecast ◽  
Wind Power Prediction ◽  
Svm Model

In view of the traditional support vector machine (SVM) model in wind power prediction parameter selection problems, this paper introduced a model which using artificial colony algorithm to seek the optimal parameters of support vector machine. The experimental results show that the SVM model of artificial swarm optimization application and prediction is effective, makes the forecast precision is improved.

scholarly journals Ultra-short-term wind speed forecasting based on support vector machine with combined kernel function and similar data

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jian He ◽  
Jingle Xu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Kernel Functions ◽  
Support Vector ◽  
Similar Data ◽  
Power Prediction ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Power Prediction

Abstract The accuracy of wind power prediction is very important for the stable operation of a power system. Ultra-short-term wind speed forecasting is an effective way to ensure real-time and accurate wind power prediction. In this paper, a short-term wind speed forecasting method based on a support vector machine with a combined kernel function and similar data is proposed. Similar training data are selected based on the wind tendency, and a combination of two kinds of kernel functions is applied in forecasting using a support vector machine. The forecasting results for a wind farm in Ningxia Province indicate that a combination of kernel functions with complementary advantages outperforms each single function, and forecasting models based on grouped wind data with a similar tendency could reduce the forecasting error. Furthermore, more accurate wind forecasting results ensure better wind power prediction.

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