A novel hybrid approach based on cuckoo search optimization algorithm for short-term wind speed forecasting

2017 ◽  
Vol 36 (3) ◽  
pp. 943-952 ◽  
Author(s):  
Kequan Zhang ◽  
Zongxi Qu ◽  
Jianzhou Wang ◽  
Wenyu Zhang ◽  
Feiyue Yang
Keyword(s):  
Wind Speed ◽  
Optimization Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Search Optimization ◽  
Cuckoo Search Optimization

Cuckoo Search Optimization Algorithm for Designing of a Multimachine Power System Stabilizer

2018 ◽  
Vol 54 (4) ◽  
pp. 3056-3065 ◽  
Author(s):  
Dhanraj Chitara ◽  
Khaleequr Rehman Niazi ◽  
Anil Swarnkar ◽  
Nikhil Gupta
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Cuckoo Search ◽  
Power System Stabilizer ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
System Stabilizer

scholarly journals Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chen Wang ◽  
Jie Wu ◽  
Jianzhou Wang ◽  
Zhongjin Hu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Parameter Optimization ◽  
Cuckoo Search ◽  
Support Vector ◽  
Optimization Approach ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Comparison Results

Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

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