Short-term Load Forecasting of Smart Grid Systems by Combination of General Regression Neural Network and Least Squares-Support Vector Machine Algorithm Optimized by Harmony Search Algorithm Method

2013 ◽  
Vol 7 (1L) ◽  
pp. 291-298 ◽  
Author(s):  
Ming Zeng ◽  
Song Xue ◽  
Zhijie Wang ◽  
Xiaoli Zhu ◽  
Ge Zhang
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Load Forecasting ◽  
Harmony Search Algorithm ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Short Term ◽  
Grid Systems ◽  
Short Term Load Forecasting ◽  
General Regression

scholarly journals Short-term Load Forecasting Model for Microgrid Based on HSA-SVM

2018 ◽  
Vol 173 ◽  
pp. 01007
Author(s):  
Han Aoyang ◽  
Yu Litao ◽  
An Shuhuai ◽  
Zhang Zhisheng
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Harmony Search ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Search Optimization ◽  
Svm Model ◽  
Short Term Load Forecasting

Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.

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