Improved genetic algorithm-based research on optimization of least square support vector machines: an application of load forecasting

被引:0
|
作者
Lin Bao-De
Zhang Xin-Yang
Zhang Mei
Li Hui
Lu Guang-Qian
机构
[1] Yunnan Power Grid Co Information Center,
[2] KunMing Enersun Technology Co. Ltd.,undefined
来源
Soft Computing | 2021年 / 25卷
关键词
Artificial intelligence (AI); Least square support vector machine; Short-term power load;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting technique based on AI algorithm. And to increase the forecast accuracy and generalization capability of LS-SVM, we applied the adopted mutation probability and new coding technology to the parameter optimization of LS-SVM. The temperature, load, weather state, working and holiday days be taken as prediction model as input, and load value was predicted output. We selected the sample data from meteorological information and historical load of a city in Yunnan province. By results, the prediction verifies the good prediction effect when associated with existing BP algorithm and the proposed IGA- LS-SVM algorithm yields a value 0.8274 more significant than all others, which is appropriate for short-term power load prediction.
引用
收藏
页码:11997 / 12005
页数:8
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