A Combined Model for Short-term Load Forecasting Based on Bird Swarm Algorithm

被引：0
|
作者：
Cao, Zhengcai [1 ]
Liu, Lu [1 ]
Zhou, Meng [1 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
来源：
2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | 2018年
基金：
中国国家自然科学基金;
关键词：
NETWORK;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Short-term load forecasting (STLF) plays a very important role in the power system scheduling of smart grid. In this paper, a variable weight combined load forecasting model is proposed, effectively improves the accuracy of short-term load forecasting. A prediction model is presented by combining there single prediction models, i.e. random forest, extreme learning machine and Elman neural network. Then a bird swarm-based intelligent algorithm is utilized to solve the weighting problem among them. Experimental results demonstrate that the new constructed prediction model has higher prediction accuracy than any single load forecasting model.
引用
收藏
页码：791 / 796
页数：6
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