摘要
基于帝王蝶优化算法,提出了一种新的帝王蝶-BP(Back Propagation)神经网络预测模型,以预测结果的平均绝对误差为目标函数,对BP神经网络模型的初始权重和阈值进行寻优,实现了对江西省能源供需的准确预测,并依据预测结果制定江西省低碳转型路径。通过与已有文献方法和权威公开数据的对比,验证了帝王蝶-BP神经网络优化预测模型的有效性和优越性。
Based on the monarch butterfly optimization algorithm,this paper proposes a new monarch butterfly-BP(Back Propagation)neural network prediction model.The average absolute error of the prediction results is used as the objective function to optimize the initial weight and threshold of the BP neural network model.The accurate prediction of energy supply and demand in Jiangxi Province is realized,and the low-carbon transformation path of Jiangxi Province is formulated based on the prediction results.By comparing with the existing classical literature methods and authoritative public data,the effectiveness and superiority of the monarch butterfly-BP neural network optimization prediction model are verified.
作者
颜高洋
丁贵立
许志浩
王宗耀
康兵
刘向向
YAN Gaoyang;DING Guili;XU Zhihao;WANG Zongyao;KANG Bing;LIU Xiangxiang(School of Electrical Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Jianxi Engineering Research Center of High Power Electronics and Grid Smart Metering,Nanchang Institute of Technology,Nanchang 330099,China;Power Supply Service Management Center of State Grid Jiangxi Electric Power Co,LTD,Nanchang 330001,China)
出处
《南昌工程学院学报》
CAS
2023年第3期88-94,共7页
Journal of Nanchang Institute of Technology
基金
江西省教育厅科学技术研究项目(GJJ211943,GJJ201928)
国网江西省电力有限公司重大科技项目(521852210017)。
关键词
帝王蝶优化算法
BP神经网络
能源预测
参数优化
monarch butterfly optimization
BP neural network
energy prediction
parameter optimization
作者简介
颜高洋(2000-),男,硕士生,1183077220@qq.com;通信作者:丁贵立(1987-),男,博士,讲师,institutional176@163.com.
