摘要
针对传统小波神经网络(WNN)综合能源系统负荷预测模型存在收敛速度、易陷入局部最优等缺点而导致预测精度不高的问题,文中提出了一种基于改进粒子群(IPSO)的WNN综合能源系统短期负荷预测方法。利用Pearson系数对各影响因素进行分析,选择合适的因素作为综合能源负荷预测的输入量;对传统粒子群算法进行改进,在PSO中引入混沌算法并根据粒子适应度采用不同的粒子惯性权重选择策略;基于IPSO建立了WNN综合能源系统短期负荷预测模型。通过案例验证,基于IPSO的WNN预测模型相比于传统WNN预测模型,预测精度明显提升。
An IPSO-WNN method for short-term load prediction of integrated energy system is proposed to solve the problem that the conventional WNN model has the disadvantages of slow convergence speed and easy to fall into local optimum,which leads to the low prediction accuracy.Firstly,the Pearson coefficient is used to analyze the influencing factors and select the appropriate factors as the input quantity of the integrated energy load prediction.Secondly,the traditional particle swarm optimization algorithm is improved,and the chaos algorithm is introduced in PSO and different particle inertia weight selection strategies are adopted according to particle fitness,and then,IPSO-WNN short-term prediction model was established based on the improved particle swarm algorithm(IPSO)to realize load prediction of integrated energy system.Finally,compared with the traditional WNN prediction model,the IPSO-based WNN prediction model has improved the prediction time and accuracy.
作者
李守茂
戚嘉兴
白星振
葛磊蛟
李涛
Li Shoumao;Qi Jiaxing;Bai Xingzhen;Ge Leijiao;Li Tao(School of Electrical and Automation Engineering,Shandong University of Science and Technology,Qingdao 266590,Shandong,China;32654 PLA Troops,Ji′nan 250024,China;Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China)
出处
《电测与仪表》
北大核心
2020年第9期103-109,共7页
Electrical Measurement & Instrumentation
基金
国家重点研发计划项目(2017YFB0903300)
国家自然科学基金资助项目(51807134)。
关键词
负荷预测
综合能源系统
小波神经网络
粒子群算法
混沌搜索
load prediction
integrated energy system
wavelet neural network
particle swarm optimization
chaos search