摘要
针对单一的预测方法难以综合描述冷负荷变化的规律性问题,本文以初投入使用的青岛市某自习室空调系统为研究对象,对基于改进算法的空调冷负荷组合预测进行研究。为获得动态负荷数据,搭建了TRNSYS模拟仿真平台,对扰动因子经平均影响值(mean impact value,MIV)和Spearman相关性分析及特征变量筛选后,对预测算法进行优化。通过引入随机粒子和混沌算法,建立基于标准粒子群算法的组合粒子群算法(combined particle swarm optimization,CPSO),得到组合粒子群优化后向传播网络(back propagation,BP)负荷预测模型CPSO-BP,并引布谷鸟搜索(cuckoo search,CS),确立布谷鸟搜索支持向量回归(support vector regression,SVR)负荷预测模型CS-SVR,建立基于遗传寻优的灰色预测模型GA-GM(1,N)。同时,将各模型的负荷预测值带入模糊系统中,建立实时模糊组合预测模型(fuzzy combination,FC),并采用Markov(M)对组合误差进行修正。结果表明,基于Markov的模糊组合预测算法FC-M优于CPSO-BP、CS-SVR和FC,组合精度与3个优化模型相比分别提高了26.32%,62.16%,94.68%,说明基于马尔可夫的模糊组合预测算法FC-M可以弥补各算法的不足,降低了预测误差,提高了预测准确率。该研究为空调节能运行策略的制定提供了理论参考。
In order to solve the problem that it is difficult to comprehensively describe the regularity of cooling load change with a single forecasting method,this article takes the cooling load in a study room in Qingdao,China,which has been put into use for the first time,as the research object,and establishes a TRNSYS simulation platform to obtain sufficient dynamic load data.After using the mean influence value(MIV)and Spearman correlation coefficient to screen the characteristic variables,the prediction models are optimized:the random particle and chaos algorithm are introduced to establish the combined particle swarm optimization(CPSO)algorithm based on standard particle swarm optimization(PSO)algorithm.This is done in order to optimize back propagation(BP)and establish CPSO-BP forecasting model;The cuckoo search support vector regression(CS-SVR)forecasting model is established by introducing cuckoo search(CS);The grey prediction model GA-grey(1,N)based on genetic optimization(GA)is established;Load prediction values of each model are brought into the fuzzy system to establish the real-time fuzzy combination(FC)model.Finally,Markov(M)is used to correct the combination error.The results show that FC-M is superior to CPSO-BP,CS-SVR and FC,and accuracy is respectively,26.32%,62.16%,94.68%higher than the three optimization models.It gives full play to the advantages of each algorithm,makes up for the shortcomings of each algorithm,and greatly reduces the prediction error,increases the reliability of forecasting system.This study provides a theoretical reference for the formulation of energy-saving operation strategy of air conditioning.
作者
张晨晨
丛意林
田野
郭安柱
刘涛
马永志
ZHANG Chenchen;CONG Yilin;TIAN Ye;GUO Anzhu;LIU Tao;MA Yongzhi(College of Mechanical and Electrical Engineering,Qingdao University,Qingdao 266071,China)
出处
《青岛大学学报（工程技术版）》
CAS
2021年第4期107-114,共8页
Journal of Qingdao University(Engineering & Technology Edition)
