典型文献
Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks
文献摘要：
Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and sta-bility analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heat-ing,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.
文献关键词：
中图分类号：
作者姓名：
Wenlong Liao;Yusen Wang;Yuelong Wang;Kody Powell;Qi Liu;Zhe Yang
作者机构：
Department of Energy Technology,Aalborg University,Aalborg 9220,Denmark;School of Electrical Engineering and Computer Science,KTH Royal Institute of Technology,Stockholm,SE-100 44,Sweden;State Grid Tianjin Chengxi Electric Power Supply Branch,Tianjin 300100,China;Department of Chemical Engineering,University of Utah,UT 84112,America;Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China
文献出处：
引用格式：
[1]Wenlong Liao;Yusen Wang;Yuelong Wang;Kody Powell;Qi Liu;Zhe Yang-.Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(06):1730-1740
A类：
GMMNs,GMMN,ramps
B类：
Scenario,Generation,Cooling,Heating,Power,Loads,Using,Generative,Moment,Matching,Networks,generations,cooling,heating,power,loads,are,great,significance,economic,operation,sta,analysis,integrated,energy,systems,In,this,paper,novel,deep,generative,proposed,curves,moment,matching,networks,where,auto,encoder,transforms,high,dimensional,into,low,latent,variables,maximum,mean,discrepancy,represents,similarity,metrics,between,generated,samples,real,After,training,new,scenarios,by,feeding,Gaussian,noises,generator,Unlike,explicit,density,models,does,not,need,artificially,assume,probability,distribution,which,leads,stronger,universality,simulation,results,show,that,only,fits,multi,class,very,well,also,accurately,captures,shape,large,peaks,fast,fluctuation,frequency,domain,characteristics,temporal,spatial,correlations,Furthermore,consumption,closely,resembles
AB值：
0.62391
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