Stochastic modelling techniques for generating synthetic energy demand profiles
Stochastic modelling techniques for generating synthetic energy demand profiles
复制标题
用于生成综合能源需求概况的随机建模技术
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Simpson
中科院分区：
文献类型：
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作者：
S. Patidar;D. Jenkins;S. Simpson
本文研究了以一分钟分辨率生成N（用户指定）合成年电力需求曲线的三种随机建模程序。本文回顾了利用隐马尔可夫综合480种不同隐马尔可夫的复杂框架，对国内电力需求的高度随机时间序列进行综合的研究工作。将时间序列反季节性技术与单个HMM集成的方法的效率与时间序列反季节性ARIMA模型的兼容随机建模框架并行研究。对所有三种随机建模程序的真实和合成剖面的各种统计测量/特征进行了比较，以确定在精细时间分辨率下产生合成电力时间序列的最有效和实际适用的介质。结果既适用于单个建筑物，也适用于许多建筑物的综合（汇总）剖面。
This paper investigates three stochastic modelling procedures for generating N (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of HMM for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.
综合英国住宅的电力需求概况
影响因子：
6.7
作者：
Jenkins D
通讯作者：
Jenkins D
影响因子：
5.6
作者：
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D