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Multiscale Markov models with random transitions for energy demand management

机译:具有随机转换的多尺度马尔可夫模型用于能源需求管理

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摘要

A stochastic model is proposed for fluctuations in electricity demand that are associated with individual user's consumption choices. Electricity consumption is modeled as a function of social activities of consumers. The dynamics of these activities are modeled as a Markov chain. Markov models are simplified models that capture the stochasticity to the unmodeled dynamics typically attributed to white noise disturbances. Additional uncertainties are also accrued in the process of calibrating the transition rates of these chains from finite samples. In this paper, these uncertainties are accounted for by considering random transition matrices. Such formalism can also reflect the fluctuations in the environment in which the chain evolves. We also discuss a third interpretation where uncertain transitions, in a multiscale setting, reflect the fine-resolution information that is lost in the process of state aggregation. As numerical demonstration, we study the activity modeling of a heterogeneous population. Activity uncertainties are propagated onto the energy demand. Demand uncertainties, in turn, are propagated onto a global performance metric. Such Uncertainty management framework bridges between the actual drivers of the energy consumption and the system health. Subsequent decisions can be robustly supported based on the results of the quantitative model proposed in this paper.
机译:针对与个人用户的消费选择相关的用电需求波动,提出了一种随机模型。电力消耗被建模为消费者的社会活动的函数。这些活动的动力学建模为马尔可夫链。马尔可夫模型是简化的模型,可以捕获通常归因于白噪声干扰的未建模动态的随机性。在校准来自有限样本的这些链的跃迁速率的过程中,还会产生其他不确定性。在本文中,这些不确定性是通过考虑随机跃迁矩阵来解决的。这种形式主义也可以反映出链条发展环境的波动。我们还讨论了第三种解释,其中在多尺度设置中不确定的过渡反映了状态聚集过程中丢失的高分辨率信息。作为数值演示,我们研究了异质种群的活动模型。活动的不确定性会传播到能源需求上。反过来,需求不确定性会传播到全球绩效指标上。这种不确定性管理框架在能耗的实际驱动因素与系统健康之间架起了桥梁。基于本文提出的定量模型的结果,可以强有力地支持后续决策。

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