The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential electricity demand remains limited. In this research, we propose and test a new framework for understanding residential electricity demand by using a dynamic energy lifestyles approach that is iterative and highly extensible. To obtain energy lifestyles, we develop a novel approach that applies Latent Dirichlet Allocation (LDA), a method commonly used for inferring the latent topical structure of text data, to extract a series of latent household energy attributes. By doing so, we provide a new perspective on household electricity consumption where each household is characterized by a mixture of energy attributes that form the building blocks for identifying a sparse collection of energy lifestyles. We examine this approach by running experiments on one year of hourly smart meter data from 60,000 households and we extract six energy attributes that describe general daily use patterns. We then use clustering techniques to derive six distinct energy lifestyle profiles from energy attribute proportions. Our lifestyle approach is also flexible to varying time interval lengths, and we test our lifestyle approach seasonally (Autumn, Winter, Spring, and Summer) to track energy lifestyle dynamics within and across households and find that around 73% of households manifest multiple lifestyles across a year. These energy lifestyles are then compared to different energy use characteristics, and we discuss their practical applications for demand response program design and lifestyle change analysis.
翻译:先进气象基础设施(AMI)的迅速扩展极大地改变了能源信息格局。然而,我们利用这一信息对住宅电力需求产生可操作的洞察力的能力仍然有限。在这项研究中,我们提出并测试一个通过反复和高度推广的动态能源生活方式方法来理解住宅电力需求的新框架。为了获得能源生活方式,我们开发了一种新颖的方法,应用了“冷淡地地流”分配(LDA),这是一种常用的方法,用于推断文本数据的潜在热点结构,以提取一系列潜在的家庭能源属性。我们这样做,我们从新的角度看待家庭电力消费,其中每个家庭都有能源属性的混合,构成确定能源生活方式收集稀缺的构件。我们通过对来自60 000户的每小时智能计量数据进行为期一年的实验来研究这一方法,我们从中提取了描述一般日常使用模式的六种能源属性。我们随后使用集群技术从能源属性比例中得出六种不同的能源生活方式简介。我们的生活方式方法也灵活到不同的时间间隔长度。我们以此来测试我们的生活方式方法,即每个住户的能源特征组合,从而对73户内和多种能源需求进行一种不同的生活方式分析。