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首页> 外文期刊>Applied Energy >Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering
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Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

机译：使用基于高斯混合模型的聚类和分层聚类来识别典型的建筑物日常用电配置文件

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摘要

This paper presents a clustering-based strategy to identify typical daily electricity usage (TDEU) profiles of multiple buildings. Different from the majority of existing clustering strategies, the proposed strategy consists of two levels of clustering, i.e. intra-building clustering and inter-building clustering. The intra-building clustering used a Gaussian mixture model-based clustering to identify the TDEU profiles of each individual building. The inter-building clustering used an agglomerative hierarchical clustering to identify the TDEU profiles of multiple buildings based on the TDEU profiles identified for each individual building through intra-building clustering. The performance of this strategy was evaluated using two-year hourly electricity consumption data collected from 40 university buildings. The results showed that this strategy can discover useful information related to building electricity usage, including typical patterns of daily electricity usage (DEU) and periodical variation of DEU. It was also shown that this proposed strategy can identify additional electricity usage patterns with a less computational cost, in comparison to two single-step clustering strategies including a Partitioning Around Medoids-based clustering strategy and a hierarchical clustering strategy. The results obtained from this study could be potentially used to assist in improving energy performance of university buildings and other types of buildings.
机译：本文提出了一种基于聚类的策略，以识别多个建筑物的典型每日用电量（TDEU）配置文件。与大多数现有的聚类策略不同，提议的策略包括两个级别的聚类，即建筑物内聚类和建筑物间聚类。建筑物内聚类使用基于高斯混合模型的聚类来识别每个建筑物的TDEU配置文件。建筑物间聚类使用聚集层次聚类，以通过建筑物内聚类为每个单独建筑物识别的TDEU轮廓来识别多个建筑物的TDEU轮廓。使用从40所大学建筑中收集的两年小时用电量数据评估了该策略的性能。结果表明，该策略可以发现与建筑物用电量有关的有用信息，包括典型的每日用电量（DEU）模式和DEU的周期性变化。还显示出，与两个单步聚类策略（包括基于Medoids的基于分区的聚类策略和分层聚类策略）相比，该提议的策略可以用较少的计算成本来识别其他用电模式。这项研究获得的结果可潜在地用于帮助改善大学建筑和其他类型建筑物的能源性能。

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