用预拓扑方法自动分类能源消耗模式，提升建筑能效管理效率
Hierarchical clustering of complex energy systems using pretopology
- 基于预拓扑空间设计多准则分层聚类算法
- 在真实能耗数据上实现高精度聚类，调整兰德指数达1
- 适合能源管理、智能电网领域研究人员使用
本文旨在解决如何对大规模分布式区域的建筑能耗曲线进行建模与分类，以优化能效管理。逐栋深度审计数千栋建筑耗时耗力且成本高昂，亟需自动化方法构建有效建议系统。为此，本文采用预拓扑建模能耗曲线，并开发了基于预拓扑空间性质的多准则分层聚类算法，集成于Python库中。评估使用三组数据：二维空间中不同大小点集、生成时间序列、来自法国能源公司400个真实能耗站点的时间序列。在点数据集上，算法可结合空间位置与尺寸参数识别簇；在生成时间序列上，基于皮尔逊相关性聚类，调整兰德指数（ARI）达1，表明聚类效果完美。
原文摘要 · Abstract (English)
This article attempts answering the following problematic: How to model and classify energy consumption profiles over a large distributed territory to optimize the management of buildings' consumption? Doing case-by-case in depth auditing of thousands of buildings would require a massive amount of time and money as well as a significant number of qualified people. Thus, an automated method must be developed to establish a relevant and effective recommendations system. To answer this problematic, pretopology is used to model the sites' consumption profiles and a multi-criterion hierarchical classification algorithm, using the properties of pretopological space, has been developed in a Python library. To evaluate the results, three data sets are used: A generated set of dots of various sizes in a 2D space, a generated set of time series and a set of consumption time series of 400 real consumption sites from a French Energy company. On the point data set, the algorithm is able to identify the clusters of points using their position in space and their size as parameter. On the generated time series, the algorithm is able to identify the time series clusters using Pearson's correlation with an Adjusted Rand Index (ARI) of 1.
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