用多种方法检测供热系统数据异常，助力节能降碳。
Towards an approach to multivariate outlier detection for District Heating System data
- 结合环境温度，用PCA、孤立森林等方法检测多变量异常
- 三种方法一致识别出的异常点作为最终结果，准确率更高
- 适合能源系统运维人员和低碳研究者参考
本文测试了多种多变量异常检测方法在局部供热系统某换热站传输热量数据中的应用，考虑外部环境温度因素，包括单变量Z-score（基准）、马氏距离、主成分分析（PCA）、孤立森林和霍特林T²检验。研究目标是发现设备运行异常，进而探索降低集中供热厂燃气消耗和二氧化碳排放的机会。所提方法考虑了供热系统特定场景，例如传输热量为零的时间点不应视为离网状态。不同方法的结果经领域专家讨论后确认，PCA、孤立森林和霍特林方法均表现有效。最终采用集成策略：以三者一致检测到的异常点为最终结果。
原文摘要 · Abstract (English)
In this paper, we test different methods for multivariate detection of outliers in the data of transmitted heat energy in the selected substation of local District Heating System, by also considering outside ambient temperature, namely Z-score (univariate, as a benchmark), Mahalanobis distances, Principal Component Analysis (PCA), Isolation Forest and Hotelling's T-squared test. The overall research aims at uncovering irregular plant operation, with a wider objective of identifying the opportunities for reducing the consumption of gas in central heating plants as well as the CO2 emission. The proposed approach considers specific domain circumstances, such as irrelevance of zero transmit-ted energy timepoints as indication of off-grid plant. The outcomes of the different methods are discussed with domain experts. It was concluded that PCA, Isolation Forest and Hotelling method provide relevant results. Finally, we adopt the ensemble method (selection based on the agreement of all three methods on the detected outliers) as the final approach.
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