发表机构
Faculty of Mechanical Engineering; University of Niš（机械工程学院; 尼什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对区域供热系统数据，测试多种多元异常值检测方法，结合领域情况确定PCA、Isolation Forest等方法有效，采用三者一致的集成方法，助力减少供热能耗与碳排放。
AI 中文摘要
本文测试了多种用于区域供热系统选定子站传输热能数据的多元异常值检测方法，同时考虑了外部环境温度，包括Z-score（单变量，作为基准）、马氏距离、主成分分析（PCA）、孤立森林（Isolation Forest）和Hotelling T平方检验。本研究的总体目标是发现设备的不规则运行，更广泛的目标是确定减少集中供热厂天然气消耗及二氧化碳排放的机会。所提出的方法考虑了特定领域情况，例如传输能量为零的时间点与设备脱网状态无关。不同方法的结果与领域专家进行了讨论，结论是PCA、Isolation Forest和Hotelling方法提供了相关结果。最后，采用集成方法（基于这三种方法对检测到的异常值达成一致的选择）作为最终方法。
英文摘要
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.
Comments10 pages, 4 figures. This preprint corresponds to the paper published in Lecture Notes in Networks and Systems, vol. 860 (ICIST 2024), Springer
Journal refLecture Notes in Networks and Systems, Vol. 860 (ICIST 2024), Springer, 2024
DOI：10.1007/978-3-031-71419-1_5
