基于MDLOF-iForest和M-KNN-Slope的公共建筑负荷异常数据识别与修复

doi:10.3969/j.issn.2097-0706.2025.03.006

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (3): 62-72.doi: 10.3969/j.issn.2097-0706.2025.03.006

• 基于AI的新型电力系统调度 • 上一篇下一篇

基于MDLOF-iForest和M-KNN-Slope的公共建筑负荷异常数据识别与修复

刘一宁¹(), 陈柏安¹, 杜鹏程¹, 林晓刚²(), 江美慧¹^,³^,^*()

1.广西大学电气工程学院，南宁 530004
2.中国科学院海西研究院泉州装备制造研究中心，福建泉州 362000
3.内蒙古工业大学新能源学院，内蒙古鄂尔多斯 017010

收稿日期:2024-12-31 修回日期:2025-02-11 接受日期:2025-03-05 出版日期:2025-03-25
通讯作者: *江美慧（1994），女，讲师，博士，从事综合能源、风光储一体化技术等方面的研究，meihuijiang@yeah.net。
作者简介:刘一宁（2001），男，硕士生，从事智慧能源系统方面的研究，2412392073@st.gxu.edu.cn；
林晓刚（1990），男，助教，博士，从事综合能源、自动控制等方面的研究，xg_lin_nuaa@126.com。
基金资助:
国家自然科学基金项目(52307072)

Detection and repair of abnormal load data of public buildings based on MDLOF-iForest and M-KNN-Slope

LIU Yining¹(), CHEN Baian¹, DU Pengcheng¹, LIN Xiaogang²(), JIANG Meihui¹^,³^,^*()

1. School of Electrical Engineering， Guangxi University， Nanning 530004， China
2. Quanzhou Institute of Equipment Manufacturing， Haixi Institute，Chinese Academy of Sciences， Quanzhou 362000， China
3. School of Renewable Energy，Inner Mongolia University of Technology， Ordos 017010， China

Received:2024-12-31 Revised:2025-02-11 Accepted:2025-03-05 Published:2025-03-25
Supported by:
National Natural Science Foundation of China(52307072)

摘要/Abstract

摘要：

在公共建筑能耗研究中，对异常负荷值进行识别与修复是不可或缺的数据处理环节。针对现有方法的局限性，提出一种基于马氏距离局部离群因子-孤立森林（MDLOF-iForest）算法和考虑斜率的K近邻改进（M-KNN-Slope）算法的负荷异常数据识别与修复方法。MDLOF-iForest算法在传统局部离群因子算法中引入马氏距离，提高了模型对数据特征间关联性的感知能力，同时将MDLOF算法与iForest算法的优势相结合，快速准确识别出异常数据。M-KNN-Slope算法利用异常数据与正常数据负荷趋势线特征相似的邻居，得到相似趋势线斜率加权平均值，完成对异常数据的修复，减少对样本数据的依赖。通过对南宁市一栋办公和一栋商业公共建筑2024年8—11月负荷数据的验证，修复后90%左右数据与正确数据差值在10%以内，且相较一般算法，M-KNN-Slope算法能够获得更多误差在5%以内的数据。分别利用极端梯度提升、长短期记忆网络、反向传播神经网络、支持向量机对修复前后的数据进行预测，均方根值分别降低了5.02%~17.83%，绝对平均误差分别降低了2.44%~13.34%。

关键词: 公共建筑能耗, 负荷数据集, 异常数据识别, 异常数据修复, 马氏距离局部离群因子-孤立森林算法, 考虑斜率的K近邻改进算法

Abstract:

In research on energy consumption of public buildings， making the detection and repair of abnormal load data an indispensable part of data processing. To address the limitations of existing methods， a method based on the Mahalanobis distance-based local outlier factor-isolation forest （MDLOF-iForest） algorithm and the modified K-nearest neighbors-slope （M-KNN-Slope） algorithm was proposed.The MDLOF-iForest algorithm incorporated Mahalanobis distance into the traditional local outlier factor algorithm， improving the models ability to perceive correlations between data features. Meanwhile， by combining the advantages of MDLOF algorithm and iForest algorithm， it enabled rapid and accurate detection of abnormal data. The M-KNN-Slope algorithm used neighbors with similar load trend line characteristics of abnormal data and normal data to obtain the weighted average values of similar trend line slopes， completing the repair of abnormal data and reducing reliance on sample data. Verification was conducted using load data from an office public building and a commercial public building in Nanning， from August to November 2024. The results showed that approximately 90% of the repaired data had a difference of less than 10% compared to the correct data. Compared with conventional algorithms，the M-KNN-Slope algorithm could obtain more data with errors within 5%. Extreme gradient boosting， long short-term memory network， backpropagation neural network， and support vector machine were used to predict the data before and after repair. The root mean square values decreased by 5.02% to 17.83%， and the absolute mean errors decreased by 2.44% to 13.34%.

Key words: energy consumption of public buildings, load dataset, abnormal data detection, abnormal data repair, Mahalanobis distance-based local outlier factor-isolation forest, modified K-nearest neighbors-slope

中图分类号:

TK01

刘一宁, 陈柏安, 杜鹏程, 林晓刚, 江美慧. 基于MDLOF-iForest和M-KNN-Slope的公共建筑负荷异常数据识别与修复[J]. 综合智慧能源, 2025, 47(3): 62-72.

LIU Yining, CHEN Baian, DU Pengcheng, LIN Xiaogang, JIANG Meihui. Detection and repair of abnormal load data of public buildings based on MDLOF-iForest and M-KNN-Slope[J]. Integrated Intelligent Energy, 2025, 47(3): 62-72.

图/表 13

表1

图1

图2

图3

图4

图5

表2

图6

图7

图8

图9

表3

表4

参考文献 27

[1]	中国建筑节能协会, 重庆大学城乡建设与发展研究院. 中国建筑能耗与碳排放研究报告(2023年)[J]. 建筑, 2024(2): 46-59.
[2]	孙健, 张云帆, 蔡潇龙, 等. 基于预测负荷的暖通空调系统优化调度[J]. 综合智慧能源, 2024, 46(3): 12-19. doi: 10.3969/j.issn.2097-0706.2024.03.002
	SUN Jian, ZHANG Yunfan, CAI Xiaolong, et al. Optimal scheduling of HVAC systems based on predicted loads[J]. Integrated Intelligent Energy, 2024, 46(3): 12-19. doi: 10.3969/j.issn.2097-0706.2024.03.002
[3]	樊颜搏, 熊亚选, 李想, 等. 基于遗传算法的建筑用能多目标优化应用进展[J]. 综合智慧能源, 2024, 46(9): 69-85. doi: 10.3969/j.issn.2097-0706.2024.09.009
	FAN Yanbo, XIONG Yaxuan, LI Xiang, et al. Advancement in multi-objective optimization for building energy use based on genetic algorithms[J]. Integrated Intelligent Energy, 2024, 46(9): 69-85. doi: 10.3969/j.issn.2097-0706.2024.09.009
[4]	JAMSHIDI E J, YUSUP Y, KAYODE J S, et al. Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature[J]. Ecological Informatics, 2022, 69: 101672.
[5]	顾国庆, 李晓辉. 基于箱线图异常检测的指数加权平滑预测模型[J]. 计算机与现代化, 2021(1): 28-33.
	GU Guoqing, LI Xiaohui. Exponential weighted smoothing prediction model based on abnormal detection of box-plot[J]. Computer and Modernization, 2021(1): 28-33.
[6]	ZHANG J F, ZHANG H, DING S, et al. Power consumption predicting and anomaly detection based on transformer and K-means[J]. Frontiers in Energy Research, 2021, 9: 779587.
[7]	JIN F, WU H, LIU Y, et al. Varying-scale HCA-DBSCAN-based anomaly detection method for multi-dimensional energy data in steel industry[J]. Information Sciences, 2023, 647: 119479.
[8]	LIU X, DING Y, TANG H, et al. A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data[J]. Energy and Buildings, 2021, 231: 110601.
[9]	王锐. 基于改进LOF的高维数据异常检测方法[J]. 电信工程技术与标准化, 2023, 36(3):41-45,62.
	WANG Rui. Anomaly detection for high-dimensional data based on improved LOF[J]. Telecom Engineering Technics and Standardization, 2003, 36(3):41-45,62.
[10]	LESOUPLE J, BAUDOIN C, SPIGAI M, et al. Generalized isolation forest for anomaly detection[J]. Pattern Recognition Letters, 2021, 149: 109-119.
[11]	WANG X L, AHN S H. Real-time prediction and anomaly detection of electrical load in a residential community[J]. Applied Energy, 2020, 259: 114145.
[12]	LI K K, TANG L P, YANG X M. Alleviating limit cycling in training GANs with an optimization technique[J]. Science China Mathematics, 2024, 67(6): 1287-1316.
[13]	陆旦宏, 范文尧, 杨婷, 等. 基于生成对抗Transformer的电力负荷数据异常检测[J]. 电力工程技术, 2024, 43(1): 157-164.
	LU Danhong, FAN Wenyao, YANG Ting, et al. Anomaly detection of power load data based on Transformer and generative adversarial networks[J]. Electric Power Engineering Technology, 2024, 43(1): 157-164.
[14]	曾静, 娄冰, 吕娜, 等. 基于多特征和LOF的用户负荷突变检测[J]. 浙江电力, 2023, 42(2):90-97.
	ZENG Jing, LOU Bing, LYU Na, et al. Abrupt user load change detection based on multiple features and LOF algorithm[J]. Zhejiang Electric Power, 2023, 42(2):90-97.
[15]	HIMEUR Y, ALSALEMI A, BENSAALI F, et al. Smart power consumption abnormality detection in buildings using micromoments and improved K-nearest neighbors[J]. International Journal of Intelligent Systems, 2021, 36(6): 2865-2894.
[16]	XU S Y, LIU H Y, DUAN L T, et al. An improved LOF outlier detection algorithm[C]//Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Computer Applications(ICAICA), IEEE, 2021: 113-117.
[17]	SOLÍS-VILLARREAL J A, SOTO-MENDOZA V, NAVARRO-ACOSTA J A, et al. Energy consumption outlier detection with AI models in modern cities: A case study from north-eastern Mexico[J]. Algorithms, 2024, 17(8): 322.
[18]	李沛智, 刘立群, 薄轶帅. 基于密度子空间孤立森林的异常用电检测[J]. 科学技术与工程, 2024, 24(10): 4115-4123.
	LI Peizhi, LIU Liqun, BO Yishuai. Anomalous electricity usage detection based on density subspace isolated forest[J]. Science Technology and Engineering, 2024, 24(10): 4115-4123.
[19]	肖碧涛, 刘宇, 赖晓路. 基于动态潜变量回归的风电机组叶片结冰监测研究[J]. 综合智慧能源, 2024, 46(12): 29-35. doi: 10.3969/j.issn.2097-0706.2024.12.004
	XIAO Bitao, LIU Yu, LAI Xiaolu. Wind turbine blades icing monitoring based on dynamic latent variable regression[J]. Integrated Intelligent Energy, 2024, 46(12): 29-35. doi: 10.3969/j.issn.2097-0706.2024.12.004
[20]	赵荣珍, 赵楠. 结合扰动集成RBF的故障识别方法[J]. 兰州理工大学学报, 2022, 48(6): 40-45.
	ZHAO Rongzhen, ZHAO Nan. Fault identification method combined with disturbance ensemble RBF[J]. Journal of Lanzhou University of Technology, 48(6): 40-45.
[21]	BA P F, ZHU S L, CHAI H Y, et al. Structural monitoring data repair based on a long short-term memory neural network[J]. Scientific Reports, 2024, 14: 9974. doi: 10.1038/s41598-024-60196-2 pmid: 38693161
[22]	林范龙, 林昀, 黄庆仕, 等. 智能电网中的异常数据修复模型研究[J]. 微型电脑应用, 2024, 40(9): 134-138.
	LIN Fanlong, LIN Yun, HUANG Qingshi, et al. Research on abnormal data repair model in smart grid[J]. Microcomputer Applications, 2024, 40(9): 134-138.
[23]	ZHANG S C. Challenges in KNN classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4663-4675.
[24]	HONG G, CHOI G S, EUM J Y, et al. The hourly energy consumption prediction by KNN for buildings in community buildings[J]. Buildings, 2022, 12(10): 1636.
[25]	YAN H Y, MA L D, ZHAO T Y, et al. Research on repair method of abnormal energy consumption data of lighting and plug based on similar features[J]. Energy and Buildings, 2022, 268: 112155.
[26]	刘立巍, 周建新, 刘培栋, 等. 基于KNN-LSTM的区域热负荷短期预测及在机组热电可行域的应用研究[J]. 热能动力工程, 2023, 38(3):91-97.
	LIU Liwei, ZHOU Jianxin, LIU Peidong, et al. Short-term regional heat load prediction using KNN-LSTM and its application in CHP feasible operation region[J]. Journal of Engineering for Thermal Energy and Power, 2023, 38(3):91-97.
[27]	许艺颖. 建筑能耗监测平台异常数据的辨识与修复[D]. 大连: 大连理工大学, 2019.
	XU Yiying. Identification and repair of abnormal data in energy consumption monitoring platform[D]. Dalian: Dalian University of Technology, 2019.

异常情况	具体表现形式
无数据	在某个时间段内没有任何能耗数据上传
“假”数据	数据维持长期固定不变，不符合客观规律
数据过小	数据反向或数值过小，不符合实际
数据过大	数据数值过大，不符合实际
与相似建筑数据偏差过大	与功能、规模、使用方式相似的其他建筑相比，能耗数据存在不合理差异
与历史数据偏差过大	与历史能耗数据相比，某一时段的能耗数据出现明显异常波动
不符合周期性规律	能耗数据通常具有一定的周期性，若能耗数据在周期性规律上出现明显违背的情况，则可能存在异常

评价标准	8月工作日	8月周末	10月工作日	10月周末
人工验证准确率/％	98.78	97.66	99.48	99.31
A_SS	2.535	2.805	1.938	2.067

模型	办公公共建筑		商业公共建筑
模型	RMSE/kW	MAE	RMSE/kW	MAE
XGBoost	97.277 9	66.267 5	97.346 7	67.144 7
LSTM	85.846 3	66.032 4	86.228 6	67.068 3
BP	177.869 3	126.422 0	175.533 1	128.534 1
SVM	126.991 6	95.372 1	129.211 0	95.538 3

模型	办公公共建筑		商业公共建筑
模型	RMSE/kW	MAE	RMSE/kW	MAE
XGBoost	92.398 2	61.398 2	92.453 7	65.506 9
LSTM	70.556 2	57.624 9	70.853 4	58.164 5
BP	152.249 9	109.552 4	152.357 6	112.438 4
SVM	114.463 2	89.463 2	116.452 2	90.233 8

基于MDLOF-iForest和M-KNN-Slope的公共建筑负荷异常数据识别与修复

Detection and repair of abnormal load data of public buildings based on MDLOF-iForest and M-KNN-Slope

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 27

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张璐, 陆安山, 华杨, 刘天皓, 张冬冬. 弹性电力系统态势感知研究综述[J]. 综合智慧能源, 2025, 47(3): 1-14.
[2]	邸亮, 董杰, 闫馨月, 甄成, 田喆, 牛纪德. 用于市场准入条件判定的建筑需求响应潜力量化方法[J]. 综合智慧能源, 2025, 47(3): 15-22.
[3]	杨丽洁, 邓振宇, 陈作双, 黄超, 江美慧, 朱虹谕. 基于MSCNN-BiGRU-MLP模型的公共建筑非侵入式负荷辨识[J]. 综合智慧能源, 2025, 47(3): 23-31.
[4]	张冬冬, 单琳珂, 刘天皓. 人工智能技术在风力与光伏发电数据挖掘及功率预测中的应用综述[J]. 综合智慧能源, 2025, 47(3): 32-46.
[5]	张冬冬, 李芳凝, 刘天皓. 新型电力系统负荷预测关键技术及多元场景应用[J]. 综合智慧能源, 2025, 47(3): 47-61.
[6]	江美慧, 许镇江, 牛统科, 朱虹谕, 李想. 考虑综合需求响应的矿区综合能源系统日内多时间尺度滚动优化调度[J]. 综合智慧能源, 2025, 47(3): 73-83.
[7]	崔再岳, 杨扬, 王立地. 考虑负荷需求响应的配电网三相不平衡优化[J]. 综合智慧能源, 2025, 47(3): 92-101.
[8]	杨建, 郝国捷, 吴昕悦, 郭明强, 陈充, 雷志敏. 计及EV碳排放的交能融合系统出行路径优化及经济运行策略[J]. 综合智慧能源, 2025, 47(2): 41-49.
[9]	刘斌, 孙周, 姜之未, 郭明阳, 曹灿. 数字化赋能交通能源融合：技术路径、应用场景与未来展望[J]. 综合智慧能源, 2025, 47(2): 1-12.
[10]	袁孝科, 沈石兰, 张茂松, 石晨旭, 杨凌霄. 基于可解释强化学习的智能虚拟电厂最优调度[J]. 综合智慧能源, 2025, 47(1): 1-9.
[11]	苏锐, 王希龙, 江阎, 宋晨辉. 基于改进成本分配法的虚拟电厂调度规划[J]. 综合智慧能源, 2025, 47(1): 10-17.
[12]	徐强. 基于NSGA-Ⅲ算法的分布式光伏储能系统优化配置方法[J]. 综合智慧能源, 2025, 47(1): 26-33.
[13]	江阎, 宋晨辉, 张宁, 贺波, 卢宇婷. 考虑多能耦合的虚拟电厂优化运行模型与策略研究[J]. 综合智慧能源, 2025, 47(1): 34-41.
[14]	曲琪, 滕菲, 郭禹辛, 张琳雪. 考虑算力需求的港口综合能源系统分布式能源管理[J]. 综合智慧能源, 2025, 47(1): 42-50.
[15]	胡嘉诚, 张宁, 曹雨桐, 胡存刚. 分布式能源接入下虚拟电厂负荷优化调度决策[J]. 综合智慧能源, 2025, 47(1): 62-69.