综合智慧能源2025，Vol.47Issue(3)：62-72,11.DOI:10.3969/j.issn.2097-0706.2025.03.006
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
摘要
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%.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分类
能源科技引用本文复制引用
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):62-72,11.基金项目
国家自然科学基金项目(52307072)National Natural Science Foundation of China(52307072) （52307072）
