Abstract：

Public buildings have many energy-using equipment, large construction areas, and a large number of users, which have great energy-saving potential. However, due to the problems of the abnormal data itemization caused by limited construction costs and the data loss and mutation caused by sensor or collector failures, the power consumption data obtained by its supporting building energy consumption monitoring platform often have anomalies. Based on the clustering algorithm, this paper proposes an abnormal data repair system composed of KNN-Matrix algorithm and KNN-Slope algorithm. Based on the current power consumption trend line of the abnormal data, the KNN-Slope algorithm looks for the recent historical power consumption data that are consistent with power consumption trend, and uses the weighted calculated power consumption value as the interpolated value to repair the abnormal data. The KNN-Matrix algorithm introduces a quantitative grade of electricity intensity characterized in matrix form, and looks for the recent historical data or average historical data that are consistent with the power consumption trend as an interpolated value for abnormal data repair. The results show that when facing different data anomalies and different public building types, the above repair system can make 99% of the abnormal data have a relative error of less than 30% with the real data after repair, and the maximum and average values of the relative errors are greatly reduced.

Keywords:publicbuilding;energyconsumptionmonitoring;datamining;proximityalgorithm;quantitativegrade;datarepair