建筑用电时间序列（building electricity consumption time series, BECTS）的季节性分割对于准确的电力负荷预测与模式挖掘意义重大。针对传统定时分割、定温分割和自适应候温分割方法难以实现准确的BECTS季节性分割问题，提出了一种基于Toeplitz逆协方差聚类（Toeplitz inverse covariance-based clustering, TICC）的BECTS自适应季节性分割方法。该方法基于建筑逐时用电负荷与室外干球温度二元时间序列，利用TICC算法进行实时分割与聚类。夏热冬暖地区某大型公共建筑真实用电数据的分析结果表明，该方法增强了同类样本之间的相似性和异类样本之间的差异性，与定时分割、定温分割和自适应候温分割方法相比，TICC分割后各季节的平均动态时间规整（dynamic time warping, DTW）距离分别提高46.54%、35.73%和7.59%。该方法可作为数据预处理，为单体建筑数据挖掘分析如建筑用电模式挖掘和负荷预测提供数据支撑。

Seasonal segmentation of building electricity consumption time series (BECTS) is of great significance for accurate load forecasting and pattern mining. Aiming at the problem that accurate BECTS seasonal segmentation is difficult to be realized by traditional timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, a new adaptive seasonal segmentation method for BECTS based on Toeplitz inversed covariance-based clustering (TICC) was proposed. The method was based on the binary time series of building hourly electricity load and outdoor dry bulb temperature, and the TICC algorithm was used for real-time segmentation and clustering. A large public building electricity load case in a hot summer and warm winter area was analyzed, and the result showed that the similarity between samples of the same type and the difference between samples of different types were enhanced by the method. Compared with the timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, the average dynamic time warping (DTW) distance of each category after TICC segmentation was improved respectively by 46.54%, 35.73% and 7.59%. This method can be used as data preprocessing to provide data support for single building data mining analysis, such as building electricity consumption pattern mining and load forecasting.