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.