Time-of-Use Period Partition Based on Improved Fuzzy C-Means and Abnormal Period Correction

In time-of-use tariff period partition, clustering algorithms are commonly used. However, as load demands become more diverse in this big data era, large amount of non-linear data makes conventional clustering algorithms methods no longer be applicable in this field alone. Facing high-time-resolution daily load data with strong non-linearity, we propose a new method to partition periods. It consists of an improved fuzzy c-means clustering algorithm and a correction method for abnormal periods. Firstly, we propose modified fuzzy membership functions to improve the initialization of clustering for operation efficiency. Secondly, the method for calculating the fuzzy parameters based on the loss function is given. Thirdly, the initial period partition is obtained by the improved clustering. Next, the recognition model and fuzzy subsethood-based correction model for abnormal periods are structed, then the corrected period partition is confirmed. Finally, the effectiveness of the proposed methods is verified by two daily load data with a time resolution of 5 minutes.