Abstract
A pattern classification method for power load analysis is proposed based on threshold and cloud improved fuzzy clustering algorithm (for short, TACIFCA). Firstly, the classic FCM clustering algorithm were improved by introducing a threshold to recognize the in-homogeneous datum and atypical homogeneous datum of each cluster and reduce their affects on the forming of cluster center. Then, cloud description of each cluster is given and the weights of each homogeneous data in same cluster were determined by the correlation coefficient which indicates the typical degree of the sample data for the cluster. The experimental result shows that the new method has better performance than traditional fuzzy c-means clustering.
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Acknowledgement
This paper is supported by the Fundamental Research Funds for the Central Universities (2015MS51) and the National Natural Science Foundation of China (Grant No. 71671064). The author also thanks the faculty of mathematical department in Middle Tenseness State University and Professor Wu for their kindly help during my visiting period.
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Gu, Yd., Cheng, Hc., Zhang, S. (2019). Power Load Pattern Classification Based on Threshold and Cloud Improved Fuzzy Clustering. In: Cao, BY., Zhong, YB. (eds) Fuzzy Sets and Operations Research. ICFIE 2017. Advances in Intelligent Systems and Computing, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-02777-3_13
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DOI: https://doi.org/10.1007/978-3-030-02777-3_13
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