Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding

This paper proposes a novel model to cluster similar load consumption patterns and identify time periods with similar consumption levels. The model represents the customer's load pattern as an image and takes into account the load variation and uncertainty by using exponential intuitionistic fuzzy entropy. The advantage is that the proposed method can handle the uncertain nature of customer's load, by adding a hesitation index to the membership and non-membership functions. A multi-level representation of the load patterns is then provided by creating specific bands for the load pattern amplitudes using intuitionistic fuzzy divergence-based thresholding. The typical load pattern is then determined for each customer. In order to reduce the number of features to represent each load pattern with respect to the time-domain data, the discrete wavelet transform is used to extract some spectral features. To cope with the data representation with fuzzy rules, the fuzzy c-means is implemented as the clustering algorithm. The proposed approach also identifies the time periods associated to different load pattern levels, providing useful hints for demand side management policies. The proposed method has been tested on ninety low voltage distribution grid customers, and its superior effectiveness with respect to the classical k-means algorithm has been represented by showing the better values obtained for a set of clustering validity indicators. The combination of load pattern clusters and time periods associated with the segmented load pattern amplitudes provides exploitable information for the efficient design and implementation of innovative energy services such as demand response for different customer categories.