Novel Electricity Pattern Identification System Based On Improved I-Nice Algorithm

As a result of the rapid increase in smart-metre popularity, a large volume of smart electricity data has been generated. The underlying information contained within such data sets is very helpful and useful to businesses and power companies. However, effective mining of this valuable knowledge is challenging for both industry and academia. Current research mainly focuses on application of statistical analyses to available smart electricity data rather than construction of an advanced learning system. This paper reports development of an intelligent system to identify electricity patterns within industrial electricity data. The core component of this novel identification system is the I-nice-a clustering algorithm, which is a variant of the I-nice algorithm. I-nice-a utilises kernel density estimation technology and the minimum mean discrepancy to optimise the cluster number and determine the cluster centres, respectively, of the target data. Our theoretical analysis proves that I-nice-a has lower computational complexity than I-nice. We also compare I-nice-a with I-nice and four other standard clustering algorithms (i.e., the Gaussian mixture model (GMM), density-based spatial clustering of applications with noise (DBSCAN), the fuzzy c-means (FCM) algorithm, and balanced iterative reducing and clustering using hierarchies (BIRCH)), based on 15 benchmark data sets and electricity consumption data. The experiment results show that I-nice-a achieves superior clustering performance; thus, the feasibility and effectiveness of the improved algorithm are demonstrated. In addition, hierarchical (i.e., daily and annual) electricity patterns are determined by analysing industrial electricity data with I-nice-a; this result elucidates the production organism and power assignment. Thus, the proposed electricity pattern identification system has considerable potential application among power companies and businesses.