Load Pattern Shape Clustering Analysis for Manufacturing

摘要

Manufacturing is dominant sector in electricity power consumption users. Currently an AMI infrastructure is widely deployed to collect real-time customer electricity consumption data. The knowledge of customer electricity consumption profiling can be used to smart grid dispatching, marketing and pricing based on analyzing and mining the load profiling in different industries, seasons and time periods. This paper investigates an auto fuzzy K-means clustering algorithm for load pattern shape data, which can find out the optimal number of power behaviors pattern. A data-preprocessing framework for load pattern shape retrieval is shown to reduce the dimensions efficiently. In the other hand, a validation index is applied in the algorithm, which balances the scattering within the clusters and the separation between the clusters, to discover the real electricity consumption pattern automatically based on the density of load time series data. The experimental results show this algorithm can efficiently discover the electricity consumption behavior, such as order, continual load and continual low load profiling, and different over time working pattern in weekend and public holiday. The results can predict the electricity consumption behavior for different type of industry, which will benefit for Demand Side Management smart grid dispatching and marketing.