Analyzing Smart Meter Data using a Two-stage Competitive Learning Method

Smart energy management is a major area of interest to meet the rising energy demand for which several countries are deploying smart meters. Presently, there is a need to better visualize the high-volume of data captured by smart meters to provide a means to effectively gather various analytical insights which can help in better understanding the energy usage patterns. This article presents a cascade application of two competitive learning algorithms - Self-organizing Map (SOM) and K-means clustering, to discover knowledge from smart meter data. A SOM is applied to construct a 2-D topologically preserving map which is useful in understanding and visualizing the consumer load profiles. Then K-means is applied on the codebook vectors of the SOM to determine the clusters containing consumers with similar energy consumption patterns. The identified consumer clusters enable the utility firms in preparing segment-specific tariffs to efficiently shape the future energy usage patterns.