Abstract:
Due to increased civilization, smart cities and advent of technology, lots of buildings including
commercials, residentials and other types are populating in numbers in the recent past. The
electricity consumption is also affecting due to increased occupancy in these buildings. To
analyse the electricity consumption patterns technology is utmost useful. This analysis will be
useful for consumers and electricity generation units too to know about consumption and future
requirements of electricity. Incremental clustering algorithm is the best choice to handle ever
increasing data. In this research work, in the first phase the electricity consumption data was
extracted from smart meter images and then in the second phase the data was taken from
extracted .csv files merging data from various sources together. This research proposes
Distributed Incremental Clustering with Closeness Factor Based Algorithm (DIC2FBA), to
update load patterns without overall daily load curve clustering. The proposed DIC2FBA has
used Amazon Web Service(AWS) and Microsoft Azure HDInsight service. The AWS EC2
instance, AWS S3 bucket, and HdInsight, which clusters data from multiple sites in iterative
and incremental mode. The DIC2FBA first extracts load patterns from new data and then
intergrades the existed load patterns with the new ones. Further, we have compared the findings
achieved using the DIC2FBA with IK means based on time, features, silhouette score, and
Davis Bouldin index which indicate that our method can provide an efficient response for
electricity consumption patterns analysis to end consumers via smart meters.
