Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.
Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems
