Data reduction in a cloud-based AMI framework with service-replication

Smart grids use cloud-based Advanced Metering Infrastructure (AMI) systems to ensure high performance in collecting, storing, and processing energy consumption data originating at the smart meters. However, this data is usually huge, so its transmission to the cloud requires stable, high-bandwidth Internet connections, which may be costly or even unavailable. A solution allowing transmission on normal connections is to reduce the data by approximating it at the cloud using forecasting. The problem is that AMI data changes pattern continuously and unexpectedly, due to the constant, uncoordinated addition and removal of loads. This makes predicting the data using a single forecasting method almost impossible. We circumvent this problem by designing an adaptive, multi-method, service-replicated Framework. At any time, the Framework uses a method that is optimal for the current data. Once the data changes pattern, the Framework automatically switches to another method that suits the new pattern. Experimental results show reduction rates up to 55% on real AMI data.