An adaptive framework for real-time data reduction in AMI

In existing Advanced Metering Infrastructure (AMI), data collection intervals for each smart meter (SM) typically vary from 15 to 60 min. If we have 1 million SMs that transmit data every 15 min, these SMs will export 4 million records per hour. This leads to dramatically increasing bandwidth usage, energy consumption, traffic cost and I/O congestion. In this work, we present an adaptive framework for minimizing the amount of data transfer from SMs. The reduction in the framework is forecasting-based; when an SM reading is close to the forecasted value, the SM does not transmit the reading. In order for the framework to be adaptive to the ever-changing pattern of SM data, it is provided with a pool of forecasting methods. A supervised-learning scheme is employed to switch in real-time to the forecasting method most suitable to the current data pattern. The experimental results demonstrate that the proposed framework achieves data reduction rates up to 98% with accuracy 96%, depending on the operational parameters of the framework and consumer behavior (statistical features of SM data).