Modeling of consumption data for forecasting in automated metering infrastructure (AMI) systems

Abstract

The Smart Grid is a new paradigm that aims at improving the efficiency, reliability and economy of the power grid by integrating ICT infrastructure into the legacy grid networks at the generation, transmission and distribution levels. Automatic Metering Infrastructure (AMI) systems comprise the entire gamut of resources from smart meters to heterogeneous communication networks that facilitate two-way dissemination of energy consumption information and commands between the utilities and consumers. AMI is integral to the implementation of smart grid distribution services such as Demand Response (DR) and Distribution Automation (DA). The reliability of these services is heavily dependent on the integrity of the AMI data. This paper investigates the modeling of AMI data using machine learning approaches with the objective of load forecasting of individual consumers. The model can also be extended for detection of anomalies in consumption patterns introduced by false data injection attacks, electrical events and unauthorized load additions or usage modes.