Publication Type:

Journal Article


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 466, p.165-173 (2016)





Advanced metering infrastructures, Artificial intelligence, Automated metering infrastructure (AMI), Automated metering infrastructures, Automatic metering infrastructures, Automation, Computation theory, Computer control systems, Electric power plant loads, Electric power system economics, Electric power transmission networks, Energy utilization, False data injection attacks, Forecasting, Intelligent systems, Learning systems, Load forecasting, Machine learning approaches, Smart grid, Smart power grids, Soft computing, Transmission and distribution


<p>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. © Springer International Publishing Switzerland 2016.</p>


cited By 0; Conference of 5th Computer Science On-line Conference, CSOC 2016 ; Conference Date: 27 April 2016 Through 30 April 2016; Conference Code:174079

Cite this Research Publication

J. A. Balaji, Harish, S., and B.B. Nair, “Modeling of consumption data for forecasting in automated metering infrastructure (AMI) systems”, Advances in Intelligent Systems and Computing, vol. 466, pp. 165-173, 2016.