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Marino, D.L., Amarasinghe, K. and Manic, M. (2016) Building Energy Load Forecasting Using Deep Neural Networks. Proceedings of the 42nd Annual Conference of the IEEE on Industrial Electronics Society (IECON), Florence, 23-26 October 2016, 7046-7051. https://doi.org/10.1109/IECON.2016.7793413

has been cited by the following article:

• TITLE: Real Time Power Monitoring Detection Based on Sequence Time Domain Reflectometry Approach

  AUTHORS: Sam Moayedi, Subhaditya Shom, Andrew Harms, Mahmoud Alahmad

  KEYWORDS: Wire Length Detection, Sequence Time Domain Reflectometry (STDR), Time Domain Reflectometry (TDR), Maximum Length Sequence, Coupling, Power Consumption Monitoring

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, December 29, 2017

  ABSTRACT: Sequence Time Domain Reflectometry (STDR) have been demonstrated to be a powerful technique for detecting the length of cable or length of open circuit or short circuit cables. Using this method along with using smart meter on the main electrical panel board to monitor consumption if load at each circuit, enable user to monitor power consumption at each node (power outlet) only by operating a smart digital meter and an STDR circuitry on each circuit at the main electrical panel board. This paper introduces this method and examines it on dead-wire and energized wire with a load connected across it. Experimental results are demonstrated for both types. Test result show the potential application of this approach to provide consumption information and potential cost saving via feedback for users.