NON-INTRUSIVE LOAD DISAGGREGATION METHODS FOR LOW-RATE SMART METER DATA

Authors

  • Divya M. Department of Electrical Engineering Fr. C. Rodrigues Institute of Technology, Navi Mumbai

DOI:

https://doi.org/10.17605/OSF.IO/YMHN9

Keywords:

Energy disaggregation, Non-Intrusive Load Monitoring (NILM), Smart meters, Data granularity

Abstract

Smart meter technology presents an opportunity to gain better insights into consumer appliance usage and consumption behaviour.  Load monitoring can provide valuable data on appliance specific energy consumption statistics which in turn will be useful for the consumer to evolve an optimum energy utilization strategy. In the utility point of view, the data acquired in this manner could be used to evolve better target demand side management programs, including demand response and energy efficiency. Non-intrusive load monitoring (NILM) is a consumer energy disaggregation technique that segregates individual appliance energy consumption from the total energy measured at the mains. Unlike intrusive load monitoring, it does not require separate meters to measure individual device consumption. This field has garnered lot of research interest recently, owing to emergence of smart grid technologies and advances in smart metering. Machine learning algorithms are predominantly used to solve NILM problems. In view of concerns regarding customer privacy and economics, low frequency smart meters are preferred. There are many challenges involved in using low granularity data for NILM algorithms. This work summarizes the current state of the art of NILM methods for low rate smart meter data. The limitations of the present methods and scope for future work are also presented.

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Published

10-11-2021

How to Cite

[1]
Divya M., “NON-INTRUSIVE LOAD DISAGGREGATION METHODS FOR LOW-RATE SMART METER DATA”, IEJRD - International Multidisciplinary Journal, vol. 6, no. ICRRTNB, p. 12, Nov. 2021.