Non-intrusive load monitoring under residential solar power influx

Dinesh, Chinthaka, Welikala, Shirantha, Liyanage, Yasitha, Ekanayake, Mervyn Parakrama B., Godaliyadda, Roshan Indika and Ekanayake, Janaka ORCID: https://orcid.org/0000-0003-0362-3767 2017. Non-intrusive load monitoring under residential solar power influx. Applied Energy 205 , pp. 1068-1080. 10.1016/j.apenergy.2017.08.094

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Abstract

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	19 October 2017
Date of Acceptance:	11 August 2017
Last Modified:	07 Nov 2023 17:55
URI:	https://orca.cardiff.ac.uk/id/eprint/105506

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