Authors: Qi Liu; Min Lu; Xiaodong Liu; Nigel Linge
Addresses: Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China; School of Computing, Edinburgh Napier University, Edinburgh, Scotland, UK ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK ' The University of Salford, Salford, Greater Manchester, M5 4WT, UK
Abstract: With the increasing of energy demand and electricity price, researchers show more and more interest in residential load monitoring. In order to feed back the individual appliance's energy consumption instead of the whole-house energy consumption, non-intrusive load monitoring (NILM) is a good choice for residents to respond to the time-of-use price and achieve electricity saving. In this paper, we discuss the system framework of NILM and analyse the challenges in every module. Besides, we study and compare the public datasets and accuracy metrics of non-intrusive load monitoring techniques.
Keywords: non-intrusive load monitoring; data acquisition; event detection; feature extraction; load disaggregation.
International Journal of High Performance Computing and Networking, 2019 Vol.14 No.1, pp.102 - 111
Received: 08 Dec 2016
Accepted: 03 Feb 2017
Published online: 08 May 2019 *