A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Marco Fagiani, Roberto Bonfigli, Emanuele Principi, Stefano Squartini and Luigi Mandolini
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Marco Fagiani: Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy
Roberto Bonfigli: Research and Development Area, MAC Srl, Via XX Settembre 23, 62019 Recanati (MC), Italy
Emanuele Principi: Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy
Stefano Squartini: Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy
Luigi Mandolini: Research and Development Area, MAC Srl, Via XX Settembre 23, 62019 Recanati (MC), Italy

Energies, 2019, vol. 12, issue 7, 1-26

Abstract: Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of F 1 -score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time.

Keywords: non-intrusive load monitoring; energy disaggregation; deep learning; neural networks; non-uniform sampling; active power; computational energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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