Hosseini, S.S.; Delcroix, B.; Henao, N.; Agbossou, K.; Kelouwani, S. Towards Feasible Solutions for Load Monitoring in Quebec Residences. Sensors2023, 23, 7288.
Hosseini, S.S.; Delcroix, B.; Henao, N.; Agbossou, K.; Kelouwani, S. Towards Feasible Solutions for Load Monitoring in Quebec Residences. Sensors 2023, 23, 7288.
Hosseini, S.S.; Delcroix, B.; Henao, N.; Agbossou, K.; Kelouwani, S. Towards Feasible Solutions for Load Monitoring in Quebec Residences. Sensors2023, 23, 7288.
Hosseini, S.S.; Delcroix, B.; Henao, N.; Agbossou, K.; Kelouwani, S. Towards Feasible Solutions for Load Monitoring in Quebec Residences. Sensors 2023, 23, 7288.
Abstract
For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Nevertheless, a practical application of this concept in the residential sector should address the underlying concerns raised by the technical specifications of case studies. From one side, such an operation must handle common matters related to the essence of any NILM system. Although this aspect has been thoroughly investigated by basic research, it is limited to the properties of public datasets. On the other side, it must deal with specific concerns corresponding to uncommon situations. These circumstances impose further restrictions on existent NILM schemes, however, they have been overlooked due to the lack of pertinent databases to scrutinize. Accordingly, this paper presents applied research on a potential solution to NILM for Quebec residences. It carries out a relevant investigation into the multi-faceted nature of this problem in order to reveal barriers to feasible implementations in the context of Quebec. This work commences with a concise discussion about the NILM idea to highlight its essential requirements for a fruitful practice. Afterward, it provides a comparative statistical analysis to represent the specificity and potential challenges of the case study in accordance with NILM necessities. For this purpose, the examination exploits data from real-world measurement systems in the same and European regions. Subsequently, this study focuses on a load identification exercise by proposing a combinatory approach that utilizes the promise of sub-meter smart technologies to integrate the intrusive aspect of load monitoring with the non-intrusive one. The former is aimed at extracting overall heating demand from the aggregate one by a supervised procedure based on Deep Learning (DL) models while the latter is designed for disaggregating the residual load through an unsupervised process on the basis of clustering techniques. The results demonstrate that geographically-dependent cases create electricity consumption scenarios under which existing NILM methods can be questioned. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly, in Quebec houses.
Keywords
Non-Intrusive Load Monitoring (NILM); low-sampling load disaggregation; statistical analysis; machine learning algorithms; electric baseboards; electric water heaters
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
