Towards Feasible Solutions for Load Monitoring in Quebec Residences

Abstract

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

“…Year Place Method Used for NILM Dataset Avg. Efficiency (%) [15] China Particle swarm 1 year 94.2 [16] Indonesia Random Forests 1 year 99 [17] India Markov Chain 31 days 94 [5] Estonia Extreme Gradient Boost (XgBoost) 3 years 97.2 [18] Malaysia K-NN, SVM, Ensemble 30 days 98.8 [19] Iran SVM 1 week 98.2 [20] Indonesia Convolutional Neural Networks (CNN) 1 month 98 [21] Italy Random Forests 27 months 96.3 [22] Spain Long Short-Term Memory Networks (LSTM) 7 months 98 [23] Greece Recurrent Neural network (RNN) 10 days 97 [24] Canada LSTM 2 days 98 [ The NILM method has been used for anomaly detection at the appliance level by incorporating machine learning [26]. In another study [8], NILM is utilized for the event matching of devices.…”

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

“…An efficient electric power management system is dependent on its electric load monitoring module [5][6][7], which can be realized by intrusive or non-intrusive approaches. Compared with Intrusive Load Monitoring (ILM), Non-Intrusive Load Monitoring (NILM) has more advantages (see Section II for details).…”