Without an appropriate monitoring system, the condition/state of electrical appliances/devices in operation in households cannot be fully assessed, resulting in uncontrolled expenses. The purpose of load monitoring techniques is to save electricity consumption. With proper controls, overconsumption of energy can be reduced and unwanted activity that can lead to unnecessary electricity consumption can be eliminated. To achieve this, two approaches are used. The first approach, which says that each device is monitored by means of individual meters or metering devices, is called intrusive load monitoring (ILM) and requires expensive deployment of metering devices for its use. In contrast to the first one, the second approach is non-intrusive load monitoring (NILM), which monitors electricity consumption without the need for any intrusion. In this configuration, the total energy consumed is disaggregated into the individual consumption of each load. With progress/advances in artificial intelligence, this approach is gaining interest with influences in other areas of research. Knowing that these developed techniques aim to encourage the occupants of dwellings to save energy by optimizing their electricity consumption, the paper presents a comparative study of these approaches, in order to highlight the strengths as well as the weaknesses of each of them. It is therefore a means of offering researchers the opportunity to make choices according to the orientations given to the research work.

Non-intrusive load monitoring (NILM) is emerging as a useful approach to improving the energy efficiency of buildings and households, particularly in the face of the growing challenges of environmental sustainability. Despite recent advances, the accuracy and reliability of disaggregation algorithms remain limited by the diversity of household energy behaviors and the heterogeneous operation of domestic appliances. In this study, we propose a method based on the prior identification of appliance operating states using the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Application with Noise) algorithm, prior to the disaggregation of overall energy consumption. Our study is based on the AMPds dataset (Almanac of Minutely Power Dataset), which provides detailed measurements of residential power consumption. Our methodology consisted of two main phases: an appliance operating state identification phase using the HDBSCAN algorithm, followed by an energy disaggregation phase based on a modified FHMM (Factorial Hidden Markov Model). The model’s performance was evaluated using three main metrics: F1 score, Mean Absolute Error (MAE) and Match Rate, in comparison with several reference models including the Adaptive-FHMM. The results obtained show that our HDBSCAN-FHMM model outperforms several models of energy disaggregation algorithms, but also the Adaptive-FHMM reference approach, with significant improvements of 6.25%, 46.24% and 12.04% for the F1, MAE and Match Rate metrics respectively. These performances reinforce the reliability of our method in accurately attributing the energy consumption of household appliances. Nevertheless, challenges remain, particularly with regard to model robustness in the face of variability in energy behavior and technological evolution. Future exploration of deep learning techniques combined with current methods could offer significant advances in energy disaggregation.

In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.