**6. Conclusions**

In this paper, a novel real-time event-based energy disaggregation methodology is introduced. Initially, a simple event-detection algorithm is proposed to find time instants when an appliance is turned-on and extract transient responses at 100 Hz. Next, a convolutional neural network classifier identifies if a transient response was caused by a target appliance. Finally, a power estimation algorithm is implemented considering appliance end-uses as pulses of constant power. Experimental results show a promising performance for specific appliances.

Unlike most relevant papers in the literature, the proposed non-intrusive load monitoring system can identify in real-time when an appliance is turned-on based on its information-rich transient response sampled at 100 Hz. Furthermore, it is delay-free since, once a target appliance has been turned-on, the active power can be directly calculated. Moreover, the system is computational and memory-efficient and can be integrated into smart meters.

The proposed approach can be used for a significant number of appliances with negligible error. However, energy consumption of specific appliances, e.g., heat pump, tumble dryer, including many states of operation, cannot be calculated by the proposed methodology. For such cases, dedicated algorithms should be implemented. As future steps, a more robust power estimation algorithm will be examined for multi-state appliance uses. Additionally, the proposed methodology will be tested on more types of appliances.

**Author Contributions:** Conceptualization, methodology, software, visualization, formal analysis, writing—original draft preparation, C.A.; validation, investigation, writing—review and editing, C.A., D.D., T.P. and A.C.; resources, C.A., D.D. and T.P.; data curation, C.A. and A.C.; supervision, D.D. and T.P.; project administration, funding acquisition, D.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research has been co–financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project: T2EDK-03898).

**Data Availability Statement:** The public available BLUED [48] dataset as well as a private dataset obtained from NET2GRID BV that is not public available have been used in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.
