**5. Conclusions**

The proposed research makes it possible to recognize the characteristic profiles of a set of household appliances with accuracies ranging from a minimum of 83.3% for the microwave to a maximum of 98.4% for the refrigerator. The incorporation of a cluster stage to the proposed methodology allows for increasing the level of precision, with a minimum of 95.1% for the kettle and 100% for the microwave and washing machine. The difference in the levels of precision between the artifacts is mainly due to the presence of various types of electrical charges: linear, non-linear, constant over time, and variable over time.

From the results obtained, it is concluded that it is possible to predict consumption profiles in household appliances, with high levels of certainty, high sampling speed, and under multiple operating states.

Among the limitations of the research, we can mention the need to validate the proposal on a large scale and the use of a basic neural network that required more than one clustering for early differentiation.

These results encourage the development of solutions that, through the automatic recognition of electrical devices, will allow progress in self-management and the promotion of citizen participation in energy efficiency.
