Selection of features from power theories to compose NILM datasets. (April 2022) - Souza, Wesley A.; Alonso, Augusto M.S.; Bosco, Thais B.; Garcia, Fernando D.; Gonçalves, Flavio A.S.; Marafão, Fernando P.
Abstract: The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation. Highlights: A feature selection method allows to characterize and classify household appliances. Collinearity analysis allows to reduce the number of features for NILM applications. The CPT and IEEE 1459 standard power theories are used for the feature selection. Results indicate the CPT as an efficient tool for load characterization in NILM. A set of the most representative features for NILM are suggested for future research.