Multiple sources of worries such as economic constraints and the dangers of climate change have moved society towards the process of optimizing the use of their electricity. However this approach towards energy consumption has become a source of uncertainty and worry as load monitoring becomes the norm. In order to overcome the privacy concerns techniques on Non-Intrusive Load Monitoring have been in development since the 1980s. In the field of load disaggregation applications of NILM there is constant reference to three topics to be improved on, results, interpretability and responsiveness. This paper investigates the role symbolic regression tools in the field of NILM, both as a singular tool of disaggregation and as a support instrument of deep learning models more common in the literature, such as LSTM, to improve on their prediction capabilities and adding a layer of interpretability to the results. The experimentation of this document offer two different solutions with various degrees of success depending on the proposed scenario although with quantifiable improvement over the established baseline.