 Non-intrusive load monitoring, NILM, is a process that analyzes the electrical consumption of a building or home to identify the different devices consuming energy. This study developed two models, one based on a recurrent neural network, RNN, and another based on a gated recurrent unit, GRU, to improve the accuracy of NILM. The results showed that both models were able to accurately identify the different devices consuming energy, as well as their respective power consumption. Additionally, when the location information of the occupants was included, the model's performance improved even further. This article was authored by Myeonghun Lee and Hyunjun Moon.