This thesis addresses the Non-Intrusive Load Monitoring (NILM) task with the aim of accurately reconstruct and identifying the individual active power consumption of domestic appliances, directly from the aggregated load signature. Given the predicted consumption, the NILM model also classifies the operational status (‘On’/’Off’) of the considered device, at each time step. The focus of this work is to assess the generalization capability of the BERT model, specifically adapted for NILM, on the UK-DALE, an open-access dataset containing real-world power consumption data. For this purpose, a public and synthetic dataset SynD has been tailored to effectively train and validate the BERT4NILM model, before assessing its performances on the real-world dataset.