Constant human intervention in tackling energy efficiency challenges is becoming a bit from the past. As moving forward to the future, smart grids are consistently improving through machine learning data analytics, from smart sensors to using algorithms to implement models that can classify and detect different power profiles without manual human efforts. Non Intrusive Load Monitoring (NILM) techniques are a leading technology in accessing the data from aggregated power signals of different facilities without installing meters on every appliance. This work aims to introduce one of the NILM techniques using a fully Convolutional Network model with the help of data already collected by a start-up based in Turin, Midori.