With the growth of computational power capabilities of electronic embedded devices, an increasing number of tasks are moving from centralized processing units to edge devices, implementing edge computing paradigms. This thesis, developed as a collaboration between Politecnico di Torino and ABB, is about the adoption of this kind of distributed architecture in ABB circuit breaker systems, aiming to evaluate the potential integration of Machine Learning capabilities directly into the microcontroller of the circuit breaker mainboard. Specifically, the treatment starts with a description of the development of a Load Forecasting Machine Learning algorithm exploiting the TensorFlow framework, discussing in detail the dataset collection, training and testing processes.