Increasing energy consumption around the world presents a number of signifi- cant challenges. Smart Grids play a crucial role in creating more sustainable and resilient energy systems. These systems are essential to face various energy and environmental challenges, allowing for more efficient management of resources, reduction of waste and integration of renewable energy sources.

This research proposes the development of prediction models for Smart Meter, a smart meter capable of performing precise measurements, collecting consumption data in real time and executing prediction algorithms locally. The device integrates technologies for long-range wireless communication and machine learning techniques optimized for edge devices.

One of the main contributions of this work is the demonstration that it is possible to perform complex energy consumption predictions directly on the measuring device, reducing the need for continuous transmission of raw data and relieving the load on centralized systems. This not only improves the operational efficiency of smart grids, but also offers benefits in terms of consumers’ privacy and data security.