Smart meter-based demand forecasting for energy management using supercapacitors

The smart grid paradigm has introduced new capabilities for monitoring and managing intelligent energy systems. In this context, IoT environments integrate smart sensors and devices to record electricity consumption and production in real time. This article proposes a methodological framework for energy management that incorporates real-time data processing, predictive modelling, and supercapacitor-based storage control to address short-term power fluctuations caused by load variability. The proposed approach is implemented in three phases. First, demand data are collected using a smart meter, with measurements stored on a local server. In the second phase, the data are processed to develop a forecasting model based on a Wide Neural Network, which updates autonomously. In the final phase, energy management is performed using a demand smoothing algorithm and a supercapacitor charge/discharge control mechanism. The forecasting performance was assessed through a comparative analysis of neural network models. The WNN achieved a correlation coefficient of 0.94 and a mean absolute percentage error of 6.3%. These results were obtained in a real-time processing environment and demonstrate the model's ability to generalize under variable load conditions. In addition, the proposed system enables direct control of the storage system's state of charge based on forecasted demand and a predefined power reference. Experimental validation was conducted in a prototype setup integrating smart metering, data acquisition, and automated response capabilities.