ARTIFICIAL NEURAL NETWORKS FOR EDGE AND FOG COMPUTING-BASED ENERGY PREDICTION

Abstract

Edge and Fog Computing have become increasingly popular in recent times as innovative distributed computing paradigms that bring computational power closer to the data source. This proximity enables real-time processing and analysis of data, making it particularly advantageous in energy management systems. Accurate prediction of energy consumption is crucial for efficiently utilizing resources and optimizing energy usage. In the realm of energy prediction, the traditional approach involves utilizing statistical methods, time series analysis, and regression models. However, these methods have their limitations. They often require manual feature engineering, overlooking intricate relationships withinthe data, and leading to limited predictive performance, especially when dealing with complex and nonlinear datasets. Furthermore, they may not fully capitalize on the benefits of distributed computing in Edge and Fog environments. On the other hand, accurate energy prediction holds immense significance for sustainable energy management, especially in the context of modern smart grid systems and Internet of Things (IoT) applications.