Innovative Energy Prediction using Kolmogorov-Arnold Networks (KAN) and Liquid Neural Networks (LNN) for Smart Grids

Abstract

The efficient operation and management of smart grids rely heavily on accurate energy consumption prediction. In this paper, we propose a novel approach for predicting energy consumption in smart grids that combines the strengths of Kolmogorov-Arnold Networks (KANs) and Liquid Neural Networks (LNNs). To deal with complex, time-varying energy consumption patterns, our hybrid model combines KANs' robust function approximation capabilities with LNNs' dynamic adaptively. KANs provide a solid framework for modelling complex, high-dimensional systems by combining multivariate functions into univariate functions. In addition, LNNs provide dynamic adaptability through their continuous and differentiable activation functions, which mimic the fluidity of liquids, making them particularly adept at dealing with time-dependent data and changing patterns. Our methodology combines the strengths of KANs and LNNs to create a hybrid model that can capture complex dependencies and temporal variations in energy usage data.