Optimized Energy Forecasting Using Hidden Markov Model and Transformed Fuzzy Relational Matrices Enhanced by Genetic Algorithm and Particle Swarm Optimization

Abstract

Accurate energy forecasting is essential for the efficient operation of smart grids and renewable energy systems. This study presents an optimized energy forecasting model that combines a Hidden Markov Model (HMM) with transformed fuzzy relational matrices (TFRM), enhanced by a genetic algorithm (GA) and particle swarm optimization (PSO). The HMM effectively captures the temporal dependencies in energy consumption data, while the TFRM handles uncertainties and imprecisions. The genetic algorithm refines the fuzzy relational matrices, and PSO further optimizes the model parameters to ensure convergence to the best possible solution.The proposed model's performance is validated using real-world energy consumption datasets, showcasing its accuracy and robustness compared to traditional forecasting methods. Experimental results demonstrate that this hybrid approach successfully addresses the nonlinear and non-stationary characteristics of energy data, leading to more reliable and precise forecasts. This innovative method holds significant promise for enhancing energy management and optimization in various applications, contributing to the development of smarter and more efficient energy systems.