Optimizing Electricity Load Forecasting Using the MFLO-BP Model: An Integration of Modified Frilled Lizard Optimization and BP Neural Network

To enhance the accuracy and convergence speed of electricity load forecasting, this paper proposes an optimized electricity load forecasting model that integrates a Modified Frilled Lizard Optimization (MFLO) algorithm with a Backpropagation (BP) neural network. The MFLO addresses the traditional challenges of slow convergence and local optima by incorporating Lévy flight mechanisms and self-weight factors, which enhance global search capabilities. By integrating information entropy into fitness adjustments, the model improves the quality of initial solutions, leading to better predictions. The MFLO-BP model demonstrates significant performance improvements, achieving a Mean Absolute Percentage Error (MAPE) of 1.09%, a Root Mean Square Error (RMSE) of 152.5, and an $\mathbf{R}^{2}$ value of 0.97, outperforming conventional BP models with MAPE of 1.57% and RMSE of 205.4. Our findings contribute to more effective energy management strategies and offer a robust framework for evolving energy demands.