Optimizing urban energy management: A strategic examination of smart grids and policy regulations

Abstract

This research addresses the critical challenges in urban energy management, focusing on load forecasting and optimizing renewable energy sources considering policy regulation. A novel Hybrid Long Short-Term Memory-Artificial Neural Network (LSTM-ANN) learning technique is proposed for accurate load and renewable energy output power forecasting. The hybrid model leverages the strengths of LSTM and ANN, providing a robust solution for capturing temporal dependencies and complex patterns in the data. Additionally, the Bee Algorithm for Optimization (BAO) is employed to handle the nonlinearity and nonconvexity inherent in energy management problems. BAO optimally schedules the operation of various generation units, considering the diverse characteristics of gas turbines, photovoltaic panels, and wind turbines. The effectiveness of the proposed hybrid model and optimization algorithm is demonstrated through comprehensive comparisons, showcasing superior accuracy and convergence speed compared to traditional methods. This research contributes to the advancement of intelligent energy management systems, offering a reliable framework for optimizing renewable energy utilization in real-world applications.