| Abstract |
The rapid urbanization and increasing energy demands necessitate innovative solutions for efficient energy governance in smart cities from the social science perspective. This paper introduces a comprehensive framework for Urban Energy Governance (UEG), focusing on the collaboration of three urban systems in energy exchange considering social and environmental impacts. To address the challenges of security, energy forecasting, and nonlinear optimization, we employ advanced techniques, including blockchain technology, Long Short-Term Memory (LSTM) networks for accurate energy forecasting, and a modified Particle Swarm Optimization (PSO) algorithm for solving the proposed nonlinear optimization problem. The utilization of blockchain enhances the security and transparency of energy transactions, fostering trust among stakeholders. Additionally, LSTM models leverage historical data to provide precise energy forecasts, optimizing resource utilization. The modified PSO algorithm tailors optimization to the specific nonlinearities of the proposed problem, ensuring efficient and effective decision-making in the context of Urban Energy Governance. Through a multi-faceted approach, our framework contributes to the advancement of smart city systems, providing a secure, forecast-driven, and optimized energy governance model for sustainable urban development. |
