Decision-making dynamics in urban energy transition: A statistical analysis of policy frameworks, stakeholder collaboration and technological innovations.

This research explores the intricate dynamics of decision-making in the context of urban energy transition, focusing on the interplay between policy frameworks, stakeholder collaboration, and technological innovations. The study makes three key contributions to the field. Firstly, it proposes an innovative approach to enhance market price prediction accuracy by integrating a Generative Adversarial Network (GAN) model. Leveraging the Adaptive Bat Algorithm (ABA) for GAN parameter optimization, the model exhibits improved precision in forecasting electricity prices, thereby providing stakeholders with more reliable insights. Secondly, the research introduces optimized model parameters for efficient energy decision-making, employing the ABA to dynamically adapt GAN parameters. This adaptive process enhances the overall performance of the model, facilitating better-informed decisions for stakeholders in the energy sector. Lastly, the integration of the ABA for GAN parameter optimization contributes to informed decision-making by generating more accurate market price values. This precision empowers policymakers, utilities, and investors with reliable insights, facilitating the development and implementation of strategies aligned with the goals of sustainable and efficient urban energy transition. The research underscores the significance of accurate market price predictions in shaping a resilient and sustainable energy landscape. • GAN technique to enhances electricity price forecast accuracy. • ABA optimizes GAN parameters for efficient decisions. • ABA technique to improve accuracy of market price predictions. [ABSTRACT FROM AUTHOR]