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• Novel GRU-LIME prediction method boosts accuracy. • GRU consistently beats traditional models for reliable predictions. • Renewable energy insights inform sustainable urban development. This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments. [ABSTRACT FROM AUTHOR]

At present, China and some other countries and regions are adopting both electricity and carbon trading markets to optimize the resource allocation of the power industry. The aim of this research is to explain the production behaviors of fossil fuel power enterprises in these complex dual market conditions and then evaluate the market effects. On the basis of the real market rules of China and the production characteristics of these enterprises, we describe the production decision process of enterprises with different market positions (leader, follower, and taker), obtain their optimal yield, and then offer market equilibrium results (total electricity supply, clearing price, total CO 2 emissions, and carbon intensity level). Besides, we evaluate the impacts of government interventions (market condition changes) on regional market equilibrium situations by numerical simulation. Our results indicate that the direction (positive or negative) of the above impacts is determined by the level of the allocation coefficients of the carbon emission quota, and the degree of these impacts is determined by the level of fine for unit excess carbon emissions. Moreover, our research also proves that enterprise collusion behaviors aggravate market failure but lower the carbon intensity level. • Fossil fuel power enterprises in China are facing complex dual market conditions. • We describe the production decision process of enterprises with different market positions. • We offer the equilibrium results of electricity and carbon trading markets. • We evaluate the impacts of government interventions and enterprise collusion behaviors. [ABSTRACT FROM AUTHOR]