A novel probabilistic approach for driving the sustainable energy circular economy: Innovating efficiency in renewable resource markets.

• Enhanced renewable energy market predictive accuracy using hybridizing RNNs and CNNs. • Efficient feature selection using MHC algorithm to enhance model efficiency. • Robust generalization using hybrid model integrates RNNs, CNNs, and MHC. Our research introduces a novel hybrid model aimed at driving the sustainable economy by innovating efficiency in forecasting renewable resource market trends for recovery. The fusion of Hierarchical Recurrent Neural Networks (RNNs) and LeNet-5 Convolutional Neural Networks (CNNs) capitalizes on the distinct advantages of both architectures. RNNs capture temporal dependencies, while CNNs identify spatial patterns, providing a holistic understanding of market dynamics. This symbiosis significantly enhances predictive accuracy, empowering stakeholders with more reliable insights into the trajectory of renewable resource markets. Efficient feature selection is achieved through the incorporation of the Modified Hill Climbing (MHC) algorithm. As an evolutionary feature selection technique, MHC dynamically refines input features during the learning process, adapting to the evolving nature of renewable resource markets. This adaptive feature selection not only improves computational efficiency and interpretability but also ensures a focus on the most relevant information, aligning with the goal of driving sustainable circular economic recovery. The fortified hybrid model, harmonizing RNNs, CNNs, and MHC, showcases heightened robustness and generalization across diverse market conditions. By effectively merging deep learning architectures with an evolutionary feature selection process, the model adeptly captures intricate patterns while mitigating overfitting. This contribution underscores the applicability of the proposed approach to real-world scenarios, positioning it as a catalyst for innovative efficiency in renewable resource markets—a pivotal driver for the recovery of the sustainable economy. Our research marks a crucial advancement in the realm of reliable and adaptable forecasting models, essential for informed decision-making in the dynamic landscape of renewable energy markets. [ABSTRACT FROM AUTHOR]