Renewable energy, especially wind power, is required to reduce greenhouse gas emissions and fossil fuel use. Variable wind patterns and weather make wind energy integration into modern grids difficult. Energy trading, resource planning, and grid stability demand accurate forecasting. This study proposes a hybrid deep learning framework that improves forecasting accuracy and interpretability by combining advanced deep learning (DL) architectures, explainable artificial intelligence (XAI), and metaheuristic optimization. The intricate temporal relationships in wind speed data were captured by training Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM-GRU hybrid, and Bidirectional LSTM-GRU following data preprocessing and normalization. To enhance transparency, Local Interpretable Model-Agnostic Explanations (LIMEs) were applied, revealing key time-step contributions across three urban datasets (Los Angeles, San Francisco, and San Diego). The framework further incorporates the Snake Optimizer Algorithm (SOA) to optimize hyperparameters such as LSTM units, dropout rate, learning rate, and batch size, ensuring improved training efficiency and reduced forecast error. The model predicted 2020–2040 wind speeds using rolling forecasting; the SOA-optimized LSTM model outperformed baseline and hybrid models, achieving low MSE, RMSE, and MAE and high R² scores. This proves its accuracy, stability, and adaptability across climates, supporting wind energy prediction and sustainable energy planning.

Local United Arab Emirates (UAE) inhabitants have shown heightened awareness and interest in renewable energy (RE), resulting in a rise in the installation of solar photovoltaic (PV) systems in their residences; however, electric utility earnings have decreased due to this tendency. Energy decision-makers are concerned about discriminatory resident access to incentives and publicly funded solar PV frameworks. To reduce solar PV installations, utilities and energy players have adjusted RE initiatives. Utility companies provide solar PV-assisted installations. Nonetheless, adopting such frameworks requires a comprehensive feasibility study of all elements to achieve a win–win condition for all stakeholders, namely energy consumers, grid operators, solar PV company owners, regulators, and financiers. This article predicts the success of numerous local UAE solar PV models using agent-based modeling (ABM) to assess stakeholders’ measurements and objectives. Agents represent prosumers who choose solar PV. The effects of their installation choices on stakeholder performance measures are studied over time. ABM results show that suitable solar community pricing policies can benefit all stakeholders. Therefore, enhanced RE implementation rates can grow equitably. Also, electric utility companies can recoup profit losses from solar PV installations, and solar PV firms can thrive. The proposed modeling technique provides a viable policy design that supports all parties, preventing injustice to any stakeholder.