Abstract
This study investigates the intricate relationship between wind farm clean energy adoption and urban quality of life through three key contributions. Firstly, an Integrated Hybrid Deep Q-Networks (DQN) and Long Short-Term Memory (LSTM) model is introduced for wind farm power prediction. This novel approach synergistically leverages DQN’s decision-making capabilities and LSTM’s sequential learning to enhance accuracy by capturing temporal dependencies in wind conditions. Secondly, a Comprehensive Statistical Study on the Urban Energy Transition Nexus goes beyond traditional analyses, exploring the links between clean energy adoption, job creation, and urban livability. Advanced statistical methods quantify wind farm impacts on job creation and overall quality of life, providing a nuanced understanding of the urban energy transition. Thirdly, the Harmony Search (HS) algorithm, with modified parameters, optimizes the hybrid DQN-LSTM model. This nature-inspired algorithm addresses hyperparameter tuning challenges, contributing a unique dimension to model optimization for better convergence and exploration of the solution space. Finally, the study delves into job creation and livability in the context of urban energy transition, examining how the adoption of wind farm clean energy contributes to employment opportunities and enhances overall urban quality of life. Together, these contributions offer a comprehensive framework for integrating wind farm clean energy into urban environments, providing innovative solutions for accurate power predictions and facilitating sustainable urban energy transitions.
Published Version
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