Artificial Intelligence Driven Internet of Things Framework for Wind Energy Monitoring and Performance Enhancement in Smart Cities

The integration of renewable energy sources in urban environments presents unique challenges due to complex wind patterns and infrastructure limitations. This study developed and implemented an advanced Internet of Things (IoT) framework incorporating deep learning algorithms for real-time wind energy monitoring and optimization in Abha, Saudi Arabia, addressing the limitations of conventional wind energy systems through intelligent sensor networks and predictive analytics. The study deployed 2300 IoT sensors across 75 urban wind turbines, collecting environmental and performance data over 24 months. The methodology implemented a custom long short-term memory (LSTM) neural network architecture with a dropout rate of 0.3, utilizing TensorFlow framework version 2.7 for model training. The system incorporated comprehensive sensor arrays including ultrasonic anemometers, digital wind vanes, temperature sensors, and tri-axial accelerometers, with data collection frequencies ranging from 0.1 Hz to 1 kHz. The implementation resulted in a 34.2% increase in energy harvesting efficiency, with turbine downtime reduced by 56%. The LSTM model achieved 91.7% accuracy in wind pattern prediction, enabling proactive adjustments that improved overall system reliability by 29%. Component-wise reliability analysis revealed the highest performance in sensor networks (MTBF = 94.3 days) and communication infrastructure (MTBF = 89.5 days). Statistical validation confirmed significant improvements across all metrics (p < 0.001) with the autoregressive integrated moving average (ARIMA) model demonstrating strong predictive capability (R-2 = 0.934). The AI-driven framework achieved a 41% reduction in maintenance costs while increasing annual energy output by 23.8%, suggesting favorable techno-economic viability despite initial investment requirements. The developed framework demonstrates significant potential for optimizing urban wind energy systems through AI-driven monitoring and predictive maintenance. The results establish a scalable approach for smart city wind energy management, providing a comprehensive solution for urban renewable energy integration.