Enhanced building energy harvesting through integrated piezoelectric materials and smart road traffic routing

The study proposes a comprehensive strategy for intelligent trajectory planning and energy optimization within building energy systems to mitigate carbon emissions. The goal is to optimize energy consumption patterns while ensuring tenant comfort and operational efficiency. The proposed model, termed SGDo-HP-LR-GP, combines XGBoost, stochastic gradient descent optimizer (SGDo), Hyperparameters (HP), lasso regression (LR), geographical mapping (GP) and polynomial features to enhance prediction accuracy in the Intelligent Emergency Routing Response System (IERRS) for road traffic trajectories. This proposed model surpasses existing approaches in accuracy and predictive capability, enabling intelligent trajectory planning for energy usage. Machine learning is employed to construct a predictive model for forecasting building energy demands, recognizing the interconnectedness between road traffic trajectory and building energy usage. The design and layout of road networks play a pivotal role in influencing energy consumption within buildings, as efficient road systems reduce travel distances and fuel consumption. Finally, integrating piezoelectric materials in strategic locations is explored as a sustainable energy source to power buildings, demonstrating the potential to contribute to greener energy practices and enhance overall energy sustainability in the future. This study aims to bridge the gap between piezoelectric technology and building energy sustainability, offering innovative approaches for efficient energy utilization and a more environmentally friendly future.