Enhanced IoT-Driven Load Forecasting with Metaheuristic-Optimized Deep Learning for Logistics Planning

The integration of IoT technologies with smart logistics operations has opened unprecedented avenues for optimizing energy consumption in warehouse facilities. Accurate forecasting of electricity load is a key factor in cost reduction, operational efficiency, and sustainable energy management. This study presents an Enhanced Integrated Load Forecasting System (E-ILFS) that synergizes metaheuristic optimization with deep learning architectures of higher order for superior electricity load forecasting in dynamic logistics environments. Building on the foundational ILFS framework, our enhanced approach integrates Harris Hawks Optimization (HHO) for robust feature selection and an improved Residual Network (ResNet) enhanced with self-supervised learning (SSL) to more effectively capture complex, non-linear temporal patterns. Finally, comprehensive experimental evaluation on a real-world IoT-driven logistics dataset demonstrates that E-ILFS achieves state-of-the-art performance with an R² score of 0.8745, MAE of 23.59, and MAPE of 3.22%, representing a significant 12.51% improvement in R² over baseline models. In fact, the proposed system provides a practical and scalable solution for real-world logistics operations.