Dynamic Satin Bowerbird-Tuned XGBoost for Enhancing Energy Efficiency in IoT-Enabled Smart Grids

Abstract

The development of smart grid (SG) technologies has significantly transformed the energy industry, particularly with the explosive growth of the Internet of Things (IoT). SG’s leverage IoT technologies to optimize electricity distribution, enhance operational efficiency, and improve energy management. However, challenges such as high energy consumption, security vulnerabilities, and limitations in real-time data processing hinder the full potential of IoT-enabled SGs. It explores the role of AI and ML in enhancing IoT-enabled SG systems to improve energy efficiency. The Dynamic Satin Bowerbird-tuned Extreme Gradient Boosting (DSB-XGBoost) algorithm is applied for short-term energy forecasting and optimizing energy efficiency in Python. AI-driven IoT sensors continuously collect data on power usage, voltage fluctuations, and demand patterns. To ensure data accuracy and consistency, data cleaning and Z-score normalization are employed for uniform data distribution. An AI-based system was used to enable real-time energy monitoring, efficient load balancing, and seamless communication between energy providers and consumers. Experimental findings demonstrate that the proposed system achieves a significant reduction in precision (91.5%), accuracy (90%), RMSE (0.17), MAE (0.12), and MSE (0.14) in energy forecasting compared to traditional methods. Furthermore, real-time AI optimization reduces power wastage, enhances energy efficiency, and lowers operational costs. These results highlight how AI and ML may transform SG systems by making them more flexible and effective, paving the way for sustainable, adaptive, and highly effective energy management systems.