Integrated AI-Driven Smart Power Grid for Real-Time Monitoring and Forecasting

Abstract

 In the context of the present day, the availability of non-renewable energy resources is declining, and the energy demand is on the rise with the increase in population and technological advancements. Although the use of solar energy and other forms of renewable energy has been integrated with the conventional energy grid, the intermittent nature of these forms of energy often creates an energy supply-demand imbalance and inefficient use of energy. To overcome the problems associated with the efficient use of energy, the present work proposes the development of an AI-based smart grid energy prediction system called NeuroWattic, which optimizes the use of energy, prevents wastage of energy, and promotes the efficient use of renewable energy. The model uses the stacking ensemble learning method, which combines the Random Forest, Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Machine (HistGBM) models as the base models, and the Ridge Regression model as the meta-model for efficient energy prediction. The model has been trained with high-resolution, multi-zone power consumption data, with a time resolution of ¹⁰ minutes, and meteorological data such as temperature, humidity, wind speed, and solar irradiance. Feature engineering has been done on the dataset, which creates temporal features, lag features, and weather-based features, which are used to model the complex energy consumption patterns. The model has been designed to perform iterative multi-step forecasting up to ¹⁴ days into the future, which enables efficient energy management. The performance of the model has been tested, and the results indicate significant improvement in the efficiency of the model, with an RMSE of ¹⁸³.¹⁸ kW, MAE of ⁹³.⁸³ kW, MAPE of ⁰.¹⁵³⁷%, and R² score of ⁰.⁹⁹⁹⁸. Moreover, the model has been integrated with an interactive dashboard that enables real-time monitoring, visualization of the duck curve, and cost optimization. The proposed system provides a scalable and intelligent solution for improving energy efficiency and enabling reliable renewable energy integration in modern smart grids.