A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction

Abstract

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.