Integrating Machine Learning for Real-Time Energy Load Forecasting in US Smart Grids: A Multi-Model Comparative Approach

Abstract

The increasing complexity and decentralization of smart grids in the U.S. have heightened the demand for accurate and responsive energy load forecasting systems. This research presents a comprehensive real-time machine learning framework for short-term energy demand prediction, utilizing multi-source data from national grid operators, weather stations, and calendar logs. We integrate electricity demand records from the U.S. Energy Information Administration (EIA) with weather attributes from NOAA, along with temporal features such as hour, day, seasonality, and holiday indicators, to create a feature-rich dataset for predictive modeling. Our feature engineering captures lagged consumption behavior, rolling averages, time-series decomposition signals, and weather-induced demand variability. Through exploration data analysis (EDA), we uncover critical load patterns, diurnal cycles, and seasonal fluctuations across different grid regions. We implement and evaluate a diverse range of supervised learning models, including tree-based regressors (Random Forest, XGBoost), multilayer perceptrons, and deep recurrent architectures such as LSTM, Bi-LSTM, and attention-enhanced LSTM. Additionally, we construct hybrid models that combine convolutional layers with temporal encoders to capture both local and sequential patterns in load data. Evaluation on real-world load datasets reveals that deep LSTM-based models outperform traditional baselines, achieving a mean absolute percentage error (MAPE) of less than 5% in high-variance regions. Visual inspection of model predictions and residuals confirms their robustness during both peak and off-peak periods. To support operational deployment, we also simulate online inference scenarios using rolling windows and highlight each model’s responsiveness to sudden shifts in demand. Our results demonstrate the scalability and reliability of machine learning-driven forecasting pipelines, providing grid operators with a data-centric tool for real-time energy management in smart infrastructure ecosystems.