AI-Powered Forecasting and Optimization of Energy Consumption in the USA: Machine Learning Approaches for Sustainable Urban and Institutional Development

Abstract

The increasing global demand for energy, combined with the urgent need for sustainability, has driven the adoption of artificial intelligence (AI) and machine learning (ML) techniques to optimize energy consumption. Traditional energy management approaches often struggle to account for the complexity of dynamic consumption patterns, operational inefficiencies, and environmental impacts. This research presents a comprehensive AI-powered framework aimed at forecasting and optimizing energy consumption across key sectors in the USA, including urban infrastructure and institutional facilities. By utilizing extensive energy datasets that encompass variables such as electricity usage, peak demand, weather variations, and building characteristics, the study applies four advanced ML models: Random Forest, XGBoost, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks, to achieve high-precision consumption forecasting. To facilitate intelligent optimization and adaptive energy management, Reinforcement Learning (RL) techniques are employed. These techniques enable dynamic decision-making to minimize energy usage without compromising service quality. Additionally, the study incorporates K-Means clustering to categorize consumption patterns and uses Isolation Forests along with Autoencoders for robust anomaly detection and monitoring of unusual energy behaviors. To enhance predictive robustness and address challenges such as seasonality, volatility, and the high dimensionality of input features, the research integrates time-series feature engineering and unsupervised learning for dimensionality reduction. Data imbalance issues are addressed using strategic sampling techniques to ensure fair model training across both normal and extreme consumption scenarios. Model performance is rigorously evaluated using metrics such as RMSE, MAE, MAPE, and R², ensuring a comprehensive assessment of predictive accuracy and optimization effectiveness.