Artificial Intelligence in Energy Sustainability: Predicting, Analyzing, and Optimizing Consumption Trends

Abstract

The growing global energy demand, coupled with the urgent need for sustainability, has necessitated the adoption of artificial intelligence (AI) and machine learning (ML) techniques to optimize energy consumption. Traditional energy management approaches often struggle to capture the complexity of consumption patterns, inefficiencies, and environmental impacts. This research presents a data-driven framework that uses AI to predict, analyze, and optimize energy consumption trends in key sectors, including hospitals, urban infrastructure, and renewable energy systems in the USA. Using large-scale energy datasets containing variables such as power usage, peak demand, weather conditions, and grid efficiency, the study employs six advanced Machine Learning models: XGBoost, Random Forest, Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Support Vector Machines (SVMs), and K-Means clustering. These models are used for consumption forecasting, anomaly detection, and demand-side management. To enhance predictive accuracy and address challenges such as seasonality and volatility in energy consumption, the study integrates time-series analysis with feature engineering techniques, including principal component analysis (PCA) and autoencoders for dimensionality reduction. Data imbalance is mitigated using the Synthetic Minority Over-sampling Technique (SMOTE) to ensure fair representation of extreme consumption behaviors. Model performance is evaluated using RMSE, MAE, MAPE, and R² metrics, ensuring robust assessment of predictive accuracy and energy optimization effectiveness. Additionally, the research explores the impact of AI-driven insights on policy formulation, cost reduction, and carbon footprint minimization.