AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA

This research explores how Machine Learning and AI can be used to enhance energy efficiency, forecast energy consumption trends, and optimize energy systems in the USA. This research used datasets comprising household energy usage, electric vehicle adoption trends, and smart grid analytics obtained from public sources, databases, and IoT sensor devices. This study applies advanced machine learning techniques such as deep learning, regression models, and ensemble learning to improve forecasting accuracy aimed at achieving efficient resource allocation. Additionally, this study investigates fault prediction in New Energy Vehicles (NEVs) and its implications for grid stability and energy demand management. The research also examines the socioeconomic impact of AI-driven energy policies and highlights their role in reducing carbon footprints, promoting energy equity, and fostering sustainable economic growth. Recurrent Neural Networks are applied to predict energy consumption trends and electric vehicle(EV) adoption rates by analyzing historical usage data. Convolutional Neural Networks and Autoencoders are used for anomaly detection in NEV battery performance and predictive maintenance. Deep Learning models also use real-time IoT sensor data to enhance the efficiency of energy distribution in smart grids. Linear Regression models are used to predict household and industrial energy demand based on factors such as weather, pricing, and socioeconomic variables. Linear Regression also predicts energy consumption trends in hospitals and factories. Random Forest and XGBoost are used in energy demand forecasting and energy consumption clustering. Performance evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) are utilized to assess model accuracy and effectiveness.