Harnessing Machine Learning to Analyze Energy Generation and Capacity Trends in the USA: A Comprehensive Study

The constraints in conventional energy forecasting models in the USA are increasingly being appreciated as the energy landscape evolves. Such models are generally constructed on linear assumptions that do not capture energy generation and consumption patterns as dynamic and multi-dimensional. This research project aimed to develop effective machine-learning models to interpret historical trends in energy generation and capacity in the United States. With access to massive datasets for various energy sources—fuel-based to renewable and nuclear energy—this research endeavors to discover actionable information that can optimize energy output and improve grid stability. The dataset used is a comprehensive overview of energy production in America, integrating information from a variety of sources, ranging from fossil fuels to renewables and nuclear energy. This extensive dataset is sourced from reliable sources such as the U.S. Energy Information Administration (EIA) which supplies vast historical and current data on energy consumption and production patterns; the Federal Energy Regulatory Commission (FERC) which supplies information on regulatory frameworks and market forces that affect energy production; and smart grid management systems that deliver real-time data on energy flows and grid performance. Selecting appropriate machine learning models is crucial to process engineered features and derive actionable insights effectively. For this purpose, we use Logistic Regression, Random Forest Classifier, and the Support Vector Machines. The model performance table indicates how well three machine learning algorithms—Logistic Regression, Random Forest, and SVM—classify between renewable and non-renewable energy sources. All three models are highly accurate, with SVM having a slight edge, followed by Random Forest and lastly Logistic Regression. AI-powered energy forecasting is revolutionizing energy infrastructure planning in America through the provision of sophisticated information to guide investment in grid upgrades and expansion. As America transitions to dependence on renewable energy, AI-based forecasting is essential to optimize the integration of solar and wind energy production onto the grid. The variability of these renewable resources poses special challenges for grid managers, who need to balance demand and supply in real time. Moreover, AI can be used to identify gaps in renewable energy capacity and storage. The infusion of AI-based insights into the energy sector has far-reaching implications for U.S. policymakers, equipping them with fact-based tools to efficiently apply energy reforms. With evolving energy patterns, legislators need to adjust regulations to facilitate a seamless transition to a more sustainable energy system.