Forecasting Renewable Energy Trends in the USA: An AI-Driven Analysis of Electricity Production by Source - Shovon, Md Shihab Sadik; Gomes, Clapher Ankur; Reza, Syed Ali; Bhowmik, Proshanta Kumar; Gomes, Clinton Anthony Heaven; Jakir, Tanaya; Amjad, Mohammad Hamid Hasan; Hossain, Arat; Hasan, Md Sakibul (Journal of Ecohumanism, 2025)
The United States is undergoing a significant shift toward renewable power as part of the broader international movement to counter climate change, reduce emissions, and increase energy independence. The shift is spearheaded by the mass adoption of solar, wind, and hydroelectric power, which are replacing the traditional fossil fuel-based power supplies. The primary purpose of this research was to develop and evaluate AI-based models for forecasting trends in the production of electricity through renewable energy in the USA. This research centered on the examination of renewable energy trends in the United States, with emphasis on solar, wind, and hydroelectric power. The dataset employed in this analysis encompasses wide-ranging electricity production data on renewable power, including solar, wind, and hydroelectric power, across several years to capture seasonal and long-term trends. The key data sources were from the United States' Energy Information Administration (EIA), offering detailed real-time and historical power production data at the national and regional levels, and the National Renewable Energy Laboratory (NREL), offering high-resolution renewable power generation, weather, and technology performance metrics data. Real-time grid data from regional transmission organizations (RTOs) and independent system operators (ISOs) has also been incorporated to add granularity and precision to the dataset. Three machine learning models were employed to make forecasts for renewable power production, namely, Random Forest, Support Vector Machines, and Gradient Boosting Regressors, each chosen for its unique strengths in tackling different aspects of the problem. For classification tasks, accuracy, precision, recall, and F1-score metrics were used to evaluate the models' ability to classify the energy production levels (e.g., high, medium, low). The maximum accuracy was achieved by Gradient Boosting, followed by SVM and Random Forest. In retrospect, AI insights are revolutionizing renewable energy planning in the USA through more accurate forecasts of the production and consumption of energy. These insights make it possible for grid managers to optimize the grid's capacity, ensuring that the infrastructure does not get over- or under-loaded. The integration of AI-based forecasting in renewable energy planning has significant policy and regulatory consequences at the federal and state levels. The integration of AI with smart grid technologies is a game-changer when it comes to renewable power management in the United States.