AI-Enhanced Sustainable Energy Management and Policy Recommendations for the U.S. Power Sector

Abstract

The transition to a sustainable U.S. power sector is faced with the challenge of balancing reliability, decarbonization, and affordability in more variable demand conditions. There is tremendous potential for artificial intelligence (AI) to make huge contributions to forecasting accuracy, optimizing resource allocation, and in developing policy and evidence-based policy. This paper presents a new model of hybrid artificial intelligence for sustainable energy management applications that consists of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer networks combined with CatBoost meta-learner and then optimized with residual BiGRU-attention and α-blender. The proposed model was tested using the public dataset of hourly electricity generation and demand data from the United States of America with temporal and operational characteristics significant to grid planning. Experimental results indicate a very high performance of the hybrid model compared with baseline methods for all performance measures. Specifically, it had a minimum MAE (0.007503), minimum MSE (0.000102) and RMSE (0.010082) and highest R2 value (0.995954) than single Lstm, GRU, Catboost and Xgboost. Aside from technical performance, the study accomplishes three contributions: efficiency-enhancing energy management using AI-driven energy forecasting and optimization, American context - use of demand forecasting for renewable integration and grid balancing, and extending technical understanding to the policy advice for future adaptive regulations. By integrating AI analytics, policy design, and sustainability policy, this research makes a methodological contribution as well as a governance-centric framework with a focus on setting up AI as an accelerator for the sustainable transformation of America's power grid.