The rapid proliferation of Artificial Intelligence (AI) and Electric Vehicles (EVs) is reshaping electricity demand patterns,

with important implications for grid stability and long-term energy planning. Conventional forecasting approaches, 

largely based on historical trends and macroeconomic indicators, have limited ability to represent structurally distinct and 

technology-driven demand growth. This study proposes a modular electricity demand forecasting framework that explicitly

separates baseline demand from AI- and EV-driven components. Within this structure, the ARIMAX model is used to 

estimate baseline demand from macroeconomic variables, while independent modules capture additional demand associated

with AI data centers and EV deployment. Using the United States as a case study, annual data from 2015 to 2050 

were analyzed. ARIMA, ARIMAX, and Prophet models were evaluated through observed-data validation and long-term 

scenario consistency analysis relative to publicly available outlook trajectories. In addition, a diffusion-based probabilistic 

module was implemented to characterize long-horizon demand uncertainty. Results indicate that incorporating AI and 

EV components improves scenario consistency relative to baseline configurations. The proposed framework provides a 

transparent and adaptable structure for integrating emerging demand drivers into medium- to long-term electricity planning

under technological disruption.