The rapid expansion of hyperscale data centers is significantly increasing electricity demand in Northern Virginia. Dominion Energy, the region's primary electric utility, must reinforce its transmission network to support this growth. These projects require planned outages that must be evaluated months in advance to support construction planning and outage coordination while meeting NERC and PJM N-1 reliability requirements. Long-term outage studies are commonly performed day by day using monthly or seasonal peak-load assumptions. Although this approach simplifies analysis, it can be overly conservative because it does not capture granular load variability. Consequently, short-duration outages that may be feasible under realistic loading conditions are often postponed or denied, delaying critical transmission expansion and grid modernization projects. This paper evaluates the operational value of incorporating realistic multigranular load forecasts into long-term, contingency-based outage assessments and compares the results with conventional peak-based methods. Daily, weekly, and monthly forecasts are developed using statistical and machine-learning models, including SARIMA, Prophet, Gradient Boosting, and Random Forest, followed by bottom-up temporal reconciliation to maintain consistency across forecast horizons. Results show that granular load forecasts reduce unnecessary conservatism and improve outage accommodation, particularly for short-duration requests, without changing existing reliability criteria. The proposed approach can strengthen long-term outage coordination and support timely transmission reinforcement and grid modernization.

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