AI and cloud are changing how electricity demand shows up on the grid. Traditional annual average forecasting misses sharp ramps, location specific spikes, and rapid additions tied to hyperscale campuses. These facilities run close to 24 or 7 baseload, then throw sudden compute surges for model training, often clustered on a few feeders or within a single county. Utilities, owners, and builders need a planning approach that is granular, flexible, and scenario driven so that projects stay on time and systems stay reliable.
What changed
Five years ago, data center growth was steady but manageable in many territories. In 2025 it is a leading driver of new demand. Reliability bodies and utility planners have flagged three shifts that break old assumptions. First, load shape has become more volatile within a day because training workloads can ramp hard and then drop off. Second, growth is highly concentrated. One metro can add hundreds of megawatts while the statewide average barely moves. Third, timelines are compressing. A campus can move from land control to major construction faster than a traditional power plant approval, which means forecasts must refresh far more often than once a year.
These realities filter into everything from interconnection queues and substation one lines to water permits and road access. Owners are asking for firm energization dates earlier in the process. Builders are sequencing work around utility outages and relay testing windows. Local governments expect construction schedules that do not disrupt neighborhoods during hot months. The planning process has to keep up with these pressures.
Why legacy forecasting falls short
Annual or even monthly views mask what operators actually face. Old models assume smooth ramps, broad geography, and predictable mixes of residential, commercial, and industrial load. They do not capture how a large compute cluster behaves when workloads shift from inference to training, or the effect of liquid cooling on auxiliary loads. They also fall short when multiple campuses arrive on the same sub transmission path. The result is a growing gap between the capital plan and the real project pipeline. That gap becomes late design changes, supply chain scrambles, and public hearings when energization slips.
A better playbook
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Forecast hourly and by location
Move from system wide averages to nodal or feeder level views with hourly profiles. Build geospatial forecasts that reflect where campuses are likely to land based on land availability, fiber routes, water access, and existing electrical infrastructure. Incorporate weather correlated profiles that capture seasonal differences, including rising winter peaks in some regions. -
Use multiple scenarios, not a single point estimate
Create low, base, and high growth cases for every affected zone, plus a contingency case for accelerated timelines. Tie each case to specific customer milestones. Examples include site purchase recorded, water and air permits approved, main building foundations poured, or interconnection deposits posted. Update forecasts quarterly so the capital plan follows facts instead of rumors. -
Plan for flexibility
Standardize on modular designs that scale with firmed commitments. Pre qualify substation package vendors. Reserve mobile transformers and spare breakers. Stage line upgrades so that initial service can land quickly, then expand as the campus grows. Temporary service solutions can bridge the gap to permanent capacity without stranding assets if growth slows. -
Align interconnection and construction schedules
Treat the grid plan as its own critical workstream in the master schedule. Lock design reviews, planned outages, meter and telecom cutovers, and energization windows early. Maintain a single milestone map that the utility, owner, EPC, and key subcontractors review together every month. When utility crews, protection settings, and site readiness move in step, months drop off the timeline and resequencing is avoided. -
Design for grid services from day one
Make large campuses part of the solution. Where policy allows, specify fast load shedding for emergencies, on site storage that can ride through short grid events, and demand flexibility that shifts non critical compute when the system is tight. These capabilities reduce stress on the grid, can lower interconnection costs, and sometimes qualify for tariffs or programs that pay for flexibility. -
Strengthen upstream and downstream visibility
Do not plan the point of interconnection in isolation. Map upstream constraints on sub transmission and transmission, and downstream constraints on distribution circuits that serve campus auxiliaries and nearby customers. Bring protection engineering, telecom, and metering teams into early design so settings, communications, and data are ready when the switch is thrown. -
Address siting, water, and cooling early
Compute density and liquid cooling change site utilities and heat rejection. Coordinate water rights, wastewater, and thermal discharge limits with local agencies before layouts harden. If water is constrained, evaluate hybrid cooling and on site thermal storage that flattens auxiliary loads. These choices affect interconnection sizing and daily load shape. -
Modernize internal governance
Move from annual planning cycles to rolling windows. Establish a cross functional load growth desk that tracks real estate, permitting, construction progress, and customer commitments, then pushes updates into the forecast each quarter. Tie investment gates to that cadence so capital stays aligned with reality.
Bottom line
The fastest growing loads are no longer steady or evenly distributed. Teams that model hourly, think locally, and plan for multiple futures at once deliver faster with less risk. Utilities, owners, and builders who adopt granular, flexible, scenario based planning will avoid costly redesigns and late stage surprises. Those who continue to rely on coarse averages and single point plans will spend more time in rework and public explanations when the plan does not match the project.
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