Get a Load of This

The report addresses the surge in US electricity demand, noting that utility integrated resource plans covering 48% of sales expect load to grow 20% from 2023 to 2035. It argues that traditional forecasting is inadequate for new large loads—specifically data centers and industrial reshoring—which possess unique load shapes, high uncertainty, and 'flight risk'. The authors recommend that regulators shift toward scenario-based forecasting, increase the frequency of updates (e.g., quarterly), and implement 'least-regrets' capital investments and risk-shifting tariffs to protect ratepayers from the costs of forecast errors.

• Electricity demand projections in the US are rising rapidly; utility integrated resource plans (IRPs) covering 48% of sales expect load to grow 20% between 2023 and 2035, a significant increase from the 7% growth projected in January 2021.
• New large load drivers, such as data centers and advanced manufacturing, introduce unique challenges including high forecast uncertainty, specific load shapes, flexibility potential, and 'flight risk'—the likelihood a load will move if conditions are unfavorable.
• Data centers present extreme forecasting uncertainty due to the volatility of AI business models and hardware cycles; in Virginia, they were estimated to comprise 25.6% of electric load in 2023, with some projections reaching 46% by 2030.
• Industrial reshoring, driven by the clean energy transition and Inflation Reduction Act incentives, is increasing load; these loads typically have lower forecasting uncertainty than data centers due to longer siting and permitting timelines.
• The report recommends that utilities move away from single deterministic forecasts in favor of scenario-based or stochastic methods that communicate load as ranges with attached probabilities.
• Regulators can improve forecast accuracy and accountability by requiring more frequent reporting (e.g., quarterly or semiannually) and implementing 'backtesting' to compare past forecasts with actual outcomes.
• To manage uncertainty, regulators should prioritize 'least-regrets' investments—those that are fast, affordable, and flexible—such as energy efficiency, virtual power plants (VPPs), and grid-enhancing technologies.
• Tariff design can be used to shift the financial risk of forecast errors from general ratepayers to the large customers driving the growth, through mechanisms like capacity reserve charges or exit fees.

APA

Sward, J., Shwisberg, L., Stephan, K., & Becker, J. (2025). Get a Load of This. RMI. https://rmi.org/app/uploads/dlm_uploads/2025/03/Get_a_load_of_this_Load_Forecasting.pdf

Chicago

Sward, Jeffrey, Lauren Shwisberg, Katerina Stephan, and Jacob Becker. Get a Load of This. RMI, 2025. https://rmi.org/app/uploads/dlm_uploads/2025/03/Get_a_load_of_this_Load_Forecasting.pdf.

Wikipedia

{{cite report |last1=Sward |first1=Jeffrey |last2=Shwisberg |first2=Lauren |last3=Stephan |first3=Katerina |last4=Becker |first4=Jacob |title=Get a Load of This |publisher=RMI |date=February 2025 |url=https://rmi.org/app/uploads/dlm_uploads/2025/03/Get_a_load_of_this_Load_Forecasting.pdf |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{sward2025get, author = {Sward, Jeffrey and Shwisberg, Lauren and Stephan, Katerina and Becker, Jacob}, title = {{Get a Load of This}}, institution = {RMI}, year = {2025}, month = feb, url = {https://rmi.org/app/uploads/dlm_uploads/2025/03/Get_a_load_of_this_Load_Forecasting.pdf}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }