Create notebook exploring bias in load growth projections

[zaneselvans]

Overview

Regulated utilities have a habit of overestimating load growth, in order to justify expanding their rate base. @arengel at RMI did a little exploration of this in 2017 in The Billion Dollar Costs of Forecasting Electricity Demand and it has become relevant once again with the rush to build gas plants and delay coal plant retirements in order to serve "hyperscale" data centers and AI training. To what extent are utilities simply taking advantage of the hype around this narrative to justify "emergency" build out of new fossil infrastructure? Data reported by planning areas in the FERC-714 can provide some context, and would also provide a nice example analysis notebook for our PUDL Examples repo.

Outline

• Create a notebook using the PUDL Dataset on Kaggle
• Use the projection data in the core_ferc714__yearly_planning_area_demand_forecast table to analyze the biases in demand forecasts.
• Try different ways of visualizing the data to make it clear what's going on. The RMI plot above is one possibility.
• Because different planning areas have wildly different levels of demand, it will probably make sense to normalize the projections, looking at them relative to actual demand, rather than in absolute MW or MWh units.
• One complication that will come up is the territory served by a given respondent can change from year to year. You can get a sense of how this might impact the results by looking at out_ferc714__summarized_demand and if necessary, make maps of the service territories and see how they evolved over time with the out_eia861__yearly_utility_service_territory and out_eia861__yearly_balancing_authority_service_territory tables and the geometries in the Census DP1 database.
• The names of the planning areas (which are often coincident with utilities or balancing authorities) can be merged in from the core_ferc714__respondent_id for readability.
• Being able to look at the prediction record of a single respondent as well as ensembles of respondents would be helpful.
• The actual peak demand numbers can be found in the historical hourly data: out_ferc714__hourly_planning_area_demand (only available as Parquet) -- you'll need to look at what the definition of winter and summer peak demand are.

Questions

• Are some respondents consistently better than others at predicting their actual future demand?
• When respondents are bad at predicting future demand, is the error random? Or is it systematic?
• Are whole regions better or worse at predicting future demand? Does that correlate with whether the utilities are competitive or regulated monopolies?
• Has the quality of predictions changed over time?
• Has the level of load growth being predicted changed over time?

Background Reading

• https://rmi.org/billion-dollar-costs-forecasting-electricity-demand/
• https://rmi.org/utility-planning-update-q3-2024/
• https://energyinnovation.org/2024/06/28/lets-stop-worrying-over-load-growth-and-get-serious-about-solutions/
• https://energyinnovation.org/wp-content/uploads/2024/03/MEETING-GROWING-ELECTRICITY-DEMAND-WITHOUT-GAS.pdf
• https://www.utilitydive.com/news/regulators-dont-panic-and-rush-to-gas/712435/
• https://xenetwork.org/ets/episodes/episode-226-load-growth-shenanigans/
• https://gridstrategiesllc.com/wp-content/uploads/2023/12/National-Load-Growth-Report-2023.pdf
• https://doi.org/10.1016/j.joule.2021.05.007
• https://www.nrdc.org/bio/jackson-morris/southeast-crossroads-bad-gas-bet-or-clean-energy-boon