My desire to make popcorn for the evening of binge watching came to a sudden halt when the microwave door latch stuck.  Undeterred by this setback, I put on my repairman cap, opened my toolbox, prepared to open the appliance.  Within minutes, I ran into some odd looking screws – not the typical “slot” or “Phillips” head.  A quick internet search revealed that I had a “security hex socket” screw.  Without the proper screwdriver, I began to look for a different snack.

What do popcorn and microwaves have to do with forecasting?  Recently, I had a conversation with a utility budget forecaster tasked with developing the Integrated Resource Plan (IRP) forecast.  While the forecaster was familiar with budget forecasting models, he was concerned that the models could not be applied to the IRP forecast.

The budget models are simple econometric equations that capture customer and average use growth based on macroeconomic drivers, normal weather, and end-shift variables.  While economic and end-shift variables project growth and calibrate the model to the latest data, the weather variables capture heating and cooling response.  This structure is well suited to forecasts next year’s budget.  However, the forecaster’s concern is whether this type of model adequately captures the changes in the system over the next 30 years.

Applying a budget forecast models to a 30-year IRP forecast assumes that the relationship between the economic drivers and energy usages remain constant over the forecast horizon.  However, our intuition tells us that changes in energy efficiency standards and new technologies will change energy usage patterns creating deviations from this historic relationship.  A few potential changes are listed on the Appliance Standards Awareness Project website (  The list includes energy efficiency changes for at least 14 residential end-uses such as air conditioners, clothes dryers, clothes washers, and furnaces.

In order to adequately capture energy consumption changes over the 30 year planning horizon, future changes should be included in the forecast models.  Including end-use energy efficiency changes is one modification needed to adapt the budget model to a 30-year model.  Additionally, projections of behind-the-meter generation (solar) and new technologies (electric vehicles) should also be included capture risk scenarios.

After much discussion, my budget forecaster agreed that applying a budget forecast model for an IRP is like trying to apply a slot or Phillips head screwdriver to the security hex socket.  While we could force the issue, applying the wrong tools in any situation only leads to frustration.

Mark Quan on EmailMark Quan on Linkedin
Mark Quan
Principal Forecast Consultant - Itron
Mark Quan is a Principal Forecast Consultant with Itron’s Forecasting Division. Since joining Itron in 1997, Quan has specialized in both short-term and long-term energy forecasting solutions as well as load research projects. Quan has developed and implemented several automated forecasting systems to predict next day system demand, load profiles, and retail consumption for companies throughout the United States and Canada. Short-term forecasting solutions include systems for the Midwest Independent System Operator (MISO) and the California Independent System Operator (CAISO). Long-term forecasting solutions include developing and supporting the long-term forecasts of sales and customers for clients such as Dairyland Power and Omaha Public Power District. These forecasts include end-use information and demand-side management impacts in an econometric framework. Finally, Quan has been involved in implementing Load Research systems such as at Snohomish PUD. Prior to joining Itron, Quan worked in the gas, electric, and corporate functions at Pacific Gas and Electric Company (PG&E), where he was involved in industry restructuring, electric planning, and natural gas planning. Quan received an M.S. in Operations Research from Stanford University and a B.S. in Applied Mathematics from the University of California at Los Angeles.