Industry Insights

Finding the Right Tool

December 17, 2015

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 (http://www.appliance-standards.org/national).  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.

By Mark Quan


Principal Forecast Consultant


Mark Quan est consultant principal en prévisions au sein de la division des prévisions d'Itron. Depuis qu'il a rejoint Itron en 1997, M. Quan s'est spécialisé dans les solutions de prévision énergétique à court et à long terme, ainsi que dans les projets de recherche sur la charge. Quan a développé et mis en œuvre plusieurs systèmes de prévision automatisés pour prédire la demande système du lendemain, les profils de charge et la consommation au détail pour des entreprises aux États-Unis et au Canada. Les solutions de prévision à court terme comprennent des systèmes pour le « Midwest Independent System Operator » (MISO) et le « California Independent System Operator » (CAISO). Les solutions de prévision à long terme comprennent le développement et le soutien des prévisions à long terme (ventes et clients) pour des clients tels que « Dairyland Power » et « Omaha Public Power District ». Ces prévisions comprennent des informations sur l'utilisation finale et les impacts de la gestion de la demande dans un cadre économétrique. Enfin, Quan a participé à la mise en œuvre de systèmes de recherche de charge, notamment chez Snohomish PUD. Avant de rejoindre Itron, Quan a travaillé dans les secteurs du gaz, de l'électricité et de l'entreprise chez Pacific Gas and Electric Company (PG&E), où il a participé à la restructuration du secteur, à la planification de l'électricité et à la planification du gaz naturel. M. Quan est titulaire d'un master en recherche opérationnelle de l'université de Stanford et d'une licence en mathématiques appliquées de l'université de Californie à Los Angeles.


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