Sometimes when I sit back and consider all the things that need to be done, I feel overwhelmed. The endless demands for regular work—like producing the annual forecast and monthly weather—get coupled with new requests for solar adoption forecasts, electric vehicle research and creatively using AMI data. This can create a big, hazy blur like this statue I saw in Austin during this year’s Energy Forecasting Group (EFG) Meeting.
From April 24 to 27, 76 participants from 49 companies met in Austin for the EFG meeting, creating the largest gathering of energy forecasters in North America. This year’s meeting included 15 industry presentations, two round tables and two pre-conference workshops covering a range of topics including (but not limited to) the following:
National and regional economic outlook
2018 annual energy outlook and energy trends
Overview of the EV market
Levering AMI data for analysis
Developing Monte Carlo simulations
Modeling solar and battery storage
While it’s difficult to isolate a single highlight, below are a few of my favorite activities from the event:
Dueling Economic Vendors. Technically, they weren’t dueling. In two separate presentations, Steve Cochrane of Moody’s Analytics and Martin Holdrich of Woods & Poole discussed the overall economic picture we use to drive our forecasts. Steve focused on the signs for the end of the “late stage expansion,” and Martin did a deep dive into the calculation of Gross Regional Product. Together, these presentations showed a picture of the future and how economists know what they know.
Weather Normalization Puzzle. Matt Croucher and Jacque Windle of Entergy shared their weather normalization puzzle and solution. In this presentation, they challenged our assumptions that weather data is unbiased and inherently trustworthy. By unraveling the mystery of a one-year sales decline, Matt and Jacque managed a lively discussion about weather stations raising the following questions: Should you use one, two or more stations?; Is there an optimal weather station weighting scheme?: Are airport weather stations more reliable?
Real Engineering. Austin Energy gave attendees a treat with a cooling plant tour. For all the economic and statistical modeling that we do, it is easy to forget the hard assets that sit behind the words “air conditioning,” “power plant” and “energy efficiency programs.” The cooling plant tour showed how Austin Energy efficiently distributes chilled water to its customer’s buildings through a set of heat exchangers for cooling. Walking under massive pipe, on top of a large ice tank, and next to giant pumps reminded me that our work is not just about writing numbers on paper (or a computer), but it is helping companies efficiently provide energy and services to people across North America.
After three days of listening, learning and discussing forecasting issues, I found the blur of my regular days becoming clearer. Just like the statue, stopping and closely examining each issue changes the blur into hundreds of individual and interconnected bicycles that are obscured from a distance. Taking the time to consider each challenge often provides clarity where it wasn’t before.
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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