When evaluating peak loads, load forecasters commonly focus on the severity of peak producing weather, considering meteorological factors such as temperature, humidity and wind speed. The peak loads are then weather adjusted to represent what the peak load would have been had the peak producing weather been normal.

However, the recent proliferation of AMI data facilitates deeper analysis. In particular, this data supports the decomposition of system peak loads into class-level and new technology (e.g. solar PV, EV and battery storage). Decomposing the peak load unveils the fact that while the severity of the peak producing weather is impactful, the time at which the peak producing weather occurs is also important, particularly in areas where the base load shape has a profound seasonal pattern.

In this blog, we will discuss two examples of the impact of timing on the winter and summer peaks, respectively.

Case 1: The Extreme Winter Peak
Case 1 considers a utility located in the heart of the Canadian prairies. Here, the winter low temperatures are frigid, approaching -40 degrees F. As most reasonable people would guess, this is a winter peaking utility. However, because the majority of customers in this area have natural gas – while the electric space heating load (driven by furnace fans) contributes to the winter peak – it is actually not the primary driving factor. Rather, the combination of business class, residential lighting and residential furnace fan loads contribute to drive the winter peak, which typically occurs just after 5 p.m.

Located far north from the equator, this area experiences significant shifts in hours of light.

  • On Dec. 21, the sun set at 4:56 p.m. local time
  • On Jan. 21, the sun set at 5:32 p.m. local time
  • On Feb. 21, the sun will set at 6:25 p.m. local time

The sunset time oscillation creates a narrow window in mid-December during which the base load is particularly high at 5 p.m. In addition, holiday lights also provide additional lift during this period. Therefore, a -40-degree F day which hits in this window drives a much stronger peak than it would if it occurs in February or March.

Case 2: The Extreme Summer Peak
Case 2 considers a utility located in the Northwest of the United States. Here, the summer temperatures approach 105 degrees F, and as one might expect, this is a summer peaking utility. However, while the air conditioning load is high, the combination of strong air conditioning and irrigation loads drives the peak.

Irrigation loads have a profound seasonal pattern, reaching peak levels in late June and early July. This produces a narrow window during which a hot day can produce an extremely strong peak. While the extreme, hot weather tends to hit in late July and August, it can occur earlier, coincident with the irrigation season peak.

Therefore, a 105-degree F day which hits in this window drives a much stronger peak than it would if it occurs in late July or August.

Quantifying Sensitivity
Both of these cases lend themselves toward bottom-up hourly approaches to peak forecasting. AMI data supports the disaggregation of system load data into the relevant components.

Weather simulations support the quantification of peak load sensitivities to both peak producing weather severity and timing. As the above cases demonstrate, both of these factors prove influential in determining both the 50:50 and 90:10 peak forecasts.

Andy Sukenik
Principal Forecast Consultant - Itron
Mr. Andy Sukenik is a Principal Forecast Consultant with Itron’s Forecasting Division. Since joining Itron, Mr. Sukenik specializes in medium, and long-term forecasting, as well as load research projects.

Mr. Sukenik has developed and implemented a multitude of medium and long-term forecasting systems for companies throughout the U.S. and Canada. Recently, Mr. Sukenik implemented a long-term forecasting solution for the Western Electricity Coordinating Council (WECC) which included 20-year ahead residential, commercial, and industrial end use forecasts of energy and peak for 42 balancing authorities, covering a geographical footprint that extends from Canada to Mexico and includes provinces of Alberta and British Columbia, the northern portion of Baja California, Mexico, and all or portions of the 14 Western states between.

In 2017, Mr. Sukenik supported the design and implementation of a long-term forecasting system for Pacific Gas & Electric, integrating the impacts of new technologies, such as Solar PV, Electric Vehicles, and Battery Storage, into a 20-year ahead forecasting framework which generates hourly forecasts by class of service, CCA, technology, and rate segment.

In addition, Mr. Sukenik has developed long-term load forecasts for CPS Energy, Dominion, DTE, Los Angeles Department of Water & Power, Oncor, PacifiCorp, PG&E, Redding Electric Utility (REU), San Diego Gas & Electric (SDG&E), and SMUD, among others. In addition, he has performed load forecast consultation for Alabama Power, American Electric Power, The California Energy Commission (CEC), Exelon, ERCOT, and PJM.

Mr. Sukenik conducts several of Itron's Brown Bag Seminars and Load Forecasting Workshops. His broad experience with short-term and long-term forecasting, statistical analysis, and database applications enable him to identify and communicate practical solutions to complicated forecasting problems. Prior to joining Itron, Mr. Sukenik worked in the utility industry for several years. He has worked for the FirstEnergy Corporation specializing in short-term modeling and daily energy and revenue tracking. Mr. Sukenik also spent time at San Diego Gas & Electric with a focus on end-use models for both the residential and commercial classes. Mr. Sukenik received a B.S. in Economics from Carnegie Mellon University.