MetrixND, MetrixLT and Forecast Manager – Version 7.0 Releases

We are excited to announce the release of three new software versions of our core forecasting suite – MetrixND, MetrixLT and Forecast Manager. These are major releases with expanded capabilities that will improve the overall user experience.

MetrixND, first released in 1997, is the power-house of Itron’s forecasting software suite and is used daily by hundreds of users for rapid development of accurate forecasts. Powerful forecasting techniques coupled with visualization tools are invaluable when analyzing data. The improved project explorer in version 7.0 provides users with even more flexibility when developing forecasting variables and models.

MetrixLT, a specialized companion product to MetrixND, is used for developing hourly and sub-hourly load forecasts to support utility generation, transmission and distribution planning. MetrixLT can be used to generate load shape forecasts by end use, class of service, system or other user defined segments for the current year or 50 years out. Users will value the newly enhanced load-shape calculation capabilities and improved user interface.

Forecast Manager version 7.0 brings numerous new functions to support advanced billing cycle calculations, construction of daily weather scenarios, calibration of hourly load profiles, and daily tracking calculations. Upgraded grids and graphs are also included to further improve the user experience. To be successful, utilities require systems that automate processes and provide data and reports that management can count on. Forecast Manager brings together sales forecasting, data management and reporting into a single integrated application. All the sales, customer and weather data required for forecasting and variance analysis are incorporated into a single database. Forecast Manager streamlines the input of key data for forecasting and analyzing sales trends. Your MetrixND® forecast and weather impact models then link directly to the Forecast Manager database.

Itron forecasting products are the most widely used forecasting software in the energy industry. They are used for short-term natural gas and electricity forecasting to support day-ahead and real-time operations, retail scheduling, short-term renewable generation as well as for long-term to support portfolio management and procurement, financial planning and budgeting, and integrated resource planning.

To learn more about Itron’s forecasting products and services, we encourage you to visit the forecasting webpage, register for our free brown bag sessions and subscribe to our blog.


July 15 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on North American and European Electricity Consumption

This data is for March through June 2020

As previously discussed in the first of this blog series on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts by region as lockdown policies are enacted to help reduce the spread of COVID-19.

In addition, the Itron Forecasting Team is now including the Southwest Power Pool (SPP) and a select set of European countries to build a picture of the load impacts by region. To assess the load impact of COVID-19 mitigation strategies, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Across Europe and North America, the biggest estimated load reductions occurred in April with an estimated reduction in average daily load between -7.2% and -11.7%.

Estimated Daily Average Energy Impact Wedge

For a detailed summary of the estimated load impacts, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on the trends.

Subscribe to our blog to be notified of new posts, or contact us at forecasting@itron.com if you have further questions.


Where Are the Electric Vehicles?

I’m the last one in my family to own a Tesla. This is an odd statement until you understand that I live in coastal California where a Tesla is as a popular as a Honda Accord. Just yesterday, I counted at least 10 Teslas on my 5-mile commute home.

If I used my commuting observations as an indicator for electric vehicle saturations, my forecast would majorly distorted. Whether it’s overstating saturations (like me) or understating saturation (like some of you), our personal experience is biased and needs a healthy injection of unbiased data.

So, how many EVs are in my area?

The Auto Alliance is an industry trade group that provides statistics on the auto industry. While you can view data by any state, I drilled into California.

Scrolling down to the bottom of the California data, is a section labelled “Registrations”.

And there it is. In 2018, California had 31.5 million registered vehicles and 16.1 million were cars. There are 262,481 electric vehicles which is 0.83% of all vehicles. If you assume all electric vehicles are cars (not a bad assumption), then the ratio is 262,841 to 16,139,269, or 1.6% of cars.

For many, state-level data are not refined enough. In this case, drill down to the congressional district level and do some math. Once you figure out how districts map your service territory, you can get close to what’s registered in your service territory.

On second thought, my perception of electric vehicle ownership in my family isn’t 75% (1 family out of 4), it’s really 30% (3 vehicles out of 10). That make me feel a bit better. After all, it’s all about getting good data.


Why the Standard 65 Degree Day Base May Not be the Best Choice

The National Oceanic and Atmospheric Administration (NOAA) often defines heating and cooling degree days using a 65-degree base. A simple explanation of how degree days are calculated to ensure everyone understands what this means is: if the average temperature for the day is 55 degrees, there are 10 heating degrees and zero cooling degrees. Degree day base 65 formulas are:

Heating Degree Day (base 65) =max ⁡(65 - Temperature,0)
Cooling Degree Day (base 65)=max⁡ (Temperature - 65,0)

But if heating degree days are calculated using 55 as a base, there are no heating degree days. So, how you define degree days matters.

The need to use degree days in the context of energy forecasting is due to the non-linear relationship between weather and energy. Above a certain temperature, energy usage increases as temperature increases, and below a certain temperature, energy usage increases as temperature decrease. Defining heating and cooling degree days in base 65 assumes 65 degrees is the temperature at which the switch from heating to cooling occurs.

The relationship between energy usage and weather can best be seen using a scatter plot – the Y-axis is monthly energy per day and the X-axis is monthly average temperature. The data illustrated is based on total system loads for a service area in the Northern U.S. Examining the graph, it becomes apparent that heating does not occur at 65 or even 60 degrees. The heating does not begin until average temperatures reach 55 degrees. Cooling, on the other hand, begins when temps are as low as 60 degrees. The slope on the heating and cooling side changes with temperatures, implying that the impact of a CDD or HDD differs depending on how hot or cold the day is. The impact on energy by an additional degree of temperature is greater when the average temperature is 75 degrees compared to 65 degrees.

A simple modeling exercise can prove the benefits of using customized degree day breakpoints. Using the same dataset from the scatter plot, a monthly regression model is estimated using 10 years of data. The initial specification uses a constant – heating degree days base 65 and cooling degree days base 65 – and a trend variable to account for overall system growth. The model is able to explain 93% (Adj. R2) of the monthly variation in energy per day with just these four variables. This model has a Mean Absolute Percent Error (MAPE) of 2.08%, strong model statistics.

Based on the scatter plot, we can easily see that a 65 base is not the best choice for this service area. In the second specification, the weather variables are changed to HDD base 55 and CDD base 60. This small change increases the adj. R2 to 0.967 and decreases the MAPE to 1.46%. The final specification uses multiple degree day spines – CDD base 60 and CDD base 70 for cooling and HDD base 45 and CDD base 55 for heating. All variables are statistically significant. This results in an adj. R2 of 0.971 and a MAPE of 1.37%. Model stats are listed in the table below:

When multiple splines are required, the coefficients can be used to create a single weighted heating or cooling degree day variable for use in forecasting models. In this simple example, only monthly energy and weather was used. Daily data would provide an even greater resolution of the relationship.


June 18 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies

This data is for March 15, 2020 – June 14, 2020.

As previously discussed in the first blog of this series on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts of COVID-19 mitigation policies by region. To do this, actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

Total ISO Impacts: Total ISO load is computed as the sum of the hourly loads across the nine ISOs for which hourly load data is publicly available. The figure below shows the average daily load reduction pre- and post- reopening of the economy for the total and individual ISOs.

  • Pre-Reopening of the Economy: For the aggregate ISO load, the average estimated impact is a reduction in average loads of about -7.5% over the period March 29 through May 17 when most COVID-19 mitigation policies were enacted.
  • Phased Reopening of the Economy: the period of May 18 through June 14 marks the start of the phased re-opening of the of the economy. Over this period, the reduction in average loads is estimated to be -3.4%, which represents a gain in average daily power consumption of about 4.0%.

For a detailed summary of the estimated load impacts for each region, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on these trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.


Impact of Climate Change on Long-Term Electricity Demand

The next Itron Forecasting Brown Bag for 2020 will be hosted on Tuesday, June 16 and is entitled "Impact of Climate Change on Long-Term Electricity Demand." This free webinar will present a recent analysis of weather trends as part of the New York Climate Impact Study, which shows a steady increase in average temperatures consistent with global climate models. This includes a discussion on the trends and an approach for incorporating them into your long-term load forecast.

Participation is free, but prior registration is required. Each seminar lasts approximately one hour, allowing 45 minutes for the presentation and 15 minutes for questions. Seminars start at noon Pacific-time. If you can’t attend a seminar or if you missed one, don’t worry! Your registration ensures that a link to the recording will be sent to you automatically approximately one week after the seminar date, plus you will get a link to watch any of this year’s previous webinars.

Register today at www.itron.com/forecastingworkshops.

Itron’s Forecasting group has conducted webinars on a variety of forecasting and load research-based topics for many years. All of our past webinars were recorded and are available in a YouTube library.


June 3 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies

This data is for March 15, 2020 – May 31, 2020

As previously discussed in the first of this blog series on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts by region. The actual loads of when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

The estimated load impacts for the weeks starting on March 15 are presented in the table below.

  • Pre-reopening of the Economy. For the aggregate ISO load, the average estimated impact is a reduction in average loads of about -7.5% from March 29 through May 17, when most COVID-19 mitigation policies had been enacted. The biggest impact is estimated for the morning hours with an average reduction of -9.6%. This could reflect the fact that despite more people staying at home, there is not enough residential HVAC load in the morning hours to offset the reduction in non-residential loads. The afternoon hours have an estimated reduction of -7.2%, and the evening hours have an estimated reduction of -7.1%. The relatively lower impact in the afternoon hours could reflect a higher than usual residential air conditioning load that is offsetting the reduction in non-residential loads.
  • Phased Reopening of the Economy. The period May 18 through May 31 marks the start of the phased re-opening of the of the economy. Over this period, the reduction in average loads is estimated to be -6.2%, which shows a trend toward higher levels of power consumption. The morning impact is estimated to be -8.4%, which reflects both a re-opening of many businesses coupled with a large portion of the population continuing to be at home. The afternoon and evening impacts also show a trend toward normal usage levels with an average load reduction of -5.0% and – 5.5%, respectively. On average, it is estimated that the early phases of the re-opening of the economy have been associated with roughly a 1.3% increase in power consumption levels.

Starting around the end of April, the California Independent System Operator (CAISO) and the Electric Reliability Council of Texas (ERCOT) control regions experienced significant temperature increases that led to a rise in air conditioning loads. The increased temperatures have uncovered an interesting side effect of the shelter-in-place policies, specifically higher than normal weather response to hot temperatures. The CAISO and ERCOT analysis highlight the additional air conditioning load associated with people staying at home. In both regions, this has led to peak loads greater than expected. Further, the peak load days are associated with higher than expected ramp rates throughout the morning, afternoon and evening hours. If the economy re-opens with a large portion of the population remaining at home, it is plausible that this summer utilities will experience higher than normal peak loads and ramping events.

For a detailed summary of the estimated load impacts for each region, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on the trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.

Estimated Load Impact by ISO Control Region and Time-of-Use Period: March 15 to May 31, 2020


May 20 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies

This data is for March 15, 2020 – May 17, 2020

As previously discussed in the first of this series on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts of COVID-19 by region. Actual loads when many of these policies began are compared to baseline loads without COVID-19 policy impacts.

The estimated load impacts for the weeks starting March 15 are presented below. For the aggregate ISO load, the average estimated impact is a reduction in average loads of about -7.5% over the period March 29 through May 17 when most COVID-19 mitigation policies were enacted. The later part of this period also covers the start of the re-opening of the North American economy. The biggest impact is estimated for the morning hours with an average reduction over this period of -9.6%. This could reflect the fact that despite more people staying at home, there is not enough residential HVAC load in the morning hours to offset the reduction in non-residential loads. The afternoon hours have an estimated reduction of -7.2% and the evening hours have an estimated reduction of -7.1%. The reduced impact in the afternoon hours could reflect a higher than usual residential air conditioning load that is offsetting the reduction in non-residential loads.

Beginning around the end of April, the CAISO and ERCOT control regions experienced significant temperature increases that led to a rise in air conditioning loads. The increased temperatures have uncovered an interesting side effect of the shelter-in-place policies, specifically higher than normal weather response to hot temperatures. The CAISO and ERCOT analysis highlights the additional air conditioning load associated with people staying at home. In both regions, this has led to peak loads greater than expected. Further, the peak load days are associated with higher-than-expected ramp rates throughout the morning, afternoon and evening hours. If the economy re-opens with a large portion of the population remaining at home, it is plausible that this summer utilities will experience higher than normal peak loads and ramping events.

For a detailed summary of the estimated load impacts for each region, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on these trends.

Subscribe to our blog to be notified of new posts, and contact us at forecasting@itron.com if you have further questions.


Observations on a Permanent Calendar: As Time Goes By

The common civil calendar used around the world is the Gregorian Calendar, which was adopted in 1582 to replace the Julian calendar. The impetus to replace the Julian calendar was that the dates were shifting with respect to the equinoxes. Both of these calendars are solar calendars; that is, they reckon time based upon the earth’s orbit around the sun. Because the earth’s orbit around the sun is not exactly 365 days, we also use leap years. In the Julian calendar, all years that are evenly divisible by 4 are leap years. In the Gregorian calendar, however, a year is a leap year if it is evenly divisible by 4, but not if it is divisible by 100, unless it is also divisible by 400. Thus, the year 1900 was not a leap year, but 2000 was a leap year. This small adjustment has helped to keep the Gregorian calendar in sync with the seasons.

For most purposes of reckoning time, the Gregorian calendar is sufficient, providing a year that is 365.2425 days long on average, but, the calendar is odd for a number of reasons.

  1.  The days of the week move around with respect to the date. That is, Jan. 1 is sometimes a Monday and it is sometimes a Tuesday.
  2. The number of days per month ranges from 28 to 31 in a less-than-obvious way. You need your knuckles or a catchy song to remember the right number: “Thirty days has September… April, June and November…”
  3. The adjustment for leap year (i.e. Feb. 29) happens in the second month. Is that really the best place for it? It seems like a more obvious candidate would be…maybe the last day of the year?
  4. October is the 10th month, even though the prefix ‘oct’ comes from the Greek word for eight. December is the 12th month, even though the prefix ‘dec’ comes from the Greek word for ten (this particular issue just offends my aesthetic sensibilities).

To be fair, there are historical and esoteric reasons to explain all of these and you can research them if you are so inclined.

From the perspective of energy forecasting, the Gregorian calendar creates challenges primarily because of the dates moving with respect to the weekday. While we have many holidays in the U.S. that are tied to a particular weekday (e.g., Memorial Day and Labor Day are always on a Monday), there are other holidays that are tied to particular dates (i.e., Independence Day is always on July 4 and Christmas is always on December 25), whose energy impacts differ depending upon the weekday on which they fall. This particular issue causes no small amount of consternation and hand wringing.

Professors Steve Hanke and Richard Henry at Johns Hopkins have formulated an alternative calendar, not surprisingly called the Hanke-Henry Permanent Calendar (as a side note, I’ve always wanted something named after me: The Simons Coefficient, The Simons Statistic or the Rich Effect. But I digress). The salient features of their calendar are:

  1. Jan. 1 is always a Monday. Thus, holidays always fall on the same weekday: July 4 is always on a Thursday and Christmas is always on a Monday.
  2. The year is broken into four quarters – each of which has three months – with days per month being 30, 30 and 31 (for a total of 91 days) respectively within the quarter.
  3. To keep the calendar in-sync with the seasons (i.e., the same reason we have a leap year), there is an extra weeklong month (which they call Xtr) after December, when the corresponding Gregorian year starts or ends on a Thursday.
  4. October is still the 10th month rather than the 8th month, but I can live with that.

In our arena, these features alleviate the problem of varying a holiday’s load-impact due to shifting weekdays. From a long-term forecasting perspective, it helps remove the problem with months having varying numbers of weekdays and weekends. And, what about that bump in your energy forecast for leap year Februarys? While it is correct to put more energy in the 29-day February (because it has 3.57% more days than a 28-day month), it always creates an oddity that requires some explanation. February now has 30 days always. Problem solved.

There are a number of other features that I encourage you to investigate on your own. One of their suggestions is that we all use the same clock; everybody would be on UTC (Universal Coordinated Time), although local areas would still operate based on their location. For instance, people in the Eastern Time Zone would wake in the morning at roughly 11 a.m. UTC (which corresponds to 6 a.m. EST). While this seems convenient and unambiguous for international business (“I’ll call you tomorrow at 12 p.m. UTC”), it still seems confusing to me.

Now, this is about as likely to be adopted as we are to cease using the QWERTY keyboard, to convince the British to drive on the right side of the road, or to adopt the metric system in the U.S. Still, it is an outstanding idea that is worthy of attention and I sincerely applaud Hanke and Henry for their noble efforts.


May 6 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies

This data is for March 15, 2020 – May 2, 2020

As previously mentioned in my blog post on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts of COVID-19 by region. We do this using actual loads from when many shelter in place policies began and compare them to baseline loads without COVID-19 policy impacts.

The estimated load impacts for the weeks starting March 15 are presented below. Since April 1, the estimated load reduction in aggregate ISO loads is about -7.9%. Areas such as New York and California have been particularly hit hard with an estimated average load reduction of a little over -9% since the beginning of April.

For a detailed summary of the estimated load impacts for each region, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on these trends.

Subscribe to our blog to be notified of new posts. You can contact us at forecasting@itron.com if you have any questions.


April 23 Update: Latest Trends in Estimated Load Impacts of COVID-19 Mitigation Policies

This data is for: March 22, 2020 - April 18, 2020

As previously mentioned in the forecasting blog post on April 13, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) to build a picture of the load impacts of COVID-19 by region. We do this using actual loads from when many shelter in place policies began and compare them to baseline loads without COVID-19 policy impacts.

The estimated load impacts for the weeks starting March 15 are presented in the table below. Since April 1, the estimated load reduction in aggregate ISO loads is about -7.5%. Areas such as New York and California have been particularly hit hard with an estimated average load reduction of a little over -10% since the beginning of April.

For a detailed summary of the estimated load impacts for each region, go to the forecasting website to download the latest COVID-19 Load Impact memo.

The Itron Forecasting Team will continue to post updated summary blogs and corresponding memos on the trends.

Subscribe to our blog to be notified of new posts and contact us at forecasting@itron.com if you have further questions.


Toward an Optimal Combined Load Forecast for System Operations

Deep penetration of non-grid connected renewable generation and storage, electric vehicle charging, smart load control and time-of-use rates create greater load volatility, which in turn, leads to eroding operational load forecast performance. To improve the system operator’s confidence with the load forecasting process, there has been a movement toward developing and presenting an ensemble of load forecasts.

The ensemble could include forecasts designed to handle the impact of rooftop solar PV and electric vehicle charging, forecasts that incorporate the impact of time-of-use pricing and smart load control, and load forecasts produced under alternative weather forecasts. If the alternative load forecasts are clustered closely around each other, then system operations may have greater confidence in the system conditions predicted by the ensemble. On the other hand, a forecast ensemble with a wide range could raise doubts about the forecasted system conditions, leading to system operators taking actions to hedge against the worst-case scenario. In effect, the forecast ensemble quantifies the plausible range of loads given the uncertainty around future meteorological conditions such as temperatures, wind and solar conditions as well as uncertainty around price sensitive loads and load control actions.

Within this new world of ensemble forecasting, there remains the reality that most downstream applications (e.g., transmission and distribution energy management systems and market models) require a single load forecast as an input. This means the load forecasting process needs a way of combing the alternative forecasts into a single “optimal” forecast that is then used for downstream processing.

Dr. Frank A. Monforte has authored a white paper that provides a high-level review of some of the econometric/operations research and data science literature on combining forecasts, and puts forth a recommendation for how to develop an optimal forecast specific to the problem of operational load forecasting.

To read the paper, go to our forecasting page at http://www.itron.com/forecasting.


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