Annual Energy Forecasting Survey

Itron’s forecasting group is in the process of conducting their Annual Energy Survey. The survey will close on June 1, 2021. As we have done in previous years, we collect information to assess industry growth expectations and forecast accuracy for electricity and natural gas. We continue to focus on forecasts of customer and sales growth, weather normalized growth and forecast accuracy. All survey participants will receive a summary report. Final results will also be presented during our brown bag seminar on September 21. Register at www.itron.com/forecastingworkshops.

If you have not participated before, you can view the 2019 report. Utility specific data is NOT disclosed. Surveys like this benefit the entire energy community by supplying valuable knowledge. We encourage you to participate and be included in this important survey this year. Contact Paige Schaefer at paige.schaefer@itron.com to receive a link to participate.


Demystifying Residential Miscellaneous Usage

In the residential Statistically Adjusted End-Use (SAE) model framework, the miscellaneous end-use is the single largest long-term driver of residential average electricity usage. Depending on the part of the country, it accounts for 20-30% of residential usage. Compounding its significance, miscellaneous is also the only end use showing significant growth. The graph below shows U.S. miscellaneous average use per household.

So, what is included in miscellaneous?  To begin with, the miscellaneous end use consists of a set of specific end uses that include:

  • Rechargeable equipment
  • Ceiling fans
  • Coffee makers
  • Dehumidifiers
  • Microwave ovens
  • Pool heaters
  • Security systems
  • Spas
  • Wine coolers
  • PC and their peripherals

In addition to the above, a major component of the miscellaneous is what we call Electric Other (Other). The graph below compares the above list of specifically identified end-uses (Specific) and Other.

Other is by far the largest part of miscellaneous electric use and contributes virtually all the growth in residential use per customer.

Household items that are included in Other are things like aquariums, electric toothbrushes, electric can openers, heated driveways and anything else you can think of that does not fall into the specific category. It is effectively the residual: Other = total residential usage – identified end-uses.

There is no denying miscellaneous other use exists, but is it having too much impact on residential average use forecast? Do we need it in our forecast, or can we make do without it?

During the fifth presentation of the upcoming 2021 Energy Forecasting Virtual Meetings on April 21-23, there will be a discussion on the latest updates to the Statistically Adjusted End-Use (SAE) model framework and the miscellaneous end-use category. Register today to learn more!


2021 Annual Energy Forecasting Virtual Webinars

Itron's regularly scheduled in-person Annual Energy Forecasting Meeting and Workshops in New Orleans have been rescheduled to April 2022.

Similar to last year, Itron is hosting several webinars throughout the originally planned dates (April 21-23) to continue to share the latest forecasting and economic information with you. We hope that you will join us! Times are listed in Pacific Time.

Wednesday, April 21

8 - 9 a.m. - EIA Residential and Commercial Updates – Kevin Jarzomski, Energy Information Administration

12 - 1 p.m. - Economic Outlook - Ryan Sweet, Moody’s Analytics

Thursday, April 22

8 - 10 a.m. - COVID-19 Effects Panel Discussion

  • Brian Childers, TVA
  • Randy Holliday, AEP
  • Markus Leuker, DTE
  • Todd Mobley, Duquesne

12 - 1 p.m. - EV Market Outlook and Load Impacts

Friday, April 23

8 - 9:30 a.m. - Closing Session, SAE and Software Updates

Participation in these webinars is free, but prior registration is required. If you can't attend or miss a webinar, each will be recorded and a link to the recordings will be sent to you automatically after the event.

For more detailed information on these webinars and other forecasting events, please go to our forecasting workshop page at http://www.itron.com/forecastingworkshops.


Real-time AMI Data Helps Utilities Anticipate Power Needs

COVID-19 has changed the way people around the world live and work. As a result of shelter in place mandates, office buildings closed, and homes quickly became makeshift offices. This shift resulted in considerable changes to both commercial and residential power consumption. With Advanced Metering Infrastructure (AMI) data, utilities can monitor and measure how much and energy is used. Itron’s Forecasting team has been closely monitoring the load impacts of COVID-19 mitigation strategies and I recently contributed the following article to RTInsights.

COVID-19 stay-at-home mandates and the shift toward a remote-work lifestyle have led thousands of office and municipal buildings across the country to remain closed for the majority of 2020. While some businesses started to reopen late-summer 2020, spikes in cases in November and December once again prompted stricter community restrictions and business closures. Real-time data from Advanced Metering Infrastructure (AMI) can help.

The COVID-19 pandemic has led to significant fluctuations in commercial power consumption. Although the end is finally in sight, the impact of COVID-19 will affect the way we live and work moving forward. With more citizens staying at home, potentially long-term, it is more important than ever for utility providers to adjust operations to meet an offsetting increase in residential power demand.

How does the shift toward a remote workforce affect power consumption and demand?

During a typical workday before the COVID-19 pandemic, businesses and homes begin to turn the lights on and consume power around 5 a.m. With stay-in-place policies, more people started working from home, eliminating their daily commute, with some using that time to start their morning routines later. This causes the aggregate system load of utilities to begin ramping up later in the morning. Not only does this result in utility providers having to adjust power supply operations to meet a shift in demand, but it can also lead to a shift in consumers’ peak load hours.

Peak load hours are the points in the day at which a city and its residents are consuming the most electrical power. According to energy usage data prior to broad stay-at-home policies and COVID-19, peak load hours tended to be late afternoon when the combination of residential and non-residential air conditioning loads were running at maximum power to cool down homes and workplaces. As a result of the pandemic, commercial buildings that are largely unoccupied have lower air conditioning loads, leading to a shift in peak load hours to earlier in the day as residential homes cool throughout the day.

As utility providers produce more power during these peak load hours, there is typically a higher billing rate associated with power consumption during these peak hours. This could result in higher than expected end-of-month energy bills for consumers working from home.

Read the full article to learn how utility providers monitor power consumption and can leverage post-COVID-19 AMI data moving forward.


March 2021: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March 2020 through Mid-March 2021

As previously discussed in the first of this blog series on April 13, 2020, as lockdown policies are enacted to help reduce the spread of the coronavirus disease (COVID-19), the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) 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 2020 with an estimated reduction in average daily load between -12.3% and -7.2%. Google Mobility data suggests a new normal of more people at home than pre-COVID.

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 and contact us at forecasting@itron.com if you have further questions.

Estimated Daily Average Energy Impact Wedge


New Forecasting Brown Bag Webinars

Itron’s Forecasting group has conducted webinars on a variety of forecasting and load research-based topics for many years and continues to host new webinars every quarter. Past webinars were recorded and are available in a YouTube library.

To watch more recent recordings, you must be registered for the current year’s webinars. Our first brown bag of 2021 is on Tuesday, March 23 and is entitled “Accounting for COVID-19 Impacts in Budget Sales Forecasts When Economics Aren’t Enough”. COVID-19 has presented new challenges in forecasting with increases in residential sales and decreases in commercial sales. This brown bag session will review the impact of the pandemic on electricity usage, present our “COVID-19 modeling tricks” for estimating COVID-19 sales and revenue impacts and consider how to incorporate the future impacts as we transition back to “normal”.

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 PDT.  If you are unable to attend a seminar or missed one, don’t worry! Your registration ensures that a link to the recording will be sent to you automatically.

Register Now!


January 2021: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March 2020 through January 2021

As previously discussed in the first of this blog series on April 13, 2020, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) 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 2020 with an estimated reduction in average daily load between -12.3% and -7.2%. This instance of the memos extends prior analyses that presents estimates of the load impacts by region, month, and the time of use period by adding pre- and post-hourly load shapes by season. A comparison of the hourly shapes provides a deeper understanding of how power consumption is evolving given current economic conditions and COVID-19 restrictions.

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


Snow Your Enemy

I have lived in New England my entire life. You may be unaware of this fact, but it snows here sometimes.

Snow can be wonderful. As a child, I shoveled my fair share of driveways. I delivered newspapers in the snow. And many times, I walked through a neighbor’s yard and climbed through a hole in the fence to get to a nearby school, which had a perfect hill for sledding.

Notwithstanding those idyllic images, snow creates unique challenges for solar generation forecasting.
There are two broad categories of solar forecasting. The first category is behind-the-meter (BTM) solar generation, which typically consists of rooftop solar panels. BTM has its own set of challenges including lack of visibility into the data itself. In most cases, the amount of energy that is generated, consumed, and/or sent back to the grid is not directly available to grid operators (at the Independent System Operator level) and it may or may not be available to utility distribution system operators. Because the data is often unavailable, the danger is that grid operators may over-commit generation resources on sunny days (when the BTM is generating a lot of power) and under-commit generation resources on cloud days (when the BTM is not generating much power at all).

The second category consists of grid-connected solar plants. In the area where I live, these facilities are often along the sides of highways. The panels tend to be fixed (i.e., they do not track the sun) and they face southward for maximum exposure to the sun as it traverses the southern sky from east to west.

In some cases, the historical generation for these grid-connect solar plants is available to us, along with the system’s total capacity. And, we have access to the useful weather concepts, such as:

  • Drybulb temperature
  • The ambient temperature
  • Cloud cover – the percent of the sky that is covered by clouds, ranging from 0 to 100, where 0 means a totally clear sky and 100 indicates a totally cloudy sky.
  • Solar irradiance – the amount of energy striking a surface on the earth, measured in watts-per-square-meter. The concepts are often Global Horizontal Irradiance (GHI) or Plane of Array (POA). A related concept is the maximum solar irradiance, which can be calculated from the latitude and longitude of a location. This does not account for the presence or absence of clouds, but merely provides the maximum value at the location and time based on a totally clear sky.

We tend to not have data on snow accumulation. Even if we did have that data, I am not sure that it would be especially useful. If you do not live in a snowy region, this may come as a surprise to you – the sky is typically cloudy when snow is falling. That is useful information because the forecast models would perceive a cloudy day and the generation forecast would be lower.

Eventually (and thankfully), the snow stops falling, at which point the sky may become totally clear. The forecast models may then predict a much higher generation forecast. Unfortunately, that forecast is likely to be wrong because of the accumulated snow covering the panels.

I visited a nearby solar array and I took the following photograph a full 24-hours after the snow stopped falling. The sky is essentially clear, but I estimate that these panels are producing exactly 0 kWh of electricity because they are entirely covered with snow. What are the factors that could influence whether the snow melts? Both increasing temperature and clear skies would certainly contribute.

It seems to me that the owners and operators of such facilities would benefit from low-tech solutions to clear the snow. There may be an entirely reasonable explanation as to why these companies do not proactively clear the panels. Maybe there are issues regarding potential damage to the panels? That seems plausible. Maybe it is too expensive and not economically worthwhile? But that seems doubtful.

I submit to you the following figure, which depicts the historical data from a solar plant in New England. I have clipped the energy units from the Y-axis to keep the data anonymous. The figure shows data from Nov. 1, 2020 through Jan. 31, 2021. The most salient point is that the observations in January 2021 barely exceed 0 for weeks, which means they are generating and selling roughly 0 kWh. This is entirely attributable to snow cover.

The days after snow falls create an inherently difficult forecasting problem. We do not know how much snow fell at the actual site of the panels. In fact, we may only have weather data for a station that is miles away. We do not know if the facility clears the panels. Even if the temperature increases sufficiently to melt the snow, we do not know how long that process may take. The temperature may increase sufficiently and then decreases again, thus freezing the partially melted snow.

These are challenging and intractable issues from the perspective of the forecaster. What can we do about this? First, we must adjust our expectations. In snowy regions, we cannot possibly expect to be as accurate in the winter as we are in the summer. Second, we could attempt a ‘persistence model’ which utilizes lagged loads. This will only be useful when real-time data is available, and the accuracy will certainly degrade as we extend into the forecast horizon. Third, we could attempt to code-up some logic regarding the temperature after a snowfall to account for snowmelt, but those relationships are not likely to be sufficiently robust or consistent to provide a useful signal for the model to discern.

Here is the coda to this tale. I visited the same solar array 3-days after the snow stopped falling. I estimate that the panels are roughly 5% clear. In other words, the array looks substantially the same as when I visited 2-days prior.

Snow is to solar panels what kryptonite is to Superman – it takes away all the power.


Summing and Averaging

The Sum function in MetrixND seems like a complex way to make adding difficult.

In a MetrixND transformation, numbers are added by joining variables with the “+” sign. Adding three variables is as simple as writing the following expression in the transformation editor formula box:

DataSource.Variable1 + DataSource.Variable2 + DataSource.Variable3

The complex way to add is using the “Sum” function. This function requires inserting the three variables separated by commas (,) as Sum function parameters, as shown below:

Sum(DataSource.Variable1,DataSource.Variable2,DataSource.Variable3)

Technically speaking, the Sum function requires five extra characters to do the same work as the traditional “+” sign.

So, why does MetrixND include this function?

In the situation where variables have missing values, the calculation using the “+” sign results in a missing value. In other words, a number plus a missing value equals a missing value.

100 + MISSING = MISSING

While the Sum function behaves the same, the Ignore Missing option changes the behavior to produce a value. In other words, the Sum function with the Ignore Missing option selected means that a number plus a missing value equals a number.

100 + MISSING = 100

To activate the Ignore Missing option, check the Ignore Missing box in the transformation editor as shown below.

The Ignore Missing options works with the following functions:

  • Sum
  • Avg
  • Max
  • Min

It doesn’t matter whether you use traditional math operators or the functions when data is complete. However, when the dataset has missing values, the functions and Ignore Missing options may be the difference between forecasting a number and forecasting a MISSING.

Complexity has its purpose.


Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March through December 2020

As previously discussed in the first of this blog series on April 13, 2020, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) 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 -12.3% and -7.2%. In both Europe and North America, December loads ran lower than expected although the impact more than likely reflects a net load reduction associated with the winter holiday season.

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 these trends.

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


I Get by With a Little Help from My Friends, and Google

It almost goes without saying that most utilities have seen a noticeable deviation in their electricity sales in 2020 due to the pandemic. But the question remains, how long will the deviation persist and what does the path ahead look like? Load forecasters the world over are peering into their crystal balls to try to figure this out.

A big part of the challenge is finding a driver that forecasters can use in their models to help capture the variation in sales due to behavioral changes from various COVID-19 mitigation policies. And if you do find a driver, with any luck you can forecast it without too much heartburn. Coincidentally, Google has been publishing daily anonymized COVID-19 Community Mobility Data that shows deviations from baseline location data by state, county and country from users who have turned on their Location History setting on their mobile devices. A few of my colleagues and I have leveraged this data in our forecast models, and the results have proven quite favorable.

The comprehensive dataset is available for download in CSV format on Google’s mobility data website, free of charge. Just scroll down to “Community Mobility Reports” and click on the “Global CSV” option to download the CSV file. The file is quite large (about 240 MB and counting) with too many records to fully load into Excel, and so I recommend opening it in something like Notepad or Notepad++ and copying and pasting the relevant data into a spreadsheet. You’ll have to do a little wrangling to get the data in a useable form, but surprisingly not much.

This data represents the percentage change in people’s visits to – or time spent – in six categories of places relative to the defined “baseline day,” or median value for that day-of-the-week from the period of Jan. 3 – Feb. 6, 2020. The categories are retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. To give you a visual, here is what the data for the state of California looks like:

From this visual, we can see there’s a positive percentage change in the residential category and a negative one in workplaces as people shift to spending more time at home and little to no time in their place of work. Retail and transit are also down as people are shopping less and not taking public transportation. Grocery and pharmacy is down as well, but not as much as other categories because people obviously still need to buy food and medications. These percentage deviations appear to have stabilized since June, which makes forecasting this data a little less intimidating.

One thing that pops out from looking at the data is there’s a well-defined day-type pattern (i.e., weekend vs. weekday) for the residential and workplace categories. That is, there’s less of a change on weekends because people were already home and not at work before the pandemic took off. The large spikes are for holidays, as those days reflect a significant change relative to Google’s baseline. Retail also has a day-type pattern, albeit a little less well-defined. For this reason, I found the retail, workplace and residential categories to be the most applicable and useful for predicting loads in this COVID-19 world. And since the data are of daily frequency, you can leverage them in a daily model, or run them through billing cycles and incorporate them into a monthly SAE model.

Going with the latter approach, I started with a “business as usual” Residential SAE model (i.e., one that’s estimated with data through February 2020 so the COVID-19 period data does not influence the model coefficients). What the model shows is that residential use per customer has been higher since April relative to where it should have been subject to the actual weather that occurred.

But incorporating Google’s mobility data into the model helps to close this gap. Moreover, forecasting what we think the percentage changes in the relevant categories will be gives us a better projection for how the rest of the year might shake out.

Undoubtedly, this data is not perfect. For example, the baseline days probably aren’t representative of the true baseline, and Google is aware of this too. And using them certainly won’t remove all of the wrenches this pandemic has thrown into our forecast models. But they just might help to tighten things up and yield a more reasonable load forecast.

Google states that the data will be available for as long as public health officials find them useful, but who knows how long that may be. I don’t think I will try and forecast that. But with any luck, that will be just long enough.

Shout out to the folks in the Operational Forecasting Team at AEMO for calling this data to our attention!


November 2020: Trends in Estimated Load Impacts of COVID-19 Mitigation Policies on European and North American Electricity Consumption

March through November 2020

As previously discussed in the first of this blog series on April 13, as lockdown policies are enacted to help reduce the spread of COVID-19, the Itron Forecasting Team is leveraging publicly available hourly load data for most North American Independent System Operators (ISOs) 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 -12.3% and -7.2%. In both Europe and North America, November marked a slight uptick in the load impact, reflecting renewed lockdown activity driven by a new wave of COVID-19 cases.

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 these trends.

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


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