We’re proud to say, however, that in many ways it was just business as usual this morning at Opower headquarters. A lot of us did what we’re accustomed to doing: rolling into work on two wheels.

Opower Bike to Work Day - Group Photo!

All of our offices boast their share of dedicated cyclists. In fact, more of us are pedaling than ever, now that Washington DC and London have well-developed urban bike sharing systems. And even though the City of San Francisco still lacks a full-fledged bike share scheme, our folks there aren’t deterred: on a typical day, a formidable 20% of our ~100 San Francisco Oployees bike into the office!

Wall-to-wall bike storage at our Arlington Headquarters

Given that biking is 50 times more energy efficient than driving a car (in terms of raw energy burned per passenger mile) and infinitely less polluting, we wholeheartedly endorse it and aspire to make it easy and fun for our people. Our offices feature designated bike storage, a growing set of maintenance tools, and even organized group rides in the evenings.

Today, in the spirit of Bike to Work Day, the number of Opower bike riders soared. It didn’t hurt that it was a balmy morning in the DC area. Sources say that many Oployees were motivated by the promise of a free catered breakfast upon arriving by bike (see below). Meanwhile, our behavioral science experts are contending that participation was high mainly thanks to social pressure, since we were told yesterday that “the majority of our colleagues would also be riding into work.”

Breakfast is served to O-Cyclists upon arrival on Bike to Work Day

And of course biking at Opower isn’t only an outdoor or commuting activity: our bi-coastal fleet of blender bikes are always at the ready to whip up a refreshing fruit smoothie. Just add pedal power.

Oployees turning kinetic energy into deliciousness on the blender bikes this morning

Ahhh, how refreshing.

Our Office Manager Donald gulps down a yummy bike-blended smoothie to celebrate Bike to Work Day

All businesses have a conventional bottom line to measure their fiscal performance (financial profit or loss). Opower values its second bottom line – our environmental and societal impact – just as highly. Although non-monetary outcomes can sometimes be harder for a business to quantify, we’re unique in that we have a verified scientific methodology to measure our results.  And those results – which center on the massive energy savings achieved in collaboration with our 80+ fantastic utility partners – are what inspire us every day.

The way we achieve these results ultimately comes down to one crucial ingredient: our people. We are committed to finding and keeping the most talented, passionate teammates and empowering them to make a real difference in the world. Everyone on the Opower team – from engineering to regulatory affairs to systems operations – contributes to our growing positive impact on the environment.  

And the best part is, we have fun while doing it. In fact, Opower headquarters was voted one of the top places to work in the Washington DC area in 2013. Check out the below video to take a quick tour of our offices and hear a range of Oployees share why they are so passionate about working here:

Our company is getting bigger and better every day, as we continue to expand globally and deploy innovative solutions to help people save energy. If you want be part of a team that truly makes a difference and are ready to give us even more reason to celebrate on Earth Day 2014, come join us! Here are just a few of the positions we’re hiring for:

• Senior Software Engineer (San Francisco and DC)
• UI/Front-End Architect (San Francisco or DC)
• Product Manager (San Francisco and DC)
• UX Designer (San Francisco or DC)
• Sales Executives (US, Europe, Latin America, Asia)

For more planet-friendly opportunities, head to the Opower Careers page. Happy Earth Day!
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Energy savings and impact calculations are conservatively based on annual savings of 1.5 terawatt-hours per year, 325 full-time employees, an average residential retail electricity price of $0.118/kWh, and outputs from the US EPA’s Greenhouse Gas Equivalencies Calculator. Additional conversions based on Metropolitan Transportation Authority, Facebook user research from Arbitron, and Nissan Leaf coverage by Time Magazine. All impact figures are mutually exclusive rather than additive.

Mother Earthly thanks to Ashley Sudney, Katie DeWitt, and Tyler Curtis.

Since then, changes in how things are distributed in our society have been cause for both hope and concern.

On the one hand, improvements in semiconductor technology for things like computer chips and solar panels have made ideas like “distributed computing” and “distributed energy” a practical reality. On the other hand, recent economic turmoil has brought America’s uneven distribution of wealth and income under scrutiny.

With both of those threads in mind, we thought it would be interesting to consider how evenly energy consumption is distributed, and what that means for the viability of energy efficiency as a distributed resource.

So we’ve drawn upon our vast dataset of US electricity consumption to examine some key distribution-related questions:

• Among American households, what’s the breakdown between high, medium, and low electricity users? 

• What drives differences in electricity consumption across households?

• Is electricity consumption as unequally distributed as the nation’s income?

• What does the nation’s electricity distribution suggest about how to go about saving energy at scale?

Starting with 25.8 million homes for which we had 2011 electricity usage information, we narrowed the dataset to 8.57 million American homes that we confirmed have natural-gas heating systems (the prevalent heating fuel across the US).  In this way, we could make an apples-to-apples statistical comparison of different homes’ energy use.

We discovered that the top 1% of homes consume a full four times more electricity than average. Still, residential electricity usage ends up being much more evenly distributed than income. We’ll draw upon a couple key economic principles to explain why that is, and describe how the shape of the nation’s electricity distribution is a critical consideration in any large-scale effort to make America more energy efficient.

The Top 1% of households (by usage) consume 4% of residential electricity

Put another way, for every unit of electricity the average American home consumes, the top 1% of homes (by usage) are consuming four units.  And the heaviest 10% percent of users are responsible for nearly a quarter of all residential electricity use.

What else does this mean?

• The top 1% of households (by usage) spend approximately $4,000 per year on electricity, while the average household’s yearly electric bill is around $1,000.

• Supplying electricity to each household in the top 1% entails greenhouse gas pollution from power plants equivalent to driving 5 gasoline-powered cars for a year. In comparison, the average household’s electric usage contributes pollution equivalent to 1.25 cars.

• 1 day of combined residential electricity usage across the top 1% of US households (comprising approximately 3.1 million people) is roughly equal to 1 year of total electricity consumption in the African country of Sierra Leone (a nation of 5.5 million people).

What’s driving the disparity in how Americans consume electricity?

Mega-Homes Mean Mega Usage (Usually)

Generally, the prime suspect in the search for causes of high energy use is large home size (i.e. square footage).

To investigate, we evaluated the relationship between electricity consumption and home size across more than 4.3 million residences in our dataset. We wanted to see how electricity usage of a mega home (i.e. among the largest 1% of homes) compares to that of an average-sized home in our dataset (approximately 1,600 square feet). 

Our findings reveal a marked difference: an average mega-home uses 2.5 times more electricity each year than a typical home.

In this light, the chart below displays a predictable correlation: the larger the home, the higher the electric bill.

But while a general correlation between home size and energy consumption makes intuitive sense (e.g. more space to cool, more rooms with TVs), a deeper examination of the data reveals a complication: there can be substantial variation in electricity use among homes that have the same square footage.

To see how this is true, take a look at the line graph below: it shows that among households of the same square footage, it is not uncommon for energy usage to vary by as much as six times. This wide degree of variation suggests that while home size can serve as a rough predictor for usage, other factors – such as income, occupancy, climate, construction features, and especially behavior – are also important drivers.

Electricity usage is much more equally distributed than income

We’ve seen that the top 1% of electricity users consume 4% of the nation’s residential electricity. How should we view this in relation to income distribution in the US, where the top 1% of households take home nearly 20% of national income?

We shouldn’t be super surprised that household electricity consumption is more equally distributed than income. That’s because as a family’s income increases, their electricity consumption is likely to grow less than proportionally.

The principle at work here is a straightforward concept from Economics 101, called “diminishing marginal utility.”  Basically, as we obtain more of a good, we value each additional unit less. For example, there’s a big difference between having no fridge and one fridge in a home. But there is much less incremental value of going from four fridges to five fridges.  In other words, people’s demand for electricity has its limits, even as their income may grow.

Another reason that the distribution of electricity is more equal than income is that, although wealthier Americans are likely to live in larger homes, they are also more able and likely to invest in energy-efficiency improvements like insulation and triple-pane windows.

A nifty way to compare the distribution of electricity and income is to use a statistical measure called a Gini coefficient (named after the Italian sociologist who created it), which is a number that ranges between zero and one. A Gini coefficient of 1 indicates a totally unequal situation (e.g. a single household using all the electricity in the country), whereas lower values (i.e. closer to 0) represent a more equal distribution of resources.

For instance, the Gini coefficient for land ownership in the Middle-Eastern country Qatar is equal to 0.9, as the Emir of Qatar owns almost all the land in that country. By contrast, land ownership in Norway has a Gini coefficient of 0.18, reflecting greater equality in the distribution of land there.

The Gini coefficient for income in the US is around 0.47.  Based on our dataset, the Gini for residential electricity consumption is 0.34 (see Methodology), further suggesting that it is much more equally distributed than income. This finding is consistent with other studies that have statistically examined the distribution of energy and utilities.

Why understanding energy distribution is important for realizing efficiency opportunities

We’ve seen that, at least when compared to income, residential electricity consumption in the US is relatively evenly distributed. The top 1% of US homes, although they use 4 times more electricity than average, only account for a sliver of overall national consumption.

Therein lies an important implication for how to go about reducing residential energy consumption: large-scale energy efficiency efforts (e.g. cutting energy waste in half by 2030) can’t exclusively focus on the very highest users, for the simple reason that such homes are in limited supply (e.g. only 4% of homes).

Instead, saving energy at scale requires a broad-based approach that works well for homes across the usage spectrum. And such approaches do exist, as evidenced by Opower’s own behavioral efficiency programs – which have enabled millions of households to save energy, regardless of their geographic location, home size, income segment, age, and initial level of consumption.

Energy efficiency initiatives that successfully reach large swathes of the population are likely to do more than save a lot of energy: they may also provide certain groups — such as seniors and low-income families — with much-needed relief from burdensome energy costs. For example, recent statistics show that elderly and needy American families routinely see 19-26% of their paycheck go toward utility bills, compared to just 4% for the median American household. This suggests that effective broad-based energy efficiency programs like Opower’s can be beneficial along multiple dimensions — environmental, social, and monetary. 

While there are differences in how American homes use energy, there are often similarities in their ability and reasons to use less.  To explore which savings opportunities are most relevant to you, your utility’s website or the new EnergySavers portal from the US Department of Energy are great places to start.  Because the future is here…and it’s full of potential for energy efficiency. 

Special thanks to David Moore, Jon Margolick, Chris Corcoran, Katie Dewitt, Jillian Cairns, Efrat Levush, Ashley Sudney, Tyler Curtis, and Arhan Gunel.

Follow @OpowerOutlier on Twitter

Appendix: Questions for the curious reader to consider

The foregoing analysis has left a few questions lingering in our heads:

1. Are we underestimating energy usage inequality by not accounting for multiple-home ownership?

We evaluated the distribution of electricity by treating every household as a distinct energy-consuming unit. Given that some families consume electricity across multiple homes, our analysis may be understating inequality. In some cases, multiple-home ownership may be nontrivial. The 2010 Census found that 3.5% of homes nationwide are for seasonal, recreational, or occasional use.  And in some states, that fraction exceeds 10%.

 2. Are the heaviest electricity users necessarily less energy efficient?

No. From an environmental and energy-efficiency perspective, high electricity consumption may not always be a bad thing. For example, a large multi-generational family living under one roof is likely to face a high electricity bill, but compared to a scenario where all family members live in separate homes, per capita energy consumption may be quite low.

Similarly, if you own a plug-in electric car, your annual electricity consumption will increase significantly.  Consider an all-electric Nissan Leaf vehicle driven 12,000 miles per year: at a fuel economy of 34 kilowatt-hours (kWh) per 100 miles, it will require 4,080 kWh of electric charging— increasing an average home’s annual electricity usage by 40-50%.  But, relative to driving an average gasoline-powered car, your environmental impact and overall energy costs will decrease.

3. How does the distribution of residential electricity compare to other measures of energy inequality?

To fully assess Americans’ relative energy and carbon footprints, it’s necessary to look beyond household electricity usage. A peek at the transportation sector suggests that Americans’ energy usage in the air and on the roads may be more unequal than in their homes.

For example, market research from the airline industry suggests that 17 million Americans (less than 6% of us) account for 58% of all flights taken by Americans.  And the energy-related carbon emissions from flying are disproportionately large: the global warming pollution from one round-trip flight between San Francisco and New York (for a single customer) is equivalent to ~1 month of an average home’s electricity use. A recent New York Times analysis suggests that if you take five long flights a year, they may well account for three-quarters of the total pollution you create.

Day-to-day, the energy consumed for getting around town may exhibit a similarly unequal distribution, especially through a suburban versus urban lens: the EPA has calculated that the transportation energy use of a household in a typical suburban area is more than double that of a household in a transit-accessible area.

Methodology:

For the purposes of comparability, we narrowed our dataset to 8.57 million homes that have natural-gas heating systems.  Analyzing gas-heat homes helps reduce the effect of exogenous/climate-related factors on our analysis.  For example, Minnesota’s winter is much colder than northern California’s winter, but our analysis is to a significant degree insulated from this variation because the homes we considered do their heating with natural gas rather than electricity. This approach is especially important because heating represents a large fraction (42%) of home energy use.

Estimates for annual electricity costs are based on the average 2011 US retail electricity rate of $0.118/kWh. Note that average annual US household consumption is estimated at 11,496 kWh.  Our average value (8,548 kWh) is lower largely because we have intentionally restricted our dataset to gas-heat households.

To compute a Gini coefficient for residential electricity consumption, we divided the 8.57 million households in our dataset into 100 groups of equal size, to determine percentiles of consumption. We computed each percentile group’s share of total electricity consumption, and then determined the cumulative share of consumption up to each percentile level. This data series allows for the construction of a Lorenz curve equation, L(X), which we integrated between 0 and 1 using a Riemann-sum approach across the 100 subintervals.

The resulting Gini coefficient (G) for residential electricity consumption, G = 1- 2 ∫ L(X) dx, was 0.34. This result parallels the 0.37 Gini coefficient for US residential electricity consumption computed by Jacobson, Milman, and Kammen (2004). It makes sense that our Gini coefficient is slightly lower (i.e. reflecting a more even distribution) than the literature’s existing estimate, as our analysis controls for heating type while Jacobson et al. appears not to.

Households in our dataset for this analysis are distributed across 23 states.  Although geographically diverse, this dataset is not a perfectly representative sample of American households. However, we are confident that it is the largest dataset ever analyzed for the purpose of examining the distribution of US residential energy consumption, and that our analytical results are validly indicative of a national phenomenon.

Sierra Leone’s national electricity consumption was 111,600 MWh in 2009. The average annual electricity usage of a top-1% household in our data set is 33,654 kWh. Extrapolative multiplication of this usage by 1,147,614 households (i.e. 1% of the US’ 114,761,359 households) yields 38,621,802 MWh/year, or (dividing by 365) 105,813 MWh per day. This amount of electricity is approximately equivalent to Sierra Leone’s total annual consumption across all sectors. 

The greenhouse gas pollution from one round-trip flight between San Francisco and New York is 675 kg CO2, according to the International Civil Aviation Organization’s carbon emissions calculator. 675 kg CO2 is tantamount to approximately 1 month of an average home’s electricity use, according to the EPA’s Greenhouse Gas Equivalencies Calculator.

Data Privacy: All data analyzed here are anonymous and treated in strict adherence to Opower’s Data Principles.

Author’s note: The analysis and commentary presented above solely reflect the views of the author(s) and do not reflect the views of Opower’s utility partners.

That’s a lot of TVs aglow at one time.  The amount of electricity collectively consumed by TVs during the big game is reckoned by General Electric to exceed 11 million kilowatt-hours – equivalent to the power generated by ten medium-sized coal-fired power plants over the course of the event.

But here’s the real kicker: all those TVs illuminated during the Super Bowl are actually a force for dramatically lower overall energy consumption.

Turning back the clock to February 5^th 2012, we analyzed electricity consumption data across 145,000 households on last year’s Super Bowl Sunday. And our analysis revealed a remarkably consistent pattern: when the game kicks off, electricity usage plummets.

Let’s examine the game-day details and explore how a major televised sporting event can affect home energy use…

When Super Bowl XLVI kicked off, home electricity usage dropped off

First, let’s take a look at game-day energy usage in the western part of the country, for which we analyzed data from 91,000 anonymous households.

The chart below shows how average residential electricity consumption on 2012’s Super Bowl Sunday (broken up into 15-minute intervals) differed from a typical midwinter Sunday. From morning through midafternoon, Super Bowl Sunday was a fairly normal energy day in the West.  Between 10am and 3:30pm, electricity usage was slightly above typical – possibly as party hosts were doing preparatory tasks like washing dishes, and soon-to-be party guests were firing up their ovens to bake jalapeño poppers. 

But then something special happened around 3:30pm PST: the Giants and Patriots ran onto the field at Indianapolis’ Lucas Oil Stadium. Immediately, electricity consumption began to tumble downwards. As the game’s suspense escalated, usage levels descended –dropping to around 7% below what is normal for a Sunday afternoon/evening.

During the halftime show, the game’s viewership surged to 114 million people (even higher than the 111.3 million that the game averaged), who were treated to performances by Madonna, the dance duo LMFAO, and the English singer M.I.A.

But something else also seemed to be M.I.A. during halftime: home electricity usage. It faded to a full 7.7% below typical levels.

And so while in the hours before the game, home electricity usage had been just slightly above (+2.4%) what is typical, things changed radically after kickoff. Over the course of the game, consumption decisively decreased to 5% below normal.

And usage remained well below average (-3.7%), even after the game was over.

Why do homes use significantly less electricity during the Super Bowl?

We think there are a couple key reasons for the sharp reduction in usage:

1) Many households are not using appliances other than their TV during the game. On a typical Sunday in the early evening, many people would have been at home using multiple different kinds of electricity-consuming appliances (e.g. laundry machine, kitchen appliances, vacuum cleaner, TV) around the house—as suggested below by the relatively high evening usage that normally takes place on a Sunday.

But the Super Bowl changes this pattern: it concentrates activity around the TV. With so many people glued to the couch during the game, fewer households are using electricity for cooking, cleaning, or anything else other than watching the tube. Abstaining from other forms of energy usage naturally causes game-time electricity consumption to decline below typical levels.

And what explains the persistence of below-average consumption levels even after the conclusion of the game? One factor (another is described below) may simply be sustained fixation on the TV, which continues to divert people away from their typical Sunday night uses of electricity.  For example, after the game ended in 2012, tens of millions Americans didn’t leave the couch: 37.6 million viewers stayed huddled around the TV to watch the season premiere of the musical reality-TV show “The Voice.”

2) Many people watch the game at houses of friends and family. For many fans, watching football is a community event — meaning that people leave their homes to watch the game with other fans. In 2011, an extensive poll by Nielsen found that 45% of Super Bowl viewers planned to watch the game with friends or relatives.

A mass movement toward collective TV-watching at friends’ houses (or at a bar) on a Sunday night will result in significantly lower-than-average electricity use. Just as carpooling reduces transportation energy use, gathering together to watch televised sports—let’s call it “TV-Pooling”—decreases home electricity use. Twenty people watching one large TV at a friend’s house requires much less energy than 20 people watching 20 TVs in their individual homes.

And because for West coast audiences the Super Bowl concludes fairly early (around 7pm), the communality of the game-watching is likely to segue into additional away-from-home social revelry that persists longer into the night…as is suggested by the sustained lower-than-average usage through the very end of Super Bowl Sunday.

The numbers and analysis above correspond specifically to the Western region, but what about elsewhere? To test whether the Super Bowl’s energy-saving effect truly represents a broader nationwide phenomenon, let’s check to see if the pattern we’ve identified also materializes on the other side of the country.

Chips and (Electricity Usage) Dips: across the country, home energy consumption declines during the Super Bowl

Now we know what to look for, as we shift our attention to 54,000 anonymous households located in the eastern part of the country…

When last year’s Super Bowl kicked off (at 6:30pm EST), did the region’s average home electricity usage substantially decrease like it did in the West?

Indeed it did: game-time consumption in the East dropped to as much as 5% below typical levels.

Up until game-time, usage in the Eastern region was slightly below normal — likely due to the relatively warm weather that day, which may have given people an incentive to spend more time outside.  But then the Giants and Patriots took the field in Indianapolis, and poof: electricity consumption decreased significantly. Each 15-minute interval of usage during the heart of the game was more anomalously low than any other interval that day.

Consolidating the 15-minute usage intervals into wider time periods, we see that home electricity usage during the game was, in total, nearly 4% lower than we would expect on a typical midwinter Sunday evening – and represented the day’s most significant deviation from normal.

The pronounced decline in game-time electricity usage in the East is, similar to what we saw in the West, most likely a consequence of two related behavioral phenomena: exclusive focus on the TV and communal game-watching.

Importantly, these two subconscious energy-saving actions take place during a time that is normally characterized by peak residential consumption — i.e. when people are typically at home doing their Sunday evening routine (cooking, washing, etc.).  It’s for this reason that the Super Bowl produces a prominent energy-conservation effect: relative to a run-of-the-mill midwinter Sunday evening, the Super Bowl catalyzes an extensive cutback in household energy use.

And why did eastern residential electricity consumption spike to above-average levels after the game, given that the entire rest of the day had been below average? Quite simply, the Super Bowl ends on the late side in the Eastern time zone. That means that immediately after the game, many people (at least those planning to report to work the next day) were probably returning home en masse from parties.  One can imagine that as they walked in their doors, they collectively flipped on the lights and other appliances. This, in turn, translated into an unusually above-average period of energy usage relative to a typical Sunday’s late night, when most people would have been winding down and going to bed.

Better together: TV-Pooling is good for the planet, the pocketbook, and civic life

We were struck by how the Super Bowl – which has for the last 3 years been the most watched television event in US history – appears to reduce home energy usage.

Mass TV watching traditionally conjures up images of enormous, electricity-gobbling 42-inch plasma screens.  But as it turns out, all these TVs are a force, at least over the course of a few hours, for decreased electricity consumption.

And we were especially intrigued by the energy-efficiency implications of a key factor behind the decrease: TV-Pooling.

Communal TV viewing may at first seem like a trivial concept, but its effect on a country’s residential energy consumption could be significant. Super Bowl XLVI demonstrated that when around one-third of Americans collectively watch a single 3.5-hour sporting event, the corresponding reduction in the nation’s daily energy bill can be upwards of $3.1 million. That’s a lot of guacamole. Replicate this phenomenon a couple times each month, and you’re potentially talking about some serious energy and cash savings.

In addition, TV-Pooling may do more than conserve energy: it may help strengthen our social bonds. In his influential book Bowling Alone, Harvard Professor Robert Putnam chronicles how Americans have become increasingly disconnected from their friends and neighbors in recent decades, replacing communal engagements with individualized entertainment. In Putnam’s analysis, television is among the culprits – and perhaps appropriately so. But, there is something different about how we watch major broadcasts like the Super Bowl.

Events like the Super Bowl are not just popular, they are social. And, as such, they may help, just a little, to unite neighbors, friends, and family – and help save the planet.

Special thanks to Ashley Sudney, Efrat Levush, Steven Blumenfeld, Nathan Srinivas, Emily Bailey, Elena Washington, Yoni Ben-Meshulam, David Moore, Katie DeWitt, Andrew Sharp, Nate Kaufman, Jordan Jakubovitz, Carly Baker, and Arkadi Gerney.

Follow @OpowerOutlier on Twitter

Methodology:

The aggregated data used for this analysis stems from two large regional samples (nwest = 91,355 households; neast = 53,574 households). Time-of-use consumption data in both cases are defined at an AMI granularity of 15 minute intervals.

Baseline electricity usage levels (used to evaluate the deviation in home electricity usage on Super Bowl Sunday, relative to a typical day) correspond to energy usage levels observed on other Sundays in January 2012 and February 2012 that exhibited similar regional weather to February 5, 2012.  Similarity in weather is based on comparable daily mean temperature and negligible-to-zero precipitation.  Each regional analysis is demarcated by a geographic area with maximum radius of 25 miles.  To reinforce the analytical comparability of daily weather conditions and the underlying electricity usage requirements associated with them, the analysis is explicitly restricted to homes that utilize gas heating systems. Meteorological data is sourced from Weather Underground.

Each 15-minute game-time usage interval exhibits a standard deviation of no more than 0.40 kWh. Accordingly, the computed interval differences from baseline usage are statistically significant at the 99% level, within an confidence interval of  +/- 0.005 kWh (equivalent to the amount of electricity consumed by a single 20-watt efficient light bulb during the interval considered).

Note that, in the western region case, the above-average usage levels observed during the pregame period are more than offset by the strongly below-average usage levels that register when the game starts. This suggests that Super Bowl Sunday does not simply reduce usage during a certain segment of day; it also, as a result of the exceptional game-time contraction in consumption, is an overall net reducer of daily residential energy use. This outcome holds true across both regional analyses.

Calculations:

TV power demand in terms of number of power plants: General Electric estimates that all TVs beaming the Super Bowl in the US collectively use 11,309,607 kWh over a 5 hour period, implying an instantaneous power demand of 2,262 MW.  Net capacity of an average coal-fired power plant in the US is 228 MW. Aggregate demand of 2,262 MW over the course of the event thus corresponds to approximately 10 average coal-fired power plants. This is a conservative assumption, as it does not reflect transmission and distribution losses, which are generally considered to be around 7%.

Game-time usage reduction > 3x the energy consumed by TVs watching it: Based on GE’s 5-hour TV energy consumption estimate of 11,309,607 kWh. Proportionality to our game-time-specific analysis suggests that all TVs watching the big game for 3.5 hours = (3.5/5) * 11,309,607 kWh = 7,916,725 kWh.  The larger of our two regional time-of-use evaluations suggest that average residential electricity usage falls to 5.0% below typical levels during the game. A given US household consumes an average of 11,496 kWh per year (i.e. 8,760 hours), and so for a 3.5 hour period is proportionally assumed to consume 11,496 kWh * (3.5/8,760) = 4.59 kWh.  Average per-household usage reduction during Super Bowl = 4.59 kWh * 5% game-time reduction = 0.23 kWh. Across 114.76 million households in the US, the specific game-time reduction nationwide sums to 26,355,811 kWh. This is approximately 3.3 times as much electricity as the 7,916,725 kWh consumed by TVs during the game. This is a conservative estimate, insofar as the Super Bowl takes place on a Sunday afternoon/evening, which is a relatively high-usage time of the week for the residential sector.

Game-time monetary savings almost equal to cost of a 30-second commercial: Specific game-time reduction sums to 26,355,811 kWh (see above). At a national average residential cost of electricity of $0.1187/kWh, the game-time energy cost savings is $3.13 million. 30-second spots during Super Bowl XLVI fetched $3.5 million.

Data Privacy: All data analyzed here are anonymous and treated in strict adherence to Opower’s Data Principles.

Author’s note: The analysis and commentary presented above solely reflect the views of the author(s) and do not reflect the views of Opower’s utility partners.

That’s according to a report published last month by the International Data Corporation (IDC), which assessed the state of the “digital universe” – a measure of all the digital data created, replicated and consumed in a single year.

IDC reports that between now and 2020, the digital universe will double every two years, growing from 2.8 zettabytes to 40 zettabytes (i.e. 40 trillion gigabytes) of data. Emerging markets’ share of the digital universe will grow to about two-thirds by that time (much of it from China), compared to one-third today.

The report also estimates that 23% of the digital universe has the potential for generating valuable insights. But that estimate is hypothetical: it assumes a scenario wherein all data is properly organized and classified. In fact, IDC considers only 3% of the digital universe to be adequately characterized, or “tagged.” And only a fraction of that tagged data is ultimately analyzed — in the end representing just 0.5% of the world’s supply of digital data.

In particular, the recent advent of smart meters—which record energy usage every hour, and in some cases every 15 minutes—has brought about an unprecedented proliferation of energy data.  One in three households in the US now have a smart meter.  While a traditional monthly meter generates only 12 data points per year, a modern meter measuring electricity consumption at 15-minute intervals generates more than 35,000 data points over the same time period.

Managing this massive boom in energy information is a lofty challenge, but it has enormous potential for good: real-time data offers significant benefits to utility companies (e.g. pinpointing outages and monitoring power quality) as well as customers (e.g. better understanding home energy usage patterns can empower customers to improve their energy efficiency and lower their bills).

As the IDC report suggests, the growing opportunity to find valuable insights in data — energy-related or otherwise — is intimately connected with our ability to manage large data streams, and rigorously apply privacy safeguards in data collection and storage. To learn more about energy data’s growing role in the digital universe and to get one perspective on how an energy company can incorporate best practices in data management and privacy, check out Opower’s Data Principles.

Follow @OpowerOutlier on Twitter

A study released last month by New England’s regional electricity transmission organization (which spans Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont) forecasts that utilities’ efficiency initiatives — such as building retrofit incentives, promotion of efficient lighting, and Home Energy Reporting programs — will drive annual regional energy savings of 1,343 gigawatt hours between now and 2021. That’s equivalent to taking approximately 140,000 homes off the electric grid each year.

The graph below, and specifically the flat black line, shows the effect of the region’s energy efficiency investments on aggregate electricity consumption. The blue line represents how electricity demand would increase in a business-as-usual scenario; the red line represents how demand would grow given a moderate dose of efficiency programs (i.e. corresponding to the near-term efficiency investments developed by the region’s Forward Capacity Market, labeled “FCM” in the graph); the black line forecasts the 0% electricity demand growth that reflects the region’s ongoing, long-term commitment to efficiency.

NE-ISO (December 2012)

Here are 10 energy statistics from 2012 that capture some of the most noteworthy trends of the year, and that will shape the energy world in the years to come.

Natural gas, one of the three key fossil fuels in our energy economy (along with coal and petroleum), continues to ascend as a major force.

One prominent example: during the month of April, for the first time ever documented in the US, the amount of electrical generation from natural gas was equal to the amount generated from coal, which has historically been the country’s predominant fuel for power plants. This moment has been on its way for a few years now, as natural gas’ share of electricity generation has been steadily increasing, while coal’s share has been steadily declining (now around 42% on average, down from 52% in 2000).

The fuel’s growing role in the US is tied to the recent boom in gas production from previously untapped shale formations (e.g. in North Dakota, Pennsylvania, and Texas), which as of September 2012 account for 35% of the country’s dry natural gas production (compared to just 2% ten years ago). The plentiful supply of natural gas helped cause the fuel’s price to dip to a ten-year low earlier this year ($2.75 per thousand cubic feet), making it more competitive relative to other energy supply sources, including renewable energy (which now accounts for 13% of US electricity generation, mostly in the form of hydropower).

Smartphone sales volumes in 2012 were huge  – estimated at 717 million retail shipments worldwide (a 45% lift over last year).

But their energy consumption is minuscule.

A study by Opower in September revealed that charging the iPhone 5 costs just $0.41 per year, and charging the Droid Galaxy SIII costs just $0.53.

The collective energy demand of all those phones is nothing to sneeze at, but in the bigger picture, a global increase in smartphone usage is likely to cause lower overall energy consumption…

How so? Many consumers now use their smartphones to do things (e.g. internet, media, games) that they used to do on bigger, energy-hogging devices (e.g. computers, televisions, and game consoles).

Source: Opower (September 2012)

2012 was a sizzling year. Through the end of November, the year’s national average temperature was 3.3°F above the 20^th-century average, and 1.0°F warmer than the previous record-setting January-November period (in 1934).

Across the country, blistering temperatures triggered new records for hourly electricity demand in multiple states, from Idaho to the Carolinas. Outlier’s analysis shows that when temperatures soar, home energy consumption can spike even higher, as Americans crank up their air-conditioners:  compared with an average summer day, homes use up to 40% more electricity when the mercury surges past 100°F.

Nor is it cheap to address this dynamic. To ensure smooth and cost-effective management of the electric grid on extreme-heat days, US-based utility companies are budgeting around $1.3 billion per year on programs that are specifically designed to address peak energy demand.

Consumers now rank efficiency as their highest priority when shopping for a vehicle, helping to drive the record-breaking gas mileage of new cars sold in America (up about 23% since 2004).

In addition, stricter fuel economy standards, adopted by Congress in a bipartisan bill in 2007, have now taken effect and will rise to an average of 35.5 miles per gallon by 2016. The European Union’s average auto fuel efficiency is already around that level, and is headed toward 48.6 mpg by 2015.

Hybrid and electric vehicles are on the rise too. November was the biggest ever month for electric-vehicle sales in the US, pushing the year-to-date sales figure to 47,500. Though that’s only around 0.4% of US automotive sales this year,  utilities have indicated that the national electricity grid is prepared for those numbers to grow significantly.

Utilities around the world continue to undergo an infrastructural transformation that is changing the way customers and energy companies interact with energy data.

The number of smart meters in the US has grown more than fivefold during the last 5 years. There are now more than 36 million US homes with smart meters, which enable real-time communication of electricity usage data (and in some cases natural gas data, too).  Real-time energy information can offer benefits to utility companies (e.g. pinpointing outages and monitoring power quality) as well as customers (e.g. understanding one’s usage patterns can empower customers to shift energy usage to times of day when energy prices may be lower).

It’s projected that more than half of US households will have a smart meter by mid-decade. And market researchers envision that the worldwide market for smart grid data analytics will grow steadily through 2020, with cumulative worldwide spending from 2012-2020 totaling more than $34 billion.

Two new nuclear reactors are expected to go online in Georgia by 2017, after they received federal regulatory approval this February – marking the first time since 1978 that the US Nuclear Regulatory Commission (NRC) has granted a license to build a new reactor.

The project cost for getting these reactors built and online is estimated at $14 billion; the reactors will be able to produce 2,200 megawatts of power.

Nuclear power provides the US with about 18% of its electricity. Of the 104 operating nuclear reactors at 64 plants across the US, about half are over 30 years old. The reactors now under construction in Georgia are the first among many that are being contemplated: 16 other plants across the country have applications with the the NRC to build 25 new reactors.

Elsewhere in the world, Japan has responded to its March 2011 Fukushima incident by shifting away from nuclear energy.  Just two years ago, around 50 nuclear reactors generated around 30% of the country’s electricity. As of this week, only 2 of them are in operation, while the others are undergoing safety review. The Japanese government announced in September that it plans to phase out its reliance on nuclear power by around 2040. Germany, Switzerland, and France have also signaled an intention to shift away from nuclear generation in the coming decades.

In the latest update to its analysis on US energy flows, Lawrence Livermore National Laboratory released a report in October showing that despite improvements in technology and efficiency, the US still wastes more energy than it uses.  The country is just 43.8% energy efficient.

Take a look at the energy flow diagram below. Of the 97.3 quadrillion British Thermal Units (known as “quads”) of raw energy inputs that flowed into the US economy in 2011, only 41.7 quads were constructively used at the end of the day (as “energy services”). The other 55.6 quads were, in essence, wasted. This waste, summarized in the top right of the flow diagram below, is euphemistically classified as “rejected energy.”

Source: Lawrence Livermore National Laboratory (October 2012)

Most of the economy’s energy waste stems from the electricity production sector (because most power plants are relatively inefficient) and the transportation sector (internal-combustion vehicles are relatively inefficient, but as indicated above, they are getting better).

A reduction in consumption by 1.7 terawatt hours (i.e. 1.7 billion kilowatt hours) translates into reducing household electric and gas bills by nearly $200 million, and cutting greenhouse gas pollution by an amount equivalent to taking 250,000 passenger vehicles off the road for a year.

In early 2013, Opower will hit a cumulative energy savings milestone of 2 terawatt hours. From there, we’ll keep up the momentum throughout the year, doing our part to empower customers in the US and around the globe to become more energy efficient.

We’re confident that our efforts will, at the very least, help move our economy toward a future in which it uses more energy than it wastes (see 56.2% statistic above).

Special thanks to Katie DeWitt, David Moore, Efrat Levush, Ashley Sudney, and Peter Kjeldgaard.

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Author’s note: The analysis and commentary presented above solely reflect the views of the author(s) and do not reflect the views of Opower’s utility partners.

The campaign, commissioned by the Danish energy company Vestforbraending (in collaboration with the creative marketing firm Anew), involved opening a new pizzeria called “Vest Pizza” in the northwestern suburbs of Copenhagen. The restaurant’s sole purpose was to reward the community’s aggregate energy savings.

In preparation for the cold season, Vestforbraending sent out 8 winter-time heating conservation tips to their customers, then subsequently measured the total reduction in winter heating load. The more heating energy saved by the community, the more pizzas the restaurant would heat up and give away.  On the opening night of the restaurant, neighbors earned 163 free pizzas – an amount proportional to the community’s energy savings during the first phase of the campaign.

Creative Sandbox; Vest Pizza (November 2012)

Although the experiment mainly confirms that offering free pizza is a surefire strategy to achieve any goal, it also highlights a few approaches that may be helpful in designing other energy conservation campaigns: empower people with specific seasonal advice, tap into the power of community, and frame the benefits in a tangible way.

Check out the video below to see the Vest Pizza campaign in action.

But there was a catch: the 600 people to whom he sent the Christmas cards were complete strangers to him. He had randomly selected them from phone books.

Amazingly, more than 200 of the strangers sent back holiday cards of their own.

This outcome was a fascinating demonstration of a key dimension of social psychology, known to behavioral scientists as the “rule of reciprocation.” The rule, which is found in virtually every culture and is pervasive across our daily lives, simply states that people are strongly inclined to repay what someone has given them.

Studies have shown, for example, that when a restaurant bill is accompanied by a candy on the check tray, tips increase by 3.3%. And when a server personally delivers a second piece of candy, makes eye contact with the customer, and tells them that the extra candy was specifically for them…tips increase to 20% above normal.

Another frequent example of reciprocation: when mailed donation requests from charities are accompanied by free pre-printed address labels, the number of donations sent back to the charity almost doubles.

Whether you’re sending holiday cards, striving for larger tips, or simply pondering the daily factors that influence human behavior  – the rule of reciprocation is an important one to take to heart.

Just imagine what might result if you slip candies into your holiday card envelopes.

For more details and applications of the rule of reciprocation, check out NPR’s recent terrific article on the subject, which motivated this post.

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In a study published earlier this month, mathematicians from the University of California present a new statistical model describing how penguins tightly huddle together for warmth during cold spells. In particular, the researchers detail how penguins strategically design their huddles to maximize protection from strong winds.

These wind-savvy penguins provide a compelling reminder that our own winter comfort also hinges on strategically guarding against cold air and winds. Excessive infiltration of cold outside air through a building’s shell is a surefire recipe for high winter energy bills. Air-sealing through caulking and weatherstripping can go a long way in helping to reduce this problem, as can planting windbreaks (trees and bushes that intercept heavy winds from reaching the house) in some cases.

Space heating accounts for an estimated 41% of home energy consumption in the US. There are many opportunities to maximize the winter energy efficiency of our homes and trim our heating bills. To find out more about what we can learn from penguins in this regard, check out the just published study “Modeling Huddling Penguins,” in the scientific journal PLoS ONE.