Month: October, 2016
Have you ever heard the old adage, “Sell in May and go away”? It’s a popular one among stock traders since prices often dip during the summer months, but it’s not a rule to live by, especially when it comes to energy purchasing. We prefer the saying, “Prep in October, before winter gets closer”. Now, yes, we did make that up, but for good reason!
Winter weather can impact multifamily real estate owners on so many levels. From site-level crises like freezing pipes (and residents) to portfolio-level energy costs, the only way to manage the changing seasons is to plan ahead, early and often. For tips on how to prepare your actual buildings for winter weather, check out our checklist. For energy purchasing, getting ahead of the weather is key, however, that means your strategy heavily depends on forecasting, historical data, and taking action.
In the last few years, volatile winter weather has rocked New York City and national energy markets on a number of, often cleverly-branded, occasions. In 2014, Con Edison electric supply prices climbed to $.22/kWh after a string of single digit degree days – popularly known as the northeast’s Polar Vortex. In this past, extremely mild winter, Con Edison prices dipped to $.05/kWh. Owners who allow the price they pay to float with the market are susceptible to unexpected swings like this, which is why a pre-season contract often makes the most sense.
Heading into the upcoming winter, energy market prices are looking unstable. Due to an extremely hot summer in 2016, prices are no longer trending downward. Natural gas production has begun to level off and is expected to fall short of consumption in 2017, leading to a deficit in the storage system.. As always, prices are heavily influenced by fluctuations in weather, available supply, and expected demand. In the winter, extremely cold temperatures will cause the use of heaters to rise, drain natural gas supply and, inevitably, prices will go up. In turn, warm weather during the winter months will preserve natural gas and keep prices low. If only we really knew what to expect from Mother Nature!
Accuweather and the Farmers Almanac predict relative warmth in the South and Southwest but a colder-than-usual stretch in the Plains and Northeast.
The NOAA predicts warmer than usual weather across the map.
Ultimately, how you decide to budget and strategize for winter energy spending depends on how much risk you’re willing to take and able to handle. If you are more conservative and want to make sure that your budgets are set and secure each year, locking in a fixed rate for your energy supply before the winter would be your best option. This strategy is very typical in supportive or affordable housing and Co-ops or condos. If you have more ability to take on risk, and have enjoyed the recent low rates in 2015 and 2016, there are a variety of flexible and aggressive strategies to implement that can help you get the lowest rates possible.
No matter which approach you take to energy procurement for your buildings, one thing is certain: now is the time to act.
EnergyScoreCards Grading FAQ’s
EnergyScoreCards grades enable users to target their efforts and resources towards the buildings in their portfolio with the greatest potential for savings. This is done by comparing buildings to their ‘peer’ buildings, assigning grades of A, B, C or D according to how their energy and water use stacks up to these peers. Our new algorithm strives to improve on our concept of a ‘peer’ building by more closely comparing buildings to those that have similar needs.
For more information about the new grading algorithm itself please click here.
Why is my building’s grade different?
The new grade is a more accurate picture of how your building is performing compared to peer buildings. You can think of this new grade as a building’s potential for savings. So a C or a D building likely has a lot of ‘low-hanging fruit’ measures that are faster and cheaper to implement, while an A or B building is performing well relative to peer buildings – it may still have opportunities to lower energy and water use, but they may take a bit more investment of time and money.
My building’s grades improved with the new algorithm, what does that mean?
Your building is likely of a type that tends to use more energy, so when it’s compared to other buildings like it, its relative energy use is not quite as substantial. This includes older, small buildings or buildings with lots of amenity space.
My building’s grades got worse with the new algorithm, what does that mean?
Your building likely is the type that tends to use less energy, so when compared to other similar buildings, we can see that there is more room for improvement. These include newer, large buildings without a lot of amenity space and all-tenant-paid garden style complexes.
What does a grade change mean for my site staff and how do I communicate this change to them?
If you’re already using EnergyScoreCards grades to motivate or inform site staff, this shouldn’t be too big of a change. Most grades either won’t change at all or by much. For buildings that do have a larger change in grades, it likely won’t be too much of a surprise to site staff, since they know your buildings best. The most important thing to communicate is that we’re continually updating our models to make the grades more useful, and this latest change is really aimed at making the grades as actionable as possible, so buildings that now have Cs or Ds should have a lot of potential for savings.
Why did the algorithm change?
We’re constantly updating our software and modeling algorithms to incorporate the most state-of-the-art methods possible. This latest change was also driven by a lot of feedback we’ve received from clients, both in terms of grades that didn’t make sense and wanting tighter regional comparison.
Why did only some of my building’s grades change?
Since this upgrade is really a refinement of our current model, many grades won’t change much or at all. It’s also possible that your overall energy grade might stay the same, but end use grades (heating, cooling, baseload electric and baseload fossil fuel) might change. This is because the end-use and overall energy grades are calculated separately, and could differ for many reasons, such as:
- The distribution of energy use (and energy needs) between end-uses is often not equal.
- There can be no energy used for a specific end-use, giving a property an end-use grade of ‘N/A’, but that means less energy overall that will factor into the overall grade.
- You can’t tell if you’re on the border between two grades, so you might be just barely a ‘B’ for end-uses, but just barely an ‘A’ for overall energy.
- Certain types of fuel are more efficient than others, so a property may have an efficient fossil fuel baseload score, but compared to properties that have those end-uses filled by electric, may not be as efficient overall.
- Depending on the payment code, the metrics for end-uses may be measured against different building areas. For example, for TTOO buildings, the cooling index (and grade) is normalized by common area, whereas the overall energy index is normalized by total multi-family area.
How does this impact energy improvement recommendations I’ve already received from Bright Power (such as through an energy audit)?
EnergyScoreCards provides a high level analysis of building energy use. While EnergyScoreCards grades are an important input for our engineers, there are a lot of factors that go into producing the recommendations that go into an energy audit report, including equipment surveys, conversations with site staff, observations from a site visit, energy modeling, and our experience upgrading similar buildings. The energy improvement recommendations (or energy conservation measures) presented in an energy audit were custom created for your building using a lot more data than is available in EnergyScoreCards. EnergyScoreCards is a great place to keep track of these recommendations and to measure the actual energy performance improvements after you have implemented them.
How does this compare to Portfolio Manager’s Energy Star score calculation?
Portfolio Manager also uses a Machine Learning Regression algorithm to calculate their Energy Star score. However, we’re using a different type of algorithm that performs much better with different types of building data, and we’re ‘training’ our algorithm on a much larger database, making it both more accurate and better able to properly model many different types of buildings. We’re also only comparing buildings to those in their climate region, and we have a custom model for each of those regions. The EnergyScoreCards grading model also grades by energy end-uses (heating, cooling, baseload electric and baseload fossil fuel) in addition to total energy and water use, so you can see which parts of your energy consumption have the biggest potential for improvement. Finally, we’re able to grade buildings based on both the owner-paid portion of utility use or whole-building data, whereas whole-building data is required for an Energy Star score.
When is this rolling out?
Will historical grades change to the new algorithm?
Will I be able to access old grades?
Not on the EnergyScoreCards website. Your EnergyScoreCards Energy Analyst can supply you with a spreadsheet showing your Most Recent Year grades before and after the change for reference.
The Science Behind the Grades
EnergyScoreCards, Bright Power’s premiere utility bill analytics software, is releasing an upgrade to its proprietary grading algorithm in October 2016. With this upgrade comes improved accuracy, refined regional analysis, and a focus on the potential for saving energy and water at every building. We’re excited to release this upgrade and give our clients an even deeper, more sophisticated understanding of energy performance past, present and projected. In this blog, we’ll take a deep dive into our new grading methodology and explore the nuances of multifamily building energy analysis.
Why do we grade buildings?
Making intelligent decisions about how to manage energy and water at the portfolio level requires distinguishing between the good, the bad, and the liability in terms of efficiency. If a property owner has two buildings on the same block with very different energy usage, does that mean that the one using less energy is performing closer to its potential? Is it even worth looking into energy saving measures in the ‘better’ performer? What if the buildings are very different ages or have different types of tenants? Or what if one building is in NYC and the other is in Los Angeles?
EnergyScoreCards grades help answer these types of questions and allow you to understand how efficiently your buildings are performing, and where best to focus your efforts and resources within a portfolio to maximize savings. We do this by comparing energy and water consumption metrics for your properties to those of similar buildings in our database of over 20,000 multifamily buildings.
As anyone who knows multifamily buildings can tell you, defining what “similar buildings” means is easier said than done. Multifamily buildings vary in terms of their location, size, age, construction, system types, amenities provided, the size and configuration of apartments, and many other physical and operational factors. On top of that, a variety of metering and payment structures means that in some cases we have access to whole building data, and in others only the portion of consumption paid for by owners. All of these factors and more must be taken into account in order to develop a meaningful and accurate grading methodology.
As our database grows, we are better able to understand the energy and water needs of all different kinds of multifamily buildings across the country. We also continually update our software to incorporate the most state-of-the-art tools available to provide value for our customers. As such, we have recently updated our EnergyScoreCards grading algorithm and it’s our most substantial change to the grading system yet. Our new model is able to better define the concept of a ‘similar’ building and uses new statistical tools to figure out which characteristics are the most important when it comes to grading. With the new model in effect, grades will be ‘fairer’ and will better indicate potential savings by being based on things that owners can actually change.
The EnergyScoreCards Dataset
Our database includes over 20,000 buildings with 800,000 units grouped into more than 6,000 properties, each with over 30 data fields such as total square feet, building age and resident demographics. By analyzing all of this data, we can figure out which building characteristics have the biggest impact on energy needs, and use these to calculate building grades.
To get an idea of the geographic span of our dataset, here’s a map of the U.S. with bubbles representing properties grouped by location.
Why this matters to you
This recent upgrade to EnergyScoreCards is part of Bright Power’s ongoing efforts to continually improve our data analysis and software to give building owners all of the information and guidance they need to better manage energy and water at their buildings. With improved peer comparison, our grades allow for:
- More targeted audits
- Better distribution of resources across a portfolio
- Continued confidence in our ability to understand your building’s needs
Stay tuned for more improvements, we’ve got so much more on the way!
For more on how we’re better defining building peer groups, read on below:
Which building characteristics affect energy use?
One of the main features of EnergyScoreCards is the building energy grades, which allow property owners to understand how their properties compare to other buildings in terms of energy and water use. But determining a fair way to compare properties to similar buildings can be challenging. Our goal is to figure out what information is relevant to building energy use and how we can use this to group buildings with their peers.
There are many factors which contribute to a building’s energy use. Many of these are permanent factors, such as the age of the building or its size, and many are things that a building owner or manager can change, such as the specific heating, cooling and distribution equipment in the building. Our new algorithm does a better job of normalizing for the permanent features of a building, so that the grade is only based on the things that an owner can change – the fixable factors. So now you can know if the huge electric bill at your 30-story building is because it’s not performing efficiently or if it’s in line with other tall buildings with lots of elevators.
Our new algorithm also breaks up our dataset into more geographic regions that have similar climates and energy use patterns. You can see the new regions in the map below.
But how can we tell which building characteristics are important for determining how much energy a building should use? Fortunately, our new grading model can help answer this question, and ranks building characteristics on how much they impact a building’s energy and water use.
Once we understand what affects energy use, we can look at what types of buildings use more or less energy based on the most predictive characteristics. The graphs below show the Energy Use Index (EUI – kBTU/sqft/yr) vs different building characteristics, for all NYC buildings with owner-paid heat and hot-water. No discernable trends are apparent for any of these characteristics on their own…
…but when we look at the EUI compared to multiple characteristics at once we can find ‘pockets’ of parameter space that stand out. In the graph below, each circle represents a group of buildings binned by average apartment size and the age of the building. The size of the circle represents the number of properties in the bin and the color represents the median EUI of the bin. From this, we can see that old buildings with small average apartment size have the most intense energy use. While buildings with small average apartment size built around the 1960s also have relatively high EUIs, these constitute a relatively small number of properties (as shown by the circle size), and so it’s difficult to tell if this is a strong trend. On average though, energy use becomes more efficient with increasing apartment size for buildings of any age, which makes sense as you have fewer residents occupying the same space.
Similarly, the graph below shows how EUI varies with total building area and the average apartment size, and we see a trend of small buildings with small average apartment size using energy the least efficiently.
These graphs allow us to identify types of buildings that require more or less energy per square foot, even when operating efficiently. We can explore this trend further by looking at the distribution of EUIs for two ‘types’ of buildings:
- Old, small buildings with small apartments
- New, large buildings with large apartments
While there is some overlap in the distributions, there is definitely a tendency for old, small buildings with small apartments to use more energy per sqft. In our model, we want to grade these buildings fairly, so they’ll only be getting C’s and D’s if their use is inefficient compared to buildings like them, and not compared to all buildings.
How do we use this information to grade your buildings?
In order to fairly compare buildings to their peers, we calculate each building’s predicted EUI based on its permanent characteristics using our entire database of buildings. This predicted EUI can be thought of as its peer building’s use. To calculate this predicted EUI, we use a Machine Learning algorithm called a Random Forest Regressor. This algorithm is effective at fitting both numerical characteristics (such as building square feet) and categorical characteristics (such as the type of fuel used for end-uses), and it’s also not as susceptible to overfitting as other types of models (something data scientists always worry about). A Random Forest Regressor is constructed of many decision trees, each of which determines the energy use of a building by traversing through the tree based on all of its characteristics. The final predicted EUI is then calculated from the average predicted value from all of the 200 trees in a given model. Since our grading algorithm uses around 60 models total, that means we’re calculating the predicted EUI for buildings in our database using 12,000 different Decision Trees!
A portion of an example decision tree is shown below.
Example Decision Tree
To determine the predicted EUI for a given property, traverse the tree from left to right. At each node, take the top or bottom branch, depending on whether the statement in the node is true or false. For example, for a property with the following characteristics:
- Average apartment size = 1300 sqft
- Total size = 800,000 sqft
- Number of Units = 250
- Year built = 1950
You would traverse the tree, by answering: TRUE, TRUE, TRUE, FALSE
We then calculate the grades based on how much a building’s actual EUI compares to the predicted EUI, so the grades represent how much a building’s energy use differs from a typical peer building based on changeable factors.
With our improved peer comparison, you can think of your building’s energy grade as its potential for savings. A ‘D’ really just means you could be saving a lot of money! You wouldn’t expect your aging apartment building in Minnesota to ever have the same energy consumption per area as a new garden development in California, but with the right focused effort, it can still get an ‘A’.
To download the pdf version of this article, click here.