The Science Behind the Grades

October 3, 2016 Sarah Newman benchmarking, data, energy management, software 2 Comments

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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.

This map shows the distribution of properties in the EnergyScoreCards database, with the size of the bubbles representing the number of properties in a given 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:

buildings

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.

This map shows the different geographic regions used by our grading algorithm.

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…

These scatter plots show the relationship between EUI and average apartment size, total area and year built. None of these building characteristics alone appear to have a strong trend with EUI.

…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.

This graph shows buildings binned by average apartment size and the age of the building, each bin shown as a circle. The size of the circle represents the number of properties in the bin and the color represents the median EUI of the bin.

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.

This graph shows buildings binned by average apartment size and the total multifamily area, each bin shown as a circle. The size of the circle represents the number of properties in the bin and the color represents the median EUI of the bin.

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.

The two lines show the smoothed distributions of EUI values for different subsets of owner-paid heat and hot water buildings in NYC from our database. The purple line shows the distribution for old (before 1950), small buildings (less than 100,000 sqft) with small apartments (less than 1000 sqft), and the green line shows the distribution for new, large buildings with large apartments. To view an interactive version of this graph, click here.

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.

2 Comments

  1. Elizabeth Derry 1 year Reply

    Hi Sarah,
    This is a great article! Do you have it available in a pdf form? I’d like to send it to costumers that are considering signing up for CPC & Bright Powers joint energy benchmarking program.

    • Sarah Newman 1 year Reply

      Hi Elizabeth,
      Thanks for your interest! I’ve added a link to a pdf version at the bottom.

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