In an effort to save energy, reduce maintenance costs, and leverage investments in existing building management system, more and more facility managers are beginning to research and deploy fault detection and diagnostics (FDD) software platforms in their facilities.
The 411 on FDD
FDD platforms consist of three major components: a way of acquiring data, a set of algorithms for processing it, and a means of displaying the results. If that sounds similar to a building automation system, don’t be surprised: Most FDD systems are designed as add-ons to an existing building management system, though some standalone platforms do exist. The difference is that, while modern BAS collect and store vast volumes of data, creating actionable results from that data is a time-consuming and difficult task. As anyone who has spent hours poring over building trend charts trying to figure out why they always get a particular alarm at 2 a.m. can tell you, this is a task worth automating. But to really save time and money, FDD systems must accurately identify real problems and provide an easily digestible idea of how to resolve it, if not an outright diagnosis. Getting it right is no easy task.
As with many cutting-edge software products, not everyone means the same thing when they talk about FDD. Generalizations are difficult, as even a single vendor’s FDD product is often infinitely configurable. No two software platforms are identical. At worst, FDD software may be nothing more than a fancy way to create alarms and trend plots, albeit with much more functionality than is built into most BAS.
Trending features can include plotting multiple time series of overlapped data, which may help determine the causes and effects of a problem by plotting one BAS point as a function of another, or plotting a mathematical expression based on multiple BMS points. True FDD systems attempt to detect and diagnose equipment failures by applying a sophisticated rule-base — what a computer scientist would call an “expert system.” Such a rule-base contains both general knowledge (“there should be little to no flow in a chilled water loop if the chilled water pump is off”) and knowledge specific to the facility in which it is deployed (“the chilled water loop delta T should never be greater than 12 degrees F or less than 4 degrees F”). From this embodied knowledge the system can make sophisticated deductions. Some FDD platforms come with this rule-base predefined and uneditable. Others allow users to modify or add to it, though often with a steep learning curve.
When the right steps are taken, FDD can be a huge success, speeding the diagnosis of subtle problems that might otherwise have taken months or years to discover. As FDD becomes a more regular add-on or feature of BAS, and as the costs of hardware and data storage continue to decrease, FDD may see wider and deeper deployment. Operator training and trust, and the ability of FDD software to provide targeted information with explanatory context, will continue to determine the success or failure of future FDD systems.
See full article here.
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.
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.
It seems like every week a press release comes out about a new energy analytics platform – usually topped off with a breathless headline and peppered with buzzwords. “The Only Energy Management Solution You’ll Need.”
It seems like “energy management” has become a catchall for hardware and software companies to sell their products. Search on Google and you’ll find building management systems, controls software, electrical switchgear, and almost anything else you can think of that vaguely relates to energy. Building controls in particular seem to dominate the conversation, though little is said about how to use them.
But where have we really come since digital building controls and graphical interfaces were introduced in the 1980s? Take a look at any energy analytics platform that just took its Series B. It will be cloud-hosted – an admitted improvement – and it will have robust support for making an endless number of dashboards. But its core capabilities will be the same as they’ve been for thirty years: alerting, trending, reporting, and sometimes benchmarking.
Make no mistake, these can be invaluable, even essential tools, but the fact of the matter is that none of them on their own will effectively manage energy in your buildings. We see it all the time. A building operator may see a trend in the system – a pressure slowly increasing, or a sudden temperature change, for example. Sophisticated enough software might call this to their attention automatically, yet effective action is not always taken. Why?
There are many potential reasons. Fear stemming from organizational mistrust can breed a culture of silence where intervention is only made in the most dire circumstances. We commonly hear, “I don’t touch that” because of the potential repercussions, perceived or actual. Signs of a problem may also be subtle. Few real estate organizations can afford to place a highly trained engineer in every building, and it’s rare that a process exists to connect operators with the technical resources they would need to solve problems. Information on how problems were detected and solved is siloed across organizations. Experience and history are constantly re-learned instead of being disseminated across the organization. And the staff who operate the building are rarely incentivized to maximize its efficiency – policies and performance indicators don’t exist at the organizational level.
As a concrete example, we had a client whose chiller failed before a major event, resulting in an emergency service call. Because of its age and concern about reliability during multiple startups, it was decided to keep the unit running 24/7 for the rest of the season. All that added up to hefty repair, operating, and energy costs that were entirely unnecessary.
Avoiding a problem like that takes more than software. If staff were properly trained, better preventative maintenance would have taken place over the years. If good recordkeeping practices were implemented, and most importantly, if someone was responsible for trending and analyzing records on the chiller, they would have seen the chiller progressively losing vacuum over the course of the summer. They could have moved the issue up the chain of command to someone who had the authority to allocate capital to the problem. Action would have been taken proactively.
Management – of any kind – requires the integration of people, process, and technology. Just as you wouldn’t replace your COO with a business analytics platform, we believe you shouldn’t stop at buying an energy analytics platform. You’ll get valuable data, to be sure, but you need someone who understands it, is empowered to act on it, and has a process to effect change.
Otherwise it’s just another box in the boiler room, sitting on your control panel – next to the last box someone told you would solve all of your problems.
People we work with often ask us why this energy management stuff really has to be so hard. Can’t it just be automated, kind of like Turbo Tax? We totally understand. A user friendly app that ties up energy management in a neat little bow would be ideal, but can it be done? We say no. Why?
Let’s break it down.
Real estate owners and managers need a tool that is built to scale. Automatically managing energy in a single family home might be a realistic software venture, although it’s still usually just Mom or Dad yelling at the kids to turn off the lights, imploring them to take shorter showers, and turning down the thermostat at night. However, energy management across an entire real estate portfolio is a different ball game. While there are plenty of tools out there that can handle the large volume of data and provide valuable insights (hello, EnergyScoreCards) they can’t actually do the dirty work.
Energy management for a real estate portfolio, like all nuanced fields, has a threshold at which an expert is necessary. Throw in an HSA and a mortgage to your tax return and you’ll be screaming for an accountant. In energy management, there are so many moving parts that a software-only solution is not only unrealistic, it’s undesirable. Technology can and should play an important role, but ultimately a person, an expert, needs to be calling the shots.
Buildings are complex, living, breathing ecosystems. (That dampness in the air could be because the ventilation system isn’t working, but it could also be that the air conditioners are oversized. Fluctuating hot water temperature could be caused by a boiler problem, a mixing valve problem, or crossover flow between hot and cold water.) Not to mention that buildings house people, and people are very tricky, unpredictable variables. (That skyrocketing water bill might be a leaky toilet, but could also be a crazy tenant who leaves the water running for white noise. The gas spike could be a problem with the boiler, but it could also be a tenant who has started using their oven as a supplemental heater.) We’ve seen it all.
With all these nuances, there is no energy management software for real estate portfolios that can do it all on its own. Technology will continue to drive this field forward, but expert humans are going to play an important role for a long time to come. Besides, you wouldn’t scrap the accountant and use Turbo Tax for your business, would you?
Today’s interconnected, tech-enabled world has unleashed a powerful wave of optimization in most sectors of the economy. However, much of the energy and building operations infrastructure, particularly in the multifamily realm, is still ripe for innovation. In 2015, it’s hard to believe that some utilities still get away with employing human meter readers and sending bills by fax, but we see it every day!
The energy management landscape is primed for technology disruption. Who are our favorite companies doing just that? Read on for our list of companies to watch as the energy industry embarks on an age of transformation.
LogCheck‘s app connects maintenance staff to upper management more efficiently than ever before. The digital logbook modernizes the way maintenance staff run buildings on a daily basis by allowing them to create and maintain records, review historical data and easily share that information with building management, bringing operations and maintenance into the 21st century.
Geli, short for Growing Energy Labs, Inc. provides software and business solutions to design, integrate, network and economically operate energy storage and microgrid systems. At its core, the Geli Energy Operating System (EOS) is a software platform that brings together energy storage, distributed generation, EV charging and building controls as part of the Internet of Energy. Their EOS can even make decisions on when and how to run different energy assets. For example, based on the current price of power and energy, Geli’s software automatically makes decisions on when to sell energy back to the grid, to ensure you’re getting the best possible value.
Enertiv is a new breed of electricity monitoring company. Their software platform allows you to see real-time energy performance and provides actionable insights. Using a combination of proprietary meters and sensors, as well as by integrating with existing building systems, Enertiv makes real-time energy consumption in buildings 100% transparent down to the equipment level and provides round-the-clock access to fine-grained data. By helping owners, operators, occupants, etc. visualize and digest their consumption, Enertiv empowers change in an unprecedented way.
Urjanet addresses a fundamental market need for quick and easy access to uniform utility data. By providing an automated data feed of detailed and structured utility data to energy service and software providers, Urjanet eliminates manual energy data collection and cleansing for these companies and allows them to focus their time and resources on analysis and action. It’s hard to underestimate the value of reliable, structured utility data, especially when you realize that there are 35,000 different utility companies worldwide, each with their own utility bill format!
These are just a few of our favorites, who are yours? Tell us in the comments section below!
As recently as five years ago, the only means of sharing and collecting energy information, such as weather, temperature and meter readings, was to either download large text files, scrape information from websites, or collect and save the data yourself. Prior to 2009, integrating the measurement and analysis work remained a cumbersome, inefficient, and expensive task. The industry simply had not developed an easy way of transporting information from one software application to another. Change came when energy companies and organizations began to construct what the software world refers to as “web services” and “APIs”, a network of programs built to interpret, store, and send large datasets across the internet.
To clarify, web services and APIs are definitive ways in which programs talk with each other. The advent of REST and SOAP web service standardizations at the turn of the 20th century was fundamental in changing how development teams construct software. For the energy industry specifically, it meant facilitating and embracing real-time information across devices, meters, utility companies, and other related businesses and organizations. Specialized analytical software, like EnergyScoreCards, interacts with these service points, forms complex mathematical computations and displays visual models that can be understood by people. Based on those models, analysts and engineers can make nuanced decisions on how you can save money, time, and energy.
To quantify how the landscape of energy APIs has changed over the past 5 years, I searched for lists of service definitions and combined those results. I tagged each entry with two categories, updated the developer links where possible, and scoured the internet for any date to associate with the release of the software. Where a reliable date could not be found, I guessed. The results were interesting:
- 2012 accounted for approximately 50% of the energy API’s released (caveats apply to the release dates found)
- 57% can be categorized under Energy Consumption, Solar Renewables, and Monitoring & Control.
- About 8% of the APIs released in this time period are already no longer available for use.
Why is this list important?
- Gives insight into what sections of the industry have the greatest opportunities to connect with various networks of information.
- Visually represents the rate in which new sources of information and capability are entering the market.
- Communicates which types of services are available.
The hope is that understanding which services are out there can influence the industry’s creative processes to build new and increasingly smarter energy-minded products. Also, by inspecting services across each category, it is possible to compare how different groups conceptualize their information, and to identify common themes.
Based upon the information available, it can be reasonably interpreted that the number of APIs available is growing at an exponential pace. One of the largest issues is that, because these integration points are so new, the shape and standardization of the data will become increasingly important. Another aspect to consider is that at some point, all future devices entering the market will need to be web-enabled to some capacity. Gone are the days where your thermostats and even your lightbulbs can escape the vast clutches of the internet.
Below is the list of API’s used as a reference for this piece: