Carmel’s DOE SBIR Grant – A 2021 Status Update

Carmel’s DOE SBIR Grant – A 2021 Status Update

In June of 2020, Carmel Software received a U.S. Department of Energy Small Business Innovation Research (SBIR) grant to develop a new software tool to help energy modelers and energy auditors better design and maintain energy efficient buildings. The details of that grant were detailed in a prior blog post. This blog post will detail the progress that we have made so far. First, we need to restate the problem that has become even more urgent since last year:

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As part of its national infrastructure plan, the Biden Administration has set a goal to retrofit 2 million commercial and residential buildings over the next 4 years. Energy usage and energy auditing data for these buildings need to be stored in a consistent manner to help achieve this aggressive goal.

Simulating the energy usage of buildings using sophisticated software has become a key strategy in designing high performance buildings that can better meet the needs of society. Automated exchange of data between the architect’s software design tools and the energy consultant’s simulation software tools is an important part of the current and future building design process.

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Steady progress over the past two (2) decades has led to computers having a pervasive impact on the building design industry. Building Information Modeling (BIM) and advances in building energy modeling (BEM) software have resulted in their adoption into the mainstream design process. BIM authoring tools are being adopted by more architects and engineers as these tools improve and become faster and easier to use. The whole premise behind BIM is that it is essentially a “database” where all the building information, including the geometry, is stored.

However, there is a fundamental disconnect between many of the BIM, BEM, building analysis, building asset, and building auditing software tools. Because these tools are developed by 10s, if not 100s, of different software vendors throughout the world, many of these tools do not “talk” with one another despite the fact they many of them require the same information about a building: i.e. – building square footages, wall areas, window areas, occupant densities, plug loads, occupancy schedules, and much more. There are many software tools in the building design, analysis, and auditing industry that allow engineers, architects, and energy modelers to perform the following types of analysis, including whole building energy use, heating and cooling load analysis, lighting analysis, CFD analysis, solar/shading analysis, life-cycle cost analysis, energy benchmarking, energy auditing, and more.

The fact that many of these tools do not talk with one another discourages wide use of these software tools by energy modelers and other related practitioners. This is where “interoperability” comes into play. Interoperability allows for the sharing of data between different software tools developed by many different vendors. Interoperability is essential for BIM to realize its potential as a transforming technology as opposed to 3D CAD programs that are limited in their use as holistic building design tools. In addition to BIM and BEM software, interoperability applies to additional software tools related to building asset information, building audit information, and energy benchmarking. This is where schemas such as gbXML, HPXML, BuildingSync XML, IFCs (Industry Foundation Classes) and others become quite relevant.  For example, BuildingSync is a schema developed by the National Renewable Energy Lab (NREL) that allows for the exchange of building energy audit information such as energy efficiency measures, utility data, and building rating information. This information can be used by other types of software tools including energy benchmarking software (such as ASHRAE Building EQ ( ), energy auditing software such as buildee, and custom software developed by cities and municipalities to satisfy energy auditing rules and mandates. Another example is Green Building XML (gbXML) which is the “language of buildings”. It was developed to facilitate the transfer of building information stored in CAD-based building information models, enabling interoperability between disparate building design and engineering analysis software tools. It is currently supported by over 55 software tools worldwide.

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While interoperability schemas have been around for twenty (20) years and are integrated into all major BIM and building performance software tools, end-users still struggle with inefficient and ineffective workflows. For example, geometric information from one BIM authoring tool is not properly represented in a popular HVAC load calculation software tool developed by a third-party vendor. While users can always manually edit and tweak data in an XML file (fortunately, it is clear text) so that it successfully imports into a consuming software tool, the ideal interoperable workflow should not include any type of human intervention.  In fact, the ideal workflow would comprise of seamless data transfers between software tools with a simple press of a button. While this may be a utopian vision, there is no reason why the current state-of-the-art cannot be dramatically improved.

In the original Phase I funding opportunity announcement (FOA), DOE’s Building Technologies Office (BTO) requested that research and development be conducted for innovative delivery models for increasing access to building asset data from tools such as Home Energy Score and Asset Score ( For Asset Score’s Audit Template tool, one of the ways to increase access to this data by third-party software tools is using an interoperability schema such as NREL’s BuildingSync XML.

BTO asked that bidders suggest new workflows for either BuildingSync or HPXML. Our proposal focused on BuildingSync XML since we wish to target the commercial building space. We suggested developing a comprehensive web-based portal (or Software as a Service, SAAS) that would help facilitate the adoption of BuildingSync and other similar interoperability schemas by third-party building analysis, auditing, and benchmarking software tools. In our 20 years of experience developing and managing the popular Green Building XML (gbXML) schema, we have come to realize that schemas do not work for end users unless there is some type of “transport” mechanism and validator modeler that ensures successful importation into a consuming tool in a straightforward and efficient manner.

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As part of Phase I of the FOA, Carmel Software developed a prototype of the SAAS described above, called Schema Server. This tool can import a BuildingSync XML file from any source (including U.S. Department of Energy’s Asset Score Audit Template), perform basic validation using Schematron technology. Additionally, this tool can import additional data for the same building from Energy Star Portfolio Manager and then send it over to a consuming software tool such as ASHRAE Building EQ to receive a building energy benchmarking score. Building EQ assists in the preparation of an ASHRAE Level 1 commercial energy audit (as defined by ASHRAE Standard 211) to identify means to improve a building’s energy performance including low-cost, no-cost energy efficiency measures and an indoor environmental quality survey with recorded measurements to provide additional information to assess a building’s performance.

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Also, as part of Phase I of the FOA, we conducted a lot of market research. The reason we were able to do this is we were accepted to DOE’s Energy I-Corps program, a key initiative of the Office of Technology Transitions. This program pairs teams of researchers with industry mentors for an intensive two-month training where the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. Researchers return to the lab with a framework for industry engagement to guide future research and inform a culture of market awareness within the labs. In this way, Energy I-Corps is ensuring our investment in the national labs is maintaining and strengthening U.S. competitiveness long-term.

We found the Energy I-Corps training to be very valuable. It taught us some great concepts such as the Business Model Canvas, the Ecosystem Model, Timeline, Lean Startup Method, and other great concepts. In addition, and most importantly, it held us accountable to conduct 30 interviews within a 6-week period. In fact, we ended up conducting 60 interviews over a 6 month period. As we got better at interviewing, we were able to really target the right stakeholders and get the exact type of information we needed to develop a better software tool.

We recently developed a proposal for Phase II of this FOA that will add much more functionality based upon our interviews with industry stakeholders. The critical need we are focusing on is getting energy auditing and performance data from one software tool to another so that stakeholders are able to do the work accurately and quickly and make better decisions for building energy design and retrofits. For Phase I, we focused on just one workflow for our prototype: Transferring building energy auditing data from Asset Score Audit Template to the ASHRAE Building EQ benchmarking software discussed above. After interviewing the 60 potential stakeholders discussed above, we determined that the above workflow does not satisfy an overwhelming need for most users. However, this software platform (Schema Server) that we created in Phase I will be the basis for Phase II development.

For Phase II, we will be expanding the number of software tools that Schema Server currently focuses on. We will also be combining disparate data about the same building from multiple sources: 3D geometric data about a building may reside in a popular BIM authoring tool while historical electrical utility data may reside in Energy Star Portfolio Manager and energy auditing data may reside in Asset Score Audit Template. There are many other features we will be incorporating that will be discussed in future blogs.

Bicycle Diaries

Bicycle Diaries

This blog post has nothing to do with HVAC software, nor sustainable design software, nor, as a matter of fact, any type of software the Carmel develops. However, I felt the need to write this post since it deals with reducing greenhouse gas emissions, which is what our software is all about.

In fact, our software is all about helping engineers, architects, and technicians design more energy efficient buildings. Also, Carmel oversees the Green Building XML (gbXML) schema that allows disparate software tools in the building design space to communicate with one another, all in the name of designing more energy efficient buildings.

I truly hope our software is doing its part to help reduce our greenhouse gas emissions that are adversely affecting our planet in so many ways. I’ve lived in California for 20+ years, but only during the past 3 years have the wildfires consistently affected our way of life. I’ve always been wary of whether man truly affects our environment, but now that I am seeing it and experiencing it first-hand, I now believe it, hands-down. I have personally witnessed a marked and extreme effect of climate change: wildfires quite literally in my backyard and air so unbreathable that I need to wear N95 masks outside when on a run or bike ride. This has consistently happened over the past 3 late-summers/falls and prior to 2017, this NEVER happened. Something is seriously wrong.

Living in California, I am seeing more and more how man is altering our environment for the worse. Paradise, California has burned twice in the past two years. If that isn’t a message, I don’t know what else is.

That being said, I’ve decided to do my small part in reducing greenhouse gas emissions by biking to work 2 days/week. I am lucky enough to live within 10 miles of my office and also to live in a part of the country where the weather is fairly predictable during the spring and summer. Therefore, I am able to consistently bike to work each week.

Being a degreed professional mechanical engineer and software designer, I’d like to focus on the metrics of biking each day: how much I’m biking, calories I’m expending, gas I’m saving, and greenhouse gas emissions I’m reducing. However, before I delve into the metrics of biking, I’d like to talk about the more intangible benefits and features.

First of all, I truly love biking. There’s something about it that is so visceral for me. If I lived in the 1800s in the American Wild West, I probably would have loved riding horses. In fact, there are similarities between bicycling and riding horses: being thoroughly engulfed in nature, the wind, the natural means of speed beyond human capabilities. I often visualize my long bike rides during the week, anticipating them with increased urgency. In fact, I’ll admit, I’m a bit crazy when it comes to bicycling. I’ll get up at 4:30 am on Sunday mornings and bike 55 miles in 40 degree weather. I’m the only one on the road in West Marin County at that time. The comedian/actor Robin Williams (an avid biker who owned 50+ bikes) once said: “My favorite thing to do is ride a bicycle. I ride road bikes. And for me, it’s mobile meditation.” I can relate.

When it comes to biking to work, even after biking only 8 or so miles, I feel great. Yes, I am sweaty and need to change my shirt and apply deodorant, but I am energized and awake. Compare this to driving, I often have to fight to stay awake while drive just 8 miles to work. And when I do arrive to work, I am sometimes lethargic. I am NEVER lethargic when biking to work.

Another intangible benefit is that I’m super hungry and thirsty at the end of the day after biking just 16 miles to and from work. Food always tastes so much better on a hungry stomach.

Now, let’s talk metrics: I LOVE metrics and both the Garmin mobile app (I own a Garmin watch) and the Strava website provide tons of data and metrics.

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Garmin Venu GPS Watch

My average time to work from home is about 40 minutes (compared with 15 minutes by car). The timing varies by 5+ minutes depending upon if I catch traffic lights and light-rail intersections. Also, other variables such as outside air temperature and wind speed affect my time: the colder it is (and the higher the wind speed, obviously), the slower I bike (often by 5 to 10 minutes).

The exact instance to work is 7.68 miles. The elevation is 500 ft. My average speed is 12 mph. There are a number of traffic lights on the way to work (5 to be exact). Also, there is a light-rail crossing which often causes a 5 minute delay. The following is a screenshot of one of my rides to work:

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There’s a nice gradual elevation that runs parallel to Highway 101 (located in Marin County, CA, just north of San Francisco) that runs for about 1 mile or so. Following that elevation, the rest of the ride to work is either downhill or at an even elevation.

Calories burned are about 300 according to app. Realistically, I think calories burned are about 250 (I am 6’1″ and weigh around 155). Anecdotally, I’ve calculated that for each mile I bike, I burn around 35 calories (this is an average since some miles are downhill and others can be brutally uphill). Since I am also a runner, I calculated that every 3 miles of biking is equivalent to 1 mile of running in terms of calories burned (ie – 100 calories / mile of running).

Therefore, by bicycling to work, I am doing the following:

  1. Riding 35 miles/week to work versus driving. This saves around 1.5 gallons of gas per week or 75 gallons of gas per year. While this is equivalent to only around $300/yr savings (I spend more than that on bike maintenance, new tires, and new brakes/year), if just 1,000,000 people (0.3% of the US population) nationwide did this, then we are talking 75,000,000 gallons of savings per year.
  2. I’m reducing mileage on my car to the tune of 1,800 miles/year, which does translate into reduced wear and tear on my car and tires. This equates to an additional $500 in savings in terms of wear and tear. Also, due to COVID-19, car insurance companies are reducing premiums for less miles driven since so many people are working from home.
  3. In terms of carbon reduction, I’m reducing CO2 emissions by 0.61 metric tons/year.
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What about health benefits? I’m lucky to already be in good shape and at a good weight for my height. However, given the fact that over 30% of the US is obese, what if every American biked an average of 15 miles to and from work 2x per week, how much weight loss are we talking? As I mentioned above, the average calories burned are around 500 calories/ride back and forth. Since a pound of weight is equal to 3600 calories, it takes around around 7 back/forth rides to reduce 1 pound (assuming you do not eat more to offset the calories burned). Assuming you ride 2x/week, this translates into about 1 lb/month or 12 lbs/year. For someone who is 50 lbs overweight, this translates into a relatively easy way to lose that weight over a 4 year period (yes, I admit it’s not a fast way to lose the weight, but it’s a slow/consistent way to lose it and keep it off forever). Most weight-loss programs cannot claim this.

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Other observations while riding a bike:

When in a car, it’s so easy to become disconnected from your surroundings and environment. You’re in a climate controlled environment with windows closed often listening to music or talk radio. However, when riding a bike, you are inherently immersed in the surrounding environment. It’s so much easier to observe the environment, both good and bad.

The bad:

Trash: I’m appalled by how much litter is on the sides of downtown roads and highways. It boggles my mind that people would throw trash outside their car instead of waiting to get home to place it in garbage bin.

Homelessness: While it’s hard to avoid seeing the homelessness while in a car; when on a bike, you feel the proximity even more so. Plus, you can hear their ramblings. It makes one realize how sad a situation homelessness is and how hopeless these people feel.

The good:

I feel I really get to know the town I live in by bicycling through it. It gives me a renewed appreciation for it and helps me feel more connected to it.

Parking is NEVER A problem. I can stop at the bank downtown without worrying about finding and paying for parking.

Most importantly, biking ALWAYS feels like an adventure to me. There’s constantly challenges and encounters that make it so engaging and interesting. Even picking up my wife’s prescriptions from the pharmacy feels like a fun adventure. Go figure.

To the contrary, driving does not feel like an adventure. In fact, it feels like a necessary burden. I always have a dreadful feeling driving due to bad traffic expectations, backups through downtown, lack of parking, etc. There’s NEVER a traffic backup when you are biking.

I recently bought a 2nd bike. My commute bike is on a 3-year old Trek FX 6 Sport hybrid, which has been great for commuting. My new bike is a Trek Domane TR 7 high-performance road-bike. What an engineering marvel: electronic shifting, incredible performance, hydraulic brakes, and so much more. Anecdotally, I compared my time over a 5 mile route on my hybrid vs. my new road-bike: a 6 minute improvement over my hybrid for every 5 miles. It really pays to have the right tools.

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In summary, I wish I could convince more people to bike to work. It’s hard, I know, but Mother Earth is revolting against the excesses of 8 billion+ human beings: wildfires in CA and the Amazon, the most powerful hurricanes in recorded history, world-wide droughts, a Northwest Passage free of ice for the first time in human history. Look at the 2 pictures below. Need I say more?

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Barrow Strait (Northwest Passage) in 1994 Lots of ice
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Barrow Strait (Northwest Passage) in 2007 – No ice

Carmel Receives Grant to Develop Building Energy Auditing Software Interop Tool

Carmel Receives Grant to Develop Building Energy Auditing Software Interop Tool


Carmel Software has recently received funding from the U.S. Department of Energy’s Building Technology Office (BTO) to develop a software tool that helps disparate software tools in the building energy audit space to communicate with one another, all in the name of reducing building energy usage. Remember, buildings use 40% of all energy in the United States, so this is a HUGE problem to solve.

The BTO released a Funding Opportunity Announcement (FOA), or a request for proposal in government-speak, to expand the number of third-party software tools that can import data from DOE’s Asset Score Audit Template ( ) and the accompanying data format that it supports: BuildingSync XML ( ).

Energy Audit

First, what is a building energy audit? The purpose of an energy audit is to determine where, when, why and how energy is used in a facility, and to identify opportunities to improve efficiency. Energy auditing services are offered by energy services companies, energy consultants and engineering firms. The energy auditor leads the audit process but works closely with building owners, staff and other key participants throughout to ensure accuracy of data collection and appropriateness of energy efficiency recommendation. The audit typically begins with a review of historical and current utility data and benchmarking of the building’s energy use against similar buildings. This sets the stage for an onsite inspection of the physical building. The main outcome of an energy audit is a list of recommended energy efficiency measures (EEMs), their associated energy savings potential, and an assessment of whether EEM installation costs are a good financial investment.

There are 3 levels of energy audits according to ASHRAE:

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ASHRAE Energy Audit Levels

Level I: Site Assessment or Preliminary Audits identify no-cost and low-cost energy saving opportunities, and a general view of potential capital improvements. Activities include an assessment of energy bills and a brief site inspection of your building.
Level II: Energy Survey and Engineering Analysis Audits identify no-cost and low-cost opportunities, and also provide EEM recommendations in line with your financial plans and potential capital-intensive energy savings opportunities. Level II audits include an in-depth analysis of energy costs, energy usage and building characteristics and a more refined survey of how energy is used in your building.
Level III: Detailed Analysis of Capital-Intensive Modification Audits (sometimes referred to as an “investment grade” audit) provide solid recommendations and financial analysis for major capital investments. In addition to Level I and Level II activities, Level III audits include monitoring, data collection and engineering analysis.

Software Tools We are Working With

Let me explain what some of the tools we are working with:

Asset Score is a national standardized web-based software tool that can be used to assess the physical and structural energy efficiency and identify retrofit potentials of commercial buildings using whole-building simulation. The Audit Template tool is a subset of Asset Score and is used to create a standard building energy audit report and submit to selected jurisdictions to comply with local ordinances, such as New York City’s LL87 or San Francisco’s BRICK.

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DOE Asset Score Audit Template Tool

BuildingSync® is a common XML schema for energy audit data that can be utilized by different software and databases involved in the energy audit process. It allows data to be more easily aggregated, compared, and exchanged between different databases and software tools. This streamlines the energy audit process, improving the value of the data, minimizing duplication of effort for subsequent audits, and facilitating achievement of greater energy efficiency. BuildingSync can be exported from Audit Template so all information in an Audit Template project can be used externally by another software tool.

BuildingSync was developed to address the lack of an industry-standard collection format for energy audit data. Standardizing energy audit data can help energy auditors, software providers, building owners, utilities, and other entities by maximizing the value that can be obtained from each set of data – value obtained through collaboration, comparison, and reuse.

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BuildingSync Use Case Selection Tool

ASHRAE Building EQ (see blog post here about ASHRAE Building EQ) is a web-based portal that provides a quick energy analysis that benchmarks a building’s energy performance. Building EQ assists in the preparation of an ASHRAE Level 1 Energy Audit to identify means to improve a building’s energy performance including low-cost, no-cost energy efficiency measures and an Indoor Environmental Quality (IEQ) survey with recorded measurements to provide additional information to assess a building’s performance.

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ASHRAE Building EQ Software Portal

The Problem and Solution

Currently, the number of software tools that import Asset Score Audit Template data and BuildingSync is limited. The whole purpose of Asset Score Audit Template is to store energy efficiency data about a commercial building. However, this data is useless if it cannot easily be consumed by other related software tools that can perform building energy benchmark tests, building energy modeling, and other types of building analysis. The term used to describe how one software tool communicates with another is: interoperability.

Unfortunately, developing interoperability integration tools in existing building analysis software is a tedious and time-consuming process. Therefore, it discourages software developers from creating functionality such as those that import BuildingSync.

For Phase I of this DOE SBIR project, we proposed developing a web-based software tool (called Schema Server) that will completely streamline the flow of information from Asset Score Audit Template into third-party software tools such as ASHRAE Building EQ, so that all the user has to do is press one button on the producing or consuming software tool, and the software will perform quick data checks and validation and then seamlessly transfer data to the consuming tool, thereby eliminating the user having to manual enter the data. Phase I will focus solely on the workflow from Asset Score Audit Template to ASHRAE Building EQ. Once it is proven that this workflow can be streamlined, future phases will focus on other software tools. Phase I will also focus on making it easier for a third-party software developer to program BuildingSync import functionality into their building analysis software tool.


Designing energy efficient buildings is of utmost importance today due to a wide variety of factors including limited fossil fuel resources, pollution, global climate change, federal and state laws, high energy costs, and a host of other reasons. Buildings use 40% of all energy and a whopping 75% of electricity. If society is going to rely less on fossil fuels, we need to design more energy efficient buildings for both new and existing construction. The first step in designing more energy efficient buildings occurs during the initial design phase which involves running building energy simulation and analysis software that will predict yearly building energy usage. Improving the interoperability workflows discussed above will benefit the following stakeholders:

  1. Energy modelers: Give them more incentive to use various software tools to design energy efficient buildings since it will be an easier and more seamless process to enter the same data in more than one BIM authoring and building analysis or benchmarking software tool.
  2. Software developers: Gives them more incentive to integrate interoperability functionality into their tools if there is an easier and less expensive way to do it.
  3. Building owners: By designing more energy efficient buildings, it will save building owners a significant amount of money in utility and energy costs over the lifetime of the building.
  4. Society as a whole: Whatever people’s political beliefs, there is no arguing that our fossil fuel supplies are finite, we are polluting the earth, and adverse climate changes are occurring all over the world including in our own backyard of California with unprecedented wildfires. Designing energy efficient buildings is just one step toward reducing our reliance of fossil fuels and cleaning the air for future generations.
Artificial Intelligence and Python

Artificial Intelligence and Python


During the downtime due to the pandemic-inspired shelter-in-place in California, I’ve decided to learn some new technologies and concepts related to software design. This has been my first foray into online learning using one of the e-learning platforms. In this case, I signed up for Udacity (

So far, I’ve been quite impressed. Of course, it does not perfectly substitute live classroom instruction, but it is still quite effective, plus I can skip to the good parts. The course I signed up for is titled “Artificial Intelligence and Python”.

While I’ve programmed scripts with Python before, I never knew that it had some many math and statistic-intensive libraries. Hence, it’s a great language for developing AI-related software since AI is all about statistics: predicting future events based upon historical information.

While the basics of Python programming is not that interesting, the libraries and tools associated with Python are fascinating and actually lots of fun to work with. This blog post will talk a bit about those libraries and also how they apply to AI. As of mid-April 2020, I have not finished the course yet, so emphasis is on the Python libraries used for AI, but not quite AI, itself.


NumPy is a library for Python. It is short for “Numerical Python”, and it includes a large amount of functionality related to multi-dimensional arrays and matrices in addition to many mathematical functions. Users can create arrays from Python dictionaries, and then manipulate the arrays in many different ways including reshaping arrays, adding arrays, multiplying arrays, and much much more.

Here’s an example of how a simple numpy array works in Python:

1. import numpy as np
2. x = np.array([0,1,2,3,4,5,6,7,8,9])
3. print(x)
4. [0 1 2 3 4 5 6 7 8 9 10]

Line 1 imports the numpy library and renames it.
Line 2 defines a single row array with values from 0 to 9.
Line 3 prints the array and Line 4 displays it.

Let’s take it a step further:

5. x.reshape(2,5)
6. print(x)
7. [[0,1,2,3,4]

Line 5 executes the “reshape” function that changes the shape of the array from a single row to a 2×5 array as seen in Line 7.
Other functions allow you to insert rows in an array:

8. x = np.insert(x, 1, [10,11,12,13,14], axis=0)

The above “insert” statement inserts a new row at row 1 (row numbers start at 0). The numbers it inserts are: 10,11,12,13,14. The “axis” tells whether to insert a row (0) or column (1).

You can also perform mathematics on 2 arrays including addition, subtraction, multiplication, and division. For example:

x = [[0,1,2,3,4]

y = [[6,3,2,8,7]

print(x + y) = [[6,4,4,11,11]

The above “x” adds each of the elements from each row and column and creates the corresponding matrix with the added values. You can also do the same with the other mathematical functions.

There are many functions associated with numpy that can be found in lots of online documentation.


Pandas is another Python library that deals with data analysis and manipulation. It takes the numpy arrays one step further and allow the creation of complex arrays. Let’s look at an example:

  1. import pandas as pd
  2. groceries = pd.Series(data=[30,6,’Yes’,’No’], index=[‘eggs’,’apples’,’milk’,’bread’])

Line 1 imports the Pandas library. Line 2 creates a complex matrix where the first column is the index of labels and the 2nd column is the actual data. It looks like this:

eggs 30
apples 6
milk Yes
bread No

dtype: object

If you “print groceries[‘eggs’]”. The result is: 30.

Pandas allows you to perform mathematics on values in a matrix:

print(groceries / 2) =

eggs 15
apples 3
milk Yes
bread No

You can also create a more complex matrix by creating a dictionary of Pandas series:

3. items = {‘Bob’ : pd.Series(data = [245, 25, 55], index = [‘bike’, ‘pants’, ‘watch’]),
‘Alice’ : pd.Series(data = [40, 110, 500, 45], index = [‘book’, ‘glasses’, ‘bike’, ‘pants’])}
4. shopping_carts = pd.DataFrame(items)
5. shopping_carts             

  Bob Alice
bike 245.0 500.0
book NaN 40.0
glasses NaN 110.0
pants 25.0 45.0
watch 55.0 NaN

shopping_carts[‘Bob’][‘Bike’] displays “245.0”.

You can also create a Python dictionary and then create a Panda dataframe from the dictionary along with indexes. See the following:

#Create a list of Python dictionaries
items2 = [{‘bikes’: 21, ‘pants’: 36, ‘watches’: 40},  {‘watches’: 12, ‘glasses’: 51, ‘bikes’: 18, ‘pants’:9}]

#Create a Panda DataFrame
store_items = pd.DataFrame(items2, index=[‘store 1’, ‘store 2’])

#Display the DataFrame

It displays as:

  bikes pants watches glasses
store 1 20 30 35 NaN
store 2 15 5 10 50.0

To add a column: 

store_items[‘shirts’] = [15,2]

Now, the DataFrame displays:

  bikes pants watches glasses shirts
store 1 20 30 35 NaN 15
store 2 15 5 10 50.0 2

Anaconda and Jupyter Notebooks

Now that I have covered a bit of NumPy and Pandas for manipulating data arrays, let’s delve a bit into a Python platform called Anaconda. Anaconda is a “navigator” that allows users to download any and all libraries available for the Python platform. These libraries include mathematical libraries, different types of Python compilers, artificial intelligence libraries (like PyTorch) and the Jupyter Notebook which is a web-based user interface for displaying comments and typing in Python code that runs on command. It’s a tool not necessarily to write production-level Python code, but more a tool to train and test out python code while displaying well-formatted comments.

Below is an example of a Jupyter webpage (or notebook) that includes a comments section with images and then a subsequent code section. This Jupyter notebook talks about Python tensors and Pytorch, the essentials for artificial intelligence.

Sample Jupyter page


Neural Networks

Neural networks have been around for a while. The basically emulate the way our brains work. The networks are built from individual parts approximating neurons which are interconnected and are the basis for how the brain learns.

“Digital” neurons are no different. They are interconnected in such a way that over time they learn and are able to apply the learned knowledge to enable useful applications such as natural language (like Alexa) and image identification (like Google Lens). It really is amazing how well it works, and the progress over the past five years alone has been remarkable. I’ll talk more about that later.

So how does it work exactly? Let’s take the example of identifying text in an image; specifically, digits 0 to 9. Just 5 years ago, this was a very complicated problem. Today, it’s a trivial one. The image below displays greyscale handwritten digits where each image is 28×28 pixels.  

Greyscale handwritten digits

Greyscale handwritten digits

The first step is to train the software or the “network” in AI lingo. This means feeding it 100s if not 1000s of sample 28×28 pixel images of digits and tagging those images with the actual numbers so the software learns what number the image represents exactly. Luckily, Pytorch includes lots of tagged training data called MNIST. This data can be used to train the network so when you present your own image of a digit, it will correctly interpret what it is.

Single digit

Single digit

The above image is an example of a greyscale “8” that is 28 x 28 pixels. This is the type of image that would be fed into the network to train it that this type of image is an “8”.

Neural Networks

Neural Networks

The above images shows a simple neural network. The far left-hand side displays the inputs (x1 and x2). In our example, the inputs would be the color of each of the 28 x 28 pixels. The values (w1 and w2) are called “weights” These weights are multiplied by each of the corresponding inputs (i.e. – dot product of two vectors) and then inputted into a function that creates an output value (0 to 9) that is compared to the actual value assigned to the image. For example, in the digit training image above (the number “8”), the tag assigned to this image is 8. Therefore, the calculated output is compared to the tagged value. If it matches, then we’ve trained it well for that particular test image and the weights will be reused. If not, then we need to go back and adjust the weights to create a new output value. This back and forth can occur 1000s of times until the correct formula is found.

ASHRAE Building Energy Quotient (Building EQ) Website

ASHRAE Building Energy Quotient (Building EQ) Website

ASHRAE’s Building EQ Web Portal provides a quick energy analysis that benchmarks a building’s energy performance. Building EQ assists in the preparation of an ASHRAE Level 1 Energy Audit to identify means to improve a building’s energy performance including low-cost, no-cost energy efficiency measures and an Indoor Environmental Quality (IEQ) survey with recorded measurements to provide additional information to assess a building’s performance.

Two different evaluations can be used independently to compare a candidate building to other similar buildings in the same climate zone or together for an assessment of a building’s design potential compared to actual operation:

In Operation compares actual building energy use based on metered energy information.

As Designed compares potential energy use based on the building’s physical characteristics and systems with standardized energy use simulation.

The Old Way

When Building EQ was first introduced, building owners and engineers could submit information about their candidate building to ASHRAE using an Excel spreadsheet template. This was a very inefficient way of doing things. It involved filling out the required data into a spreadsheet, and then uploading the spreadsheet(s) to the ASHRAE Building EQ website. Then, ASHRAE personnel would open the spreadsheet and determine whether the data was valid, and if it was, what rating to assign the building. The spreadsheet method was wrought with many inefficiencies including:

  1. If data was missing or invalid, the spreadsheet would be sent back to the building owner to be corrected. This involved working with multiple versions of the spreadsheet which could very quickly become confusing and could potentially result in working with outdated and incorrect data.
  2. ASHRAE personnel would validate all of the data manually, which was a slow and inaccurate process.
  3. There were no links within the spreadsheet to ENERGY STAR Portfolio Manager that would allow users to migrate data to/from other programs into bEQ.
  4. The spreadsheet was only in IP units.
  5. Many more inefficiencies too numerous to list here

The New Web Portal

In 2016, Carmel Software was hired to develop the web-based user interface that would solve all of the problems above and introduce even more efficiencies and features that a spreadsheet could never provide. In addition, Carmel has tasked to develop a website that would be able to accommodate many types of connected devices including Windows and Mac desktops/laptops, all iOS and Android mobile smartphones and tablets. After about 9 months of development, the ASHRAE Building EQ portal was officially launched, and it has been a resounding success. More project submissions were made within the first 2 months of the website launch than within the first 5 years of existence of the Building EQ rating system with only spreadsheet submissions. As of November 2019, over 500 projects have been submitted.

BEQPortal - Carmelsoft Blog

What Does Building EQ Measure?

Improve Performance Icon - Carmelsoft Blog

The Building EQ rating system rates building energy usage only. It is not meant to compete with LEED which measures far more including water usage, material sourcing, and much more. The Building EQ rating system works as follows:

  1. Based upon the building type, climate zone, and heating and cooling degree days, a lookup for a benchmark value is performed using an ASHRAE Standard 100 site median table derived from CBECS (Commercial Building Energy Consumption Survey) 2012 building energy usage data. This usage data is expressed in units of energy use intensity (EUI)  which is the amount of energy used per square foot per year.
  2. The user then enters a year’s worth of utility data and the total square footage of the building to calculate the specific building’s EUI. This building-specific EUI is compared with the normalized benchmark EUI  and a Building EQ score is derived by dividing the two numbers then multiplying by 100. The range of the score is from 0 to 200 where 0 is the most energy efficient and 200 is the least. A score below 100 is considered energy efficient since the specific building beats the benchmark EUI derived from CBECS.

Additional Inputs

There are many additional inputs in the ASHRAE Building EQ web portal above and beyond those that are used to calculate the Building EQ score. Below is a list of these additional inputs for the In Operation method:

Building Performance Credentials

The “Building Performance Credentials” section allows the user to input any other ratings or scores the building may have received including Energy Star, LEED, Green Globes, and more.

Building Performance Chars

IEQ Screening

The “Indoor Environmental Quality Screening” tab includes a number of accordions (sections) that allow the user to input additional information about the building.

The objective of the building indoor environmental quality (IEQ) screening is to verify that the IEQ of the building as it affects the occupants has not been obviously compromised in the pursuit of energy efficiency and energy savings. The screening is intended to go beyond professional judgment with the inclusion of actual measurements. The measurements are focused on areas identified in the screening and are therefore representative of the building spaces and not intended to be all inclusive. If no issues are identified, the Assessor should take representative spot measurements throughout the building in order to provide feedback to the building owner/operator. Representative space types may be determined by space type (office, conference room, corridor), by space usage (different tenants or floors), or by space system type (building served by multiple system types). The information is provided to the building operator for follow-up actions and to benchmark, evaluate, and diagnosis building systems that affect indoor environmental quality including thermal comfort, lighting quality, and ventilation for indoor air quality. The IEQ screening is not intended to serve in the place of a full IEQ evaluation performed by an expert in that field. For this reason, it is important that the building owner follow up separately on any deficiency or potential problem noted on the forms by having a full IEQ evaluation performed by a qualified professional.

Indoor Environmental Quality

Energy Efficiency Measures (EEMs)

This tab allows the user to input any energy efficiency or conservation measures that have been implemented. The measures are divided by category: Building Envelope, Lighting, HVAC, Refrigeration, Energy Generation/Distribution, Other). Within each accordion is a drop down with a list of pre-populated measures.  These measures are outlined in Informative Annex D and Informative Annex E of ASHRAE Standard 100-2015. The measures are divided by category: Building Envelope, Lighting, HVAC, Refrigeration, Energy Generation/Distribution, Other).

Building EQ EEM Categories

The user is also able to enter a cost range and payback period for each measure.

There are 3 additional inputs in each accordion that allow the user to input their own custom measure descriptions along with cost ranges and payback periods.

Building EQ Example EEM

Photos and Attachments

This final tab allows users to add photos and attachments along with descriptions and categories. These photos will appear in the narrative report.

Building EQ Photo Tab

Building EQ Reporting

Standard 211 Audit Spreadsheet

Building EQ does something else that no other rating system does: It works closely with ASHRAE Standard 211 – Standard for Commercial Building Energy Audits. This is an ANSI standard that formalizes the process of performing building energy audits. ASHRAE Standard 211 protects a building owner/operator’s energy audit investment by providing an outline for auditors and offering best practices that ensure quality audits. It sets forth requirements for the experience and credentials of energy auditors, specifications for compliance and clear definitions of the audit processes and scope.

A Standard 211 audit spreadsheet is included along with the actual text of the standard. This spreadsheet allows users to fill in all information related to Level 1 and Level 2 energy audits.

Remember, the primary function of an energy audit is to identify all of the energy streams in a facility in order to balance total energy input with energy use. The ASHRAE Level 1 is a simple and quick audit that requires a brief review of building operating characteristics. It mainly identifies low-cost/no-cost measures and will only uncover major problem areas. Level 1 audits are a great way to prioritize energy efficiency projects and to assess the need for a more detailed audit. The ASHRAE Level 2 audit includes the Level 1 audit plus more detailed energy calculations and life cycle cost analysis of proposed energy efficiency measures. This type of audit identifies all energy conservation measures appropriate for the facility given its operating parameters. 

Much of the information required to fill out the Level 1 inputs in the audit spreadsheet are already inputted into the ASHRAE Building EQ web portal. Therefore, the portal allows the user (for a fee) to create a Standard 211 spreadsheet with many of the Level 1 inputs pre-populated. Even though the spreadsheet also includes Level 2 parameters, Building EQ does not include most of the information required for Level 2 audits. Therefore, this information needs to be manually filled in.

Below is a screenshot of one of the tabs in the Standard 211 Excel spreadsheet:

ASHRAE Standard 211 spreadsheet

ASHRAE Building EQ Label

Once a Building EQ project is approved by ASHRAE personnel, the user is able to print out a Building EQ label that includes the Building EQ logo along with a sliding scale showing the final Building EQ score. The following is an example of the Building EQ label:

Building EQ Label

Building EQ Energy Audit Narrative Report

This Microsoft Word doc report provides a template for an ASHRAE Level 1 Energy Audit that follows the information in Section 6 (Reporting), Annex C (Reporting Forms), and Annex D (Report Outlines) in ASHRAE Standard 211.  The template provides recommended text and boiler plate language to assist the user in preparing a comprehensive report and is automatically populated with information collected and entered into the Building EQ Portal as part of the Building EQ In Operation assessment process. The recommended text can be edited as needed by the user. The audit specific information populated from the Building EQ Portal is shown in filled-in tables in the report. Below is an example of two pages of the report:

ASHRAE Building EQ Narrative Report

Additional Functionality

The Building EQ portal includes additional functionality that helps expand its usefulness:

Integration with Energy Star EUI Data

Depending upon the building type that the user selects, the energy utilization index (EUI) data will either be pulled from ASHRAE Standard 100 database in Building EQ or from an Energy Star service hosted by Architecture 2030 Zero-Tool. Each Energy Star building type has a different set of parameters associated with it so the user will be prompted to input many different types of values. Once the user has inputted all of the required information, pressing the “Get EUI Values” button calls a remote calculation engine that retrieves the appropriate Energy Star EUI value based upon the building type and parameter inputs.

Energy Star

Integration with Energy Star Portfolio Manager

For electricity, natural gas, and other “non-delivered” fuel types, the user can import utility data that already exists in Energy Star’s Portfolio Manager software. All the user needs to do is export the utility data from a PM project to a .csv file. Then, import the .csv file into the appropriate fuel type. The data should be monthly for one year taken within the past 18 months.

Building EQ Utility Data

Integration with BuildingSync

BuildingSync® is a common schema for energy audit data that can be utilized by different software and databases involved in the energy audit process. It allows data to be more easily aggregated, compared, and exchanged between different databases and software tools. This streamlines the energy audit process, improving the value of the data, minimizing duplication of effort for subsequent audits, and facilitating achievement of greater energy efficiency.

Several tools utilize BuildingSync including U.S. Department of Energy’s Building Energy Asset Score. Asset Score is a national standardized tool for assessing the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifies opportunities to invest in energy efficiency upgrades. Data exported from Asset Score (specifically Audit Template) via BuildingSync can be imported into BuildingEQ to populate relevant (but not all) data.

Building EQ Audit Template

Latest Stats

The following are the latest stats as of December 1, 2019:

Number of users: 998

Number of projects: 627

Average area of buildings analyzed: 69,392 sqft

To sign up for a free account, click here.

Mobile App Analytics

Mobile App Analytics

Carmel Software has been developing mobile apps for 10 years. In fact, we were the first to release HVAC-specific mobile apps for Apple iOS, and we currently have 200,000+ apps downloaded by users world-wide.

What makes mobile apps so different from desktop apps is the ability for small companies like ours to reach tens of thousands of users world-wide. Downloading an app from your smartphone is so easy, simple, and inexpensive, that it enables a company like ours to reach out to so many more users compared to our desktop software.

Another amazing thing about mobile apps is the ability to anonymously (and I stress ANONYMOUSLY) track where and how users actually user our apps out in the field. This provides incredibly valuable feedback to us regarding how our apps are actually used in real time. Let’s look at some examples below.


The HVAC PT Chart app is a free iOS app that allows HVAC technicians to quickly perform pressure-temperature lookups for over 100 different types of refrigerants. As a technician is measuring refrigerant pressure or temperature using the pressure-temperature gauge, they can use our app to confirm that the pressure-temperature combinations are correct. Because this app is free, the number of downloads is exponentially greater than even our $0.99 apps. Free really does sell.

Tens of thousands of technicians have downloaded this app, so it really helps create critical mass in terms of tracking how this app is used and how and where technicians work.

The image above captures a snapshot of how many users have used the PT Chart app on a typical Friday by late morning. This dashboard also captures other interesting information including:

  • User locations
  • How long users have used the app (interestingly, most users use this app for less than 1 minute at a time)
  • Whether the app has crashed or not
  • What version of the app users are using
  • What parts and features of the app are users taking advantage of

The image below shows geographical usage since the beginning of the year. As you can see, the app is pretty much used world-wide (the light and dark blue signifies countries using the app). The dark blue areas indicate the highest concentration of users.

Another screen shows the following additional information:

The left-hand side shows what percentage of users are using the app on iPhones versus iPads. The right-hand side shows what percentage of users are using the app on different iOS versions.

Now, there question is: Is any of this data useful and actionable?

We can see that the majority of users use the app on the iPhone, so this tells us to concentrate most of our efforts on improving the iPhone interface. Also, knowing that 100s of HVAC technicians use this app each day tells us that this app is quite useful and a good candidate for some in-app advertising (more on this in a future blog post).

Also, it’s quite useful to know that users use this app for less than 1 minute on average. This allows us to better tailor certain features to accommodate speedy usage of the app. For example, immediately when users open the app, they are able to select a refrigerant type, select the temperature or pressure and observe the corresponding values.

HVAC Equipment Locator

The HVAC Equipment Locator is a mobile application for Apple iOS and Android that lets users track, share, and customize HVAC equipment nameplate and maintenance data:

  • TRACK all equipment location, nameplate, and maintenance data along with photos and spec sheets.
  • SHARE all of this data with other authorized users.
  • CUSTOMIZE the app input screens so users can store and view the exact data about building, equipment, or maintenance events. All equipment data and user access using the Equipment Locator Cloud website.

The following is an interactive Google map that plots a subset (1000s) of all of the approximate locations of HVAC equipment that are currently being tracked and serviced by the HVAC Equipment Locator app:

Not only are we tracking usage of the Locator app, itself, but also tracking the GPS coordinates of all of the equipment that users are storing in the cloud. Once we receive enough equipment information, we can start to see trends of where different makes and models of equipment are geographically located, and also what types and where HVAC maintenance is being performed.