Robert Fassbender's blog

Bonus LEED Points by Simple Math

Posted on: February 2, 2012

LEED Points Logo

Let's cut straight to it:

LEED background information:

LEED energy models have a default requirement that process loads and receptacles are 25% of the baseline building costs. The total cost must be identical in the proposed LEED model.

However, this assumption is based on a standard office building. Thus, if the process loads and receptacles are designed and documented (think spreadsheets, or even a basic description), one can use the actual designed loads. This can yield free leed points, bascially LEED plus a little math gets you 2 valuable LEED points.

....LEED + Math = 2 Points!

Example LEED Case Study:

Let's consider a building that has 31% savings on the proposed building over 90.1-2007 building:

Let's say the Baseline utilities are $100,000 annuallu, of which $25,000 is from misc equipment ($75,000 from everything else). At 31% savings, the Proposed building would be $69,000 annually - of which $25,000 is from misc equipment (since by default, it must equal the baseline)

If instead, someone took the time to do it, and the misc loads were documented and that made the price equal to $15,000 in the baseline model, the revised savings are:

Baseline:

$75,000 (everything else) + $15,000 = $90,000

New Advanced material for eQUEST course

Posted on: November 29, 2011

Energy-models.com is proud to announce a new advanced section in the eQUEST online course. While there are more videos in production, we have just released these popular requests (most of them are related to LEED or to help yield more LEED points):

All lessons contain real-world demos in eQUEST. These are best lessons of the course. Thanks for adding them

Ryan P.  - eQUEST user

  • Global parameter example (change LPD quickly in parametric runs)
  • Model a Dedicated Outdoor air unit
  • Model Energy Recovery Devices
  • Daylighting 
  • Custom curves (example using a LEED fan curve)
  • Optimum Start schedules (for LEED)
  • Import custom items (import curves for LEED)
  • Calculate Loads in eQUEST (Try the free tool)

Confessions of an Energy Modeler

Posted on: November 17, 2011

When it comes to energy, I suppose we are all hypocrites at some point. I mean, look at Al Gore flying around in a private jet. Of course, Al Gore isn't exaclty going to come clean on his hypocrisy. But, that's because he wouldn't make millions on his powerpoints movies.

Anyway we here at energy-models.com are dishing out the dirt. Do you have a confession? Please comment!

This is what got us started: here's an interesting forum post we had from a very zealous but graciously honest energy-modeler:

I have to be 100% honest here. I have a very general lust for sports cars but more specifically for older American Muscle cars. If there were an AA group for Energy Engineers who secretly love fast, questionably efficient cars (or better stated, not at all), then I would have to go and come clean. What can I say? In my professional life I eat and breathe building efficiency every working minute. But when I get into a car all I wanna do is go fast and live that visceral experience of accelration and sweet sweet sound of red lining a V8 as the speedometer climbs.

AI Energy Simulation Advice and Predictions

Posted on: February 19, 2024

AI Predictions in the Energy Simulation Industry

The advent of machine learning in the energy sector has the potential to bring a golden age for building energy modelers. Imagine being able to generate building geometry from a PDF, or calibrate a model as easily as using the solver function in Excel? Imagine running a pre-simulation that approximates results in real time. These things are already possible and being done on small scale. We just need the right people to step up and develop such tools and offer them open-source or at reasonable prices.

But who will develop the AI tools? And why?

Well, several stakeholders are perhaps unknowingly sitting atop AI gold mines. This series of blog posts aims to assist companies in identifying these untapped resources, and provides strategies for individuals to plan for the evolving landscape.

Initially, I reserved these insights for a competitive edge, I've decided to disseminate some thoughts so as to get this party started. (I know factually that many important people will read this)

Understanding Machine Learning's First Wave

Let's delve into how the first round of machine learning works. AI evolves in a stepwise fashion. First, machines require databases filled with existing inputs and outputs. Note that the format is important. The machine maps inputs to outputs and "learns" to identify patterns and inferences in ways that are beyond human comprehension.

This round of AI is characterized by three key factors:

  1. Data existing in a predefined input and output format.
  2. Large volumes of data in a consistent format.
  3. A valuable computation between the input and output, offering creative or predictive potential.

The upcoming impact of AI on Energy Simulation

Posted on: February 14, 2024

Today, we're going to discuss AI and Energy Simulation. AI suggests you will find these topics life-changing, so please read and explore the links! Machine learning isn’t just a buzzword; it has already impacted all of us, whether we realize it or not. Many of us are familiar with digital twins and other innovative tools. A significant question arises: Will AI replace our jobs?

Over the past few months, I've explored AI tools extensively. The team at Energy-models.com can now accomplish tasks that previously required a freelancer. I can complete many tasks faster and more accurately on my own. Here are a few things we've achieved:

  • Developed a new type of video for faster, more concise “how-to” videos
    Here's an example from the latest eQUEST tutorial
  • Cloned my voice, allowing any editor to update my videos
  • Optimized and enhanced old audio (a remastered OpenStudio demo)
  • Generated captions with punctuation and transcripts, reducing costs significantly

A BEM Calibration method on steroids

Posted on: May 6, 2022

Here's a video excerpt of an Energyplus model that has been trained to run in AI and can now run instantly along 4 variables

(note, this uses 4 different variables than those mentioned but the principle is the same)

What does this have to do with Calibration?

I posted a question on LinkedIn some time ago surveying modelers on what some of their biggest time sinks are when calibrating an energy model. Most every response involved getting accurate data. Not one person mentioned something that I thought would be very obvious: simulation time.

Whenever we are calibrating and we receive updated information, we have to iterate the calibration by one variable at a time, and then add the next variable and iterate. Moreover, we almost never obtain perfectly accurate data, and have to make educated guesses in the form of iterations.

PROBLEM: This takes an extreme amount of time 

When iterating a simulation, there is a factorial effect based on the number of unknowns. The time required is exponential. For example, if we have 1 variable with 5 possible values, that’s 5 simulations, but if we have 4 variables with 5 possible values each, that’s 625 possible iterations. Usually, we have more variables with more possible values than that, and that’s usually a best-case calibration. In that example, a 10-minute simulation time, results in over 100 hours of simulation time, not including time editing the model and viewing results. Even if we automate it, that’s a long time and it’s expensive.

What is Energy Model Calibration? Pt 1

Posted on: January 14, 2021

It seems to me that the whole building energy simulation community has become fixated on the topic of "How to calibrate an energy model." Calibration ensures long term growth of the energy modeling industry because a calibrated model commands respect across multiple disciplines. Calibration is nearly always useful, albeit unnecessary, in many circumstances. First things first:

What does it mean to calibrate an energy model?

In laymen's terms, calibrating an energy simulation means "Gathering the actual energy used by the REAL building, after the building has been fully operational for some time - usually about a year - and then adjusting energy model inputs so that the simulation output more closely approximates the energy consumed by the real building"

When can a simulator calibrate a model?

The simulator requires a completed initial model AND the actual building energy consumption after it has been in full operation for some time (ideally, a full year).  At that point, one can compare the model results to actual data determined by the unique condition called "reality". The simulator may subsequently adjust the model's inputs so that the modeled outputs align best with the building's energy consumption in real-world operation.

Do all models require calibration?

Not all models require calibration. Many simulators do not intend to predict the actual results. Instead, many simulators create models to yield a comparative product. For example, if a simulator calculates electric resistance heating compared to a gas boiler, the simulator expresses the energy cost in terms of a percent difference. In most cases, a calibrated model produces a similar percent difference, especially if the model has reasonable assumptions.

How to add a 2nd meter in eQUEST

Posted on: October 21, 2020

From the mailbag:

An energy-models.com trainee asked a great question: Can I determine the individual energy consumption of an individual room or zone?

There are multiple options, from creating custom hourly reports and load tracking, but that gets complicated in a hurry. The answer is not perfect but the main thing to do would be create a submeter. 

If a user wants to truly isolate the energy of an individual room, it depends if the room has a room level fan and room level cooling. Otherwise, you can't necessarily determine the exact impact of a room. You could potentially estimate that if a room at 600 cfm of a 6000 cfm fan, that it would be 10% of the fan energy but really only works in the case of a constant volume fan. The cooling and heating would be tricky because it depends on values specific to the zone, including geometry and other schedules. In the case of shared equipment, the KW/ton changes depending on the block load. A secondary meter is your best bet.

To create a 2nd meter:

  • Go to the Utilities and Economics module on the far right (assuming detailed mode).
  • Find the first meter, usually called, "EM1"
  • Right click to create a new meter
  • It will use the default name, "Electric Meter 2" but you can and should name it an appropriate unique name that will act as a descriptor, such as the zone name if you are going to create many of these.
  • Select "Copy Existing Component" in the dropdown. eQUEST should automatically pick EM1 (because it is currently the only choice!) and click okay.

There are many options that will pop up for you to change, but once you create EM2, the defaults will do the job of creating a 2nd meter that is ready to use.

Self-learning is a necessity. I'm living proof

Posted on: October 21, 2020

I have spent a great deal of my career teaching. Would it surprise you to learn that I am a terrible student? (or not be surprising at all?). I can't seem to pay attention in a classroom. I get too chatty, distracted, or I get too bored. I'm sociable, but learn best by self-study, which I learned in my freshman year of college. I even set a goal of staying awake through an entire lecture hall presentation. I met that goal only once or twice.

It even came to the point where one of my professors singled me out in a lecture hall of about 250 students, I still couldn't stay awake. The professor complained about his dislike of those sleeping in class, and I didn't hear him because I was asleep. Then, he shouted, "Like that guy right there!". I awoke, and he demanded that I come up to the front of the classroom. I was like, "Um, no" because he didn't know my name, and I knew I would say, "I'm sorry, Professor Enchanting" if he let me near the microphone. Too bad he didn't know my name, he would have found it amusing when he saw that I received the 1st or 2nd highest score on the his midterm.

Despite the good grade, it was still a significant flaw, and I'm not proud of it, but that story was pretty funny. I frequently heard it retold, as someone said, "Hey some kid got called out for sleeping in class in a huge lecture hall" and then I would laugh and say, "That was me!" (okay, I was a little proud of that one)

Syndicate content