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Energy Modeling to Secure Larger Loans with Lower Interest Rates

Aug 20, 2024

In the quest for energy efficiency, many property owners and developers are turning to energy modeling to optimize building performance and reduce operating costs. However, the benefits of energy modeling extend beyond just energy savings. Did you know that incorporating energy modeling into your building project can also help you secure lower interest rates on loans? Financial institutions increasingly recognize the value of energy-efficient buildings, and they are offering better loan terms to projects that demonstrate significant energy savings through energy modeling. Here’s how energy modeling can help you save on loan costs, along with a list of specific programs that offer these financial benefits.

The Role of Energy Modeling in Securing Lower Interest Rates

Energy modeling is a powerful tool that allows you to simulate a building's energy performance before it is built or renovated. By using sophisticated software, you can predict how different design choices, materials, and systems will impact energy consumption. This detailed analysis provides a clear picture of potential energy savings, which is not only beneficial for reducing utility bills but also attractive to lenders.

Banks and financial institutions are increasingly offering favorable loan terms—such as lower interest rates, longer repayment periods, or higher loan amounts—to projects that include energy efficiency measures verified by energy modeling. This is because energy-efficient buildings are less risky for lenders; they have lower operating costs and are often more attractive to tenants or buyers, leading to higher occupancy rates and property values.

How Much Can You Save?

The interest rate savings you can secure by incorporating energy modeling into your project can vary, but it's not uncommon to see a reduction of 0.25% to 1% on loan interest rates. This might seem small at first glance, but over the life of a large loan, it can translate into substantial savings.

For example, on a $1 million loan, a 0.5% reduction in interest could save you approximately $50,000 over the loan’s term. These savings can be reinvested into your project, further enhancing its energy efficiency or profitability.

Programs Offering Lower Interest Rates with Energy Modeling

Here are some of the specific programs and financial products that offer lower interest rates or better terms when energy modeling is part of the project:

  1. Green Mortgages and Energy-Efficient Mortgages (EEMs)
    Offered by: Various banks and government-backed programs like Fannie Mae and Freddie Mac.
    Description: These mortgages offer lower interest rates or larger loan amounts for homes that include energy efficiency improvements. Energy modeling is often required to demonstrate the expected savings from these improvements.
  2. Green Loan Programs
    Offered by: Several banks, credit unions, and financial institutions.
    Description: Green loans provide financing specifically for energy-efficient upgrades or new constructions. Lenders may offer more favorable interest rates based on the energy savings projected by energy modeling.
  3. Property Assessed Clean Energy (PACE) Financing
    Offered by: Local governments in partnership with private lenders.
    Description: PACE financing allows property owners to finance energy efficiency projects through property taxes, often at lower interest rates. Energy modeling is crucial for calculating the expected savings and determining the loan terms.
  4. FHA PowerSaver Loans
    Offered by: The Federal Housing Administration (FHA) through approved lenders.
    Description: FHA PowerSaver loans provide lower interest rates for energy-efficient home improvements, with energy modeling used to validate the efficiency gains.
  5. Commercial Real Estate Green Financing
    Offered by: Commercial banks and lenders.
    Description: For commercial properties, green financing options offer reduced interest rates for projects that incorporate energy efficiency measures validated through energy modeling.
  6. Fannie Mae Green Financing
    Offered by: Fannie Mae.
    Description: Fannie Mae’s Green Rewards financing program provides lower interest rates or higher loan proceeds for multifamily properties that plan to implement energy efficiency improvements, with energy modeling used to demonstrate the expected savings.

Conclusion

Incorporating energy modeling into your building project not only helps you design a more energy-efficient structure but can also lead to significant financial benefits, including lower interest rates on loans. By reducing the perceived risk to lenders and demonstrating the long-term cost savings of energy-efficient buildings, energy modeling can make your project more attractive to financial institutions, ultimately saving you money over the life of your loan.

If you’re planning a new construction or a major renovation, it’s worth exploring the financial incentives available through energy modeling. The savings on interest rates, combined with the operational savings from reduced energy consumption, can significantly improve your project’s financial outlook.

14 Common Programs that Require Energy Models

Aug 20, 2024
  1. LEED (Leadership in Energy and Environmental Design)
    Administered by: U.S. Green Building Council (USGBC)
    Purpose: LEED certification is one of the most widely recognized green building certification systems worldwide. Energy modeling is required to demonstrate that a building meets energy performance criteria, particularly for the Energy and Atmosphere (EA) credit category.
  2. 179D Energy Efficient Commercial Buildings Deduction
    Administered by: U.S. Internal Revenue Service (IRS)
    Purpose: This tax deduction encourages the design and construction of energy-efficient buildings in the U.S. To qualify, energy models must show that a building's energy systems achieve certain efficiency thresholds relative to a baseline.
  3. ASHRAE Standard 90.1 Performance Rating Method
    Administered by: American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)
    Purpose: ASHRAE 90.1 is a widely adopted standard for energy efficiency in buildings. The Performance Rating Method, which involves energy modeling, is used to demonstrate compliance with energy code requirements and to achieve additional LEED credits.
    State and Municipal Compliance: ASHRAE 90.1 is often used as the basis for state and municipal energy codes in the U.S., making energy modeling essential for compliance with local regulations.
  4. ENERGY STAR for Buildings
    Administered by: U.S. Environmental Protection Agency (EPA)
    Purpose: ENERGY STAR certification for buildings typically involves benchmarking energy performance. For new construction, energy modeling may be required to project energy performance and achieve certification.
  5. Title 24 Compliance (California Energy Code)
    Administered by: California Energy Commission (CEC)
    Purpose: Title 24 requires energy modeling to demonstrate that new buildings or major renovations comply with California's energy efficiency standards. This is one of the most stringent energy codes in the U.S.
  6. WELL Building Standard
    Administered by: International WELL Building Institute (IWBI)
    Purpose: The WELL Building Standard focuses on human health and well-being in the built environment. Energy modeling is used to assess indoor environmental quality and ensure that buildings meet energy performance criteria that impact occupant health.
  7. BREEAM (Building Research Establishment Environmental Assessment Method)
    Administered by: BRE (Building Research Establishment)
    Purpose: BREEAM is a sustainability assessment method for master planning projects, infrastructure, and buildings. Energy modeling is used to assess energy use and efficiency as part of the overall sustainability performance.
  8. Passive House Certification
    Administered by: Passive House Institute (PHI)
    Purpose: Passive House certification requires buildings to meet rigorous energy efficiency standards. Energy modeling, often using the Passive House Planning Package (PHPP), is essential to demonstrate that a building meets the extremely low energy consumption targets.
  9. Green Globes
    Administered by: Green Building Initiative (GBI)
    Purpose: Green Globes is an alternative to LEED, focusing on sustainable building practices. Energy modeling is used to assess energy performance, contributing to the overall score needed for certification.
  10. Living Building Challenge
    Administered by: International Living Future Institute (ILFI)
    Purpose: The Living Building Challenge is a holistic building certification program that focuses on sustainability and regenerative design. Energy modeling is used to ensure that buildings generate more energy than they consume, achieving net-positive energy.
  11. International Energy Conservation Code (IECC) Compliance
    Administered by: International Code Council (ICC)
    Purpose: The IECC sets minimum energy efficiency standards for buildings. Energy modeling is one of the methods to demonstrate that a building meets or exceeds these standards.
  12. National Green Building Standard (NGBS)
    Administered by: Home Innovation Research Labs
    Purpose: The NGBS is a green building certification program primarily for residential buildings. Energy modeling may be required to demonstrate compliance with the energy efficiency requirements of the standard.
  13. Zero Net Energy (ZNE) Certifications
    Various Administrators: Multiple organizations, including the New Buildings Institute (NBI) and the International Living Future Institute (ILFI)
    Purpose: ZNE certifications are awarded to buildings that generate as much energy as they consume over the course of a year. Energy modeling is essential for predicting and verifying energy performance in ZNE projects.
  14. High-Performance Building Certification (HPBC)
    Administered by: Various organizations, often related to specific countries or regions
    Purpose: High-performance buildings are those that exceed standard energy efficiency criteria. Energy modeling is used to demonstrate that a building meets the specific high-performance criteria, often including energy, water, and indoor environmental quality.
Join thousands of others and learn how to perform the required energy model in our On-Demand Training Courses

5 Times When an Energy Audit Needs an Energy Model

Aug 19, 2024

Energy audits are essential for identifying inefficiencies in buildings and recommending ways to reduce energy consumption and costs. However, certain situations require a deeper analysis, where a standard energy audit may not be enough. In these cases, energy modeling becomes a crucial tool for providing accurate, data-driven insights. Here are five scenarios when an energy audit truly needs the support of an energy model.

1. When Upgrading HVAC Systems

Heating, ventilation, and air conditioning (HVAC) systems are among the largest energy consumers in most buildings. When an energy audit identifies the need for an HVAC upgrade, it’s important to assess how different system options will perform in the specific building environment. This is where energy modeling comes into play.

Energy modeling allows auditors to simulate various HVAC systems and configurations, comparing their energy consumption, costs, and potential savings under real-world conditions. This level of analysis ensures that the recommended HVAC system not only meets the building’s current needs but also optimizes energy efficiency, reducing long-term operational costs.

2. When Pursuing Green Building Certifications

Achieving certifications like LEED (Leadership in Energy and Environmental Design), ENERGY STAR, or other green building standards often requires a detailed analysis of a building’s energy performance. A standard energy audit may highlight areas for improvement, but it may not provide the detailed performance projections necessary for certification.

Energy modeling provides the in-depth analysis needed to meet the stringent requirements of these certifications. It allows auditors to demonstrate how specific upgrades or design changes will impact the building’s energy efficiency, ensuring that all necessary criteria are met for certification. This not only helps in securing the certification but also adds value to the property.

3. When Analyzing Complex Building Designs

Some buildings have unique or complex designs that make it difficult to predict energy performance using traditional audit methods. For example, buildings with irregular shapes, large atriums, or extensive glass facades can have unpredictable heating, cooling, and lighting needs.

In these cases, energy modeling is essential. By creating a virtual model of the building, auditors can simulate how different elements of the design will affect energy consumption. This allows for a more accurate assessment of potential energy-saving measures and ensures that the recommendations are tailored to the building’s specific design challenges.

4. When Planning Major Renovations

Major renovations provide an opportunity to significantly improve a building’s energy performance, but they also present challenges in predicting the outcomes of various interventions. Whether it’s replacing windows, upgrading insulation, or overhauling the lighting system, the potential impact on energy use can be complex and multifaceted.

Energy modeling helps auditors navigate these complexities by simulating different renovation scenarios. It can show how changes will interact with existing building systems and predict the overall impact on energy consumption. This allows building owners to make informed decisions about which renovations will provide the best return on investment in terms of energy savings.

5. When Evaluating Renewable Energy Integration

As the push for renewable energy grows, more building owners are considering the integration of solar panels, wind turbines, or other renewable energy sources. However, determining the feasibility and impact of these systems requires more than a standard energy audit.

Energy modeling can simulate the integration of renewable energy systems into the building’s existing infrastructure. It can predict how much energy these systems will generate, how they will interact with the building’s energy needs, and what the potential cost savings might be. This detailed analysis is crucial for ensuring that the renewable energy investments are both practical and economically viable.

Conclusion

While standard energy audits provide valuable insights into a building’s energy performance, there are situations where a more detailed analysis is required. Energy modeling offers the depth of analysis needed to make informed decisions in complex scenarios, such as HVAC upgrades, green building certifications, complex designs, major renovations, and renewable energy integration.

By incorporating energy modeling into these audits, energy auditors can provide their clients with more accurate, data-driven recommendations that lead to greater energy efficiency and cost savings. In today’s energy-conscious world, understanding when to move beyond a standard audit and into the realm of energy modeling can make all the difference in achieving optimal building performance.


Understanding the 179D Tax Deduction and the Importance of Energy Modeling

Aug 16, 2024

As the push for energy efficiency continues to shape the future of building design and construction, incentives like the 179D tax deduction have become increasingly important for building owners, designers, and engineers. But what exactly is the 179D deduction, and why is energy modeling so critical in this context? Let’s break it down.

What is the 179D Tax Deduction?

The 179D tax deduction, also known as the Energy Efficient Commercial Buildings Deduction, is a federal incentive designed to encourage energy-efficient construction and retrofits in commercial buildings. Initially introduced as part of the Energy Policy Act of 2005, this deduction allows building owners or designers to deduct a portion of the costs associated with energy-efficient improvements from their taxable income.

For buildings placed in service in 2024, the deduction can be as high as $5.00 per square foot for qualifying energy-efficient measures. This can translate into significant tax savings, especially for large commercial projects. To qualify, the building must demonstrate substantial reductions in energy use intensity (EUI) compared to a baseline established by the ASHRAE 90.1 standard.

The Role of Energy Modeling in 179D

Energy modeling is at the heart of the 179D deduction process. It’s a sophisticated simulation process that allows designers and engineers to predict a building’s energy consumption based on its design, systems, and operational characteristics. Here’s why energy modeling is essential:

Baseline Comparison

To qualify for the 179D deduction, a building’s energy performance must be compared to a baseline set by the ASHRAE 90.1 standard, which provides minimum energy efficiency requirements. Energy modeling is used to create this baseline and then to simulate the proposed design’s performance. To learn the fastest way to make the baseline building and automate much of the process. check out eQUEST Baseline System course

Quantifying Energy Savings

The deduction is based on the percentage of energy savings achieved. Energy modeling provides the quantitative data needed to demonstrate how much more efficient the building is compared to the ASHRAE baseline. Achieving a 50% reduction in energy use intensity, for example, could qualify the building for the maximum deduction.

Design Optimization

Energy modeling allows for iterative testing of different design options. By tweaking variables such as insulation levels, HVAC systems, lighting designs, and more, designers can identify the most cost-effective measures to achieve energy savings and qualify for the deduction.

Compliance and Certification

To claim the deduction, the building’s energy performance must be certified by a licensed engineer or architect. This certification is based on the results of the energy model, which must demonstrate that the building meets or exceeds the required energy savings thresholds.

Why You Need to Know Energy Modeling

Whether you’re a building owner, architect, engineer, or energy consultant, understanding energy modeling is crucial if you want to take advantage of the 179D tax deduction. Here’s why:

  • The Only Way to Maximize Tax Deduction: Proper energy modeling can significantly increase your potential tax deduction by accurately capturing all qualifying energy-efficient measures.
  • Other reasons: There are numerous other reasons for modeling. However, if you are reading this, the first one is the only one that matters.

What software is used for 179D

  • Various Software Packages: Check if a software Package is ASHRAE Standard 140 compliant
  • Free Software: eQUEST is the most common software used to achieve the tax credit. The modeling process is nearly identical to the LEED Modeling process. Learn how to model for 90.1 Compliance (for 179D in this case) with our eQUEST 301/310 course.

How does 179D differ from year to year?

Year ASHRAE Standard Maximum Deduction per Square Foot
2014 ASHRAE 90.1-2007 $1.80
2015 ASHRAE 90.1-2007 $1.80
2016 ASHRAE 90.1-2007 $1.80
2017 ASHRAE 90.1-2007 $1.80
2018 ASHRAE 90.1-2007 $1.80
2019 ASHRAE 90.1-2007 $1.80
2020 ASHRAE 90.1-2007 $1.80
2021 ASHRAE 90.1-2016 $1.80
2022 ASHRAE 90.1-2016 $1.80
2023 ASHRAE 90.1-2016 $5.00
2024 ASHRAE 90.1-2019 $5.00

Conclusion

The 179D tax deduction is a powerful tool for promoting energy efficiency in commercial buildings, and energy modeling is the key to unlocking its potential. By accurately predicting a building’s energy performance and comparing it to stringent standards, energy modeling helps designers and owners achieve significant tax savings while contributing to a more sustainable future. If you’re involved in the design, construction, or retrofit of commercial buildings, understanding energy modeling is not just an option—it’s a necessity.

AI Energy Simulation Advice and Predictions

Feb 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.

A Simple Example

Consider the trend on social media where users posted a current picture of themselves alongside an image from 10 years prior. This phenomenon met the first two conditions perfectly, offering a vast dataset in a standardized file format of photographs, with clearly defined inputs and outputs.

The result was an AI that could predict what you will look like in a decade. Most people think that the value is for fun little apps that predict what you look like. The real value goes far beyond personal curiosity. Consider the potential for unsolved crimes, missing persons cases, and military uses. Plus, when combined with other data, such as facial recognition technology, this capability becomes even more powerful. For instance, posting an old photo on Facebook might automatically tag and connect you to a childhood friend, enhancing social interactions and potentially influencing the platform's stock value.

A Forgotten Goldmine of Energy Modeling 

So why do I mention stock price? What does that have to do with energy modeling?

Don't ever forget that our models have a significant influence on infrastructure. They determine investments. Our work drives building design decisions,  which drives data, ultimately accumulating into hundreds of billions of dollars towards various industry trends (Ground source VRF anyone?)

Therefore machine learning provides two advantages 1) make it easier to save energy 2) identify products that save the most energy in various applications (data driven proof that a product worked!)

Who will tap into this goldmine? If you locate large amounts of energy modeling data, the data formatting is often inconsistent. You can’t just dump randomly organized data into a machine. That’s not how AI works.

Data needs to be parsed, organized, and THEN mapped.

If you have pre-organized data, you have a massive headstart. The question is, Where is the machine-ready data?

Finding Large, Consistent Data Sets

Entities with access to vast amounts of consistently formatted data can skip the parsing and pre-processing. They can dump their data directly into a machine.

Who has large amounts of simplified and organized data?

In the context of energy simulation, several organizations come to mind:

  • U.S. Green Building Council (USGBC)
  • ENERGY STAR Portfolio Manager
  • Utility company rebate programs
  • Various government initiatives

Example, Turning the G in USGBC from Green to Goldmine? 

I've simulated hundreds of LEED buildings, so I'm going to use the USGBC as an example (this is purely speculative, as it's the best example I have personal experience with).

In order to earn various LEED certifications, modelers are required to download and fill out a detailed spreadsheet with various inputs and outputs from their energy modeling files. This spreadsheet alone, when fed into a neural network, could yield predictions beyond our current understanding. Alongside other submitted documents, USGBC could amass a treasure trove of data invaluable to corporations (e.g., "To obtain LEED Platinum, and cut your energy bills by 40%, use one of these three light bulb manufacturers").

How could this change how you do your job?

Beyond benefits to corporations, machine learning, in the USGBC data alone, could pioneer an accurate "pre-simulation" engine. For instance, with as few as ten inputs, a well-trained AI could predict building energy usage with better accuracy than many full energy simulation models. Additionally, the software might recommend commonly used energy strategies that have resulted in LEED certification and highlight the most cost-effective solutions. 

Test and improve a simple free tool

In line with this, we took some limited information, combined it with a custom GPT and developed "Optimize Architect," a free chatbot designed to guide you in identifying the ten most effective energy conservation measures. It begins with basic queries and progressively offers more creative suggestions. 

Check it out Optimize Architect here

If you're keen to understand energy modeling in ways AI currently cannot, our ecourses offer a comprehensive learning experience that focus on "why" and "how" and other items where you can beat the bots.

Stay tuned

I have been experimenting with various AI tools and plan to share insights on safeguarding your career against the AI revolution in upcoming blog posts. This will ensure you remain invaluable during the golden age of energy simulations, allowing you to focus on the unique contributions only humans can provide.

The upcoming impact of AI on Energy Simulation

Feb 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

AI has surprised us in many ways. For instance, from a few transcripts, it managed to create eQUEST and OpenStudio code snippets for ASHRAE 90.1 items. AI has undoubtedly made certain tasks easier and faster. It has also replaced several jobs, removing pay from our transcriptionist, content organizer, graphic designer, and video editor. Even our web developer went on vacation, and I was able to do his job in ways I never imagined possible. AI even replaced me as an instructor in a random video (we'll see if anyone notices and if it engages users more effectively).

AI rewrote this entire post. No, I'm joking. It proofread, and I had to undo some of its work. I believe I'm still a more engaging writer.

Everyone is crazy about ChatGPT. 

We connected our database to some GPT’s and you can test them

The Big Picture Simulation

Back in 2019, which feels like the Stone Age for artificial intelligence, a team used AI to simulate the universe. The simulation began with a certain amount of matter and energy, then projected what would happen over time. Questions like, "Do stars form?" "How many and how big are they?" and "Does the simulation collapse into a black hole?" were explored. Given the complexity of the universe, it's understandable that initial simulations required 300 computation hours.

The team developed faster methods, reducing calculation time to two minutes, though this speed increase came at the expense of accuracy. The fast models had a relative error of 9.3% compared to the initial model. Scientists developed an AI method, called the Deep Density Displacement Model (D3M), focusing on one parameter (the amount of ordinary matter in the universe) and leveraging thousands of models for machine learning. This effort paid off, producing an AI simulation that computed within 30 milliseconds and improved the relative error to 2.8%—making it three times more accurate and 3,000 times faster than the other rapid simulation method.

Mysteriously, the D3M model worked for parameters it hadn't been trained on. When the team altered the starting conditions, such as the amount of dark matter, the model still simulated accurately.

For those familiar with machine learning, this outcome is puzzling. None of the scientists understood how the AI model worked. I've wondered if any of them considered, "Maybe it's because dark matter doesn't exist, and the AI figured that out." Fun fact: I named my business "Cosmo Logic" because I believe simulations will solve the cosmologic constant problem without needing the "boogeyman" of dark matter and dark energy.

It remains unknown whether the D3M model discovered an algorithm that functions without dark matter or if it transforms Planck's constant into something entirely unexpected. Machine learning doesn't "show its work," after all.

Why Is This Relevant to Building Energy Simulation?

Physicists have led the way in simulations and AI, yet robots haven't replaced their jobs. In fact, there are more simulation scientists now, conducting far more advanced simulations. The roles lost have been in data collection, data entry, and post-processing.

Simulations serve as a tool to explore "What happens if?" The value of a simulation scientist lies in answering "Why did it happen?" and "What can we do about it?"

As building simulation specialists, this is crucial for us. We're in a field that will grow with AI. Those who merely input data into models may find their roles evolving, but those who excel will be:

  • Experts in understanding complex methods
  • Inquisitive individuals who ask the right questions and apply AI effectively
  • Professionals who know how to translate data into actionable insights

So, if your current role involves basic model input, consider this a call to deepen your knowledge and secure your position in an exciting future.

Thanks for reading,

Bob (Or was this entire thing written by AI?)

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