Join us for a new EnergyPlus Training series and get exclusive first access to the software shown in the video, where you can finally learn EnergyPlus visually and interactively!
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Dec 9, 2024
Join Us for a One-of-a-Kind Software Demo!
Discover a revolutionary tool while expanding your understanding of EnergyPlus.
Why Attend?
The power of EnergyPlus
The ease of eQUEST
The speed of SketchUp
What You’ll Learn:
From building geometry to HVAC systems, see how EnergyPlus is now more accessible than ever with EP3.
Event Details
- Date: December 17th
- Time: 12:00 - 1:00 PM (Central - Chicago)
- Software Demonstrated: EP3
- Presenter: Karen Walkerman
- Cost: Free
Meet the Presenter
Karen Walkerman is a seasoned Energy Modeling Professional with 18 years of experience. From small passive-solar homes to complex, large-scale projects, Karen has modeled it all. She developed EP3 as an in-house simulation tool and has used it to model hundreds of buildings.
Don’t miss this opportunity to enhance your skills and discover how EP3 can transform your workflow.
Nov 15, 2024
About two months ago, I created an AI energy model to determine how much time and effort it required. Much to my surprise, it was faster and easier than I expected, with some interesting challenges, and I will outline the process here. Note that this process works with any software. I chose eQUEST because it required me to gather data from varying text-based files. After all, most AI requires collecting and organizing data that isn't in Excel format.
The Process
Here's a quick rundown of how it worked:
- Start with a specific model I began with a file I had of an architect's office. I named it CAD Captivity Center
- Generate the data set I ran 50 simulations using the same three variables. For each simulation, I set each variable as follows
- LPD (Lighting Power Density): Randomly selected between 0.5 and 1.5 W/Sq ft.
- Shading Coefficient: Randomly chosen between 0.3 and 0.8.
- Insulation (R-Value): Random values between R-5 and R-50.
- Put the Data into Spreadsheet format Collected the inputs and organized them into a spreadsheet. Used python to export the load results from the simulation files into put them into a spreadsheet to train the machine.
- Process the Data Divide the data into two groups, 40 models for training the AI, and 10 models for testing it. (the 80/20 rule)
- Generate the AI Model I used open-source machine learning software, Tensorflow.js, and a little Python code; I fed the data into Tensorflow, and it generated a working AI model. The training process took about one minute!
- Testing and Verification I ran manual tests of the model by inputting LPD, Shading Coefficient, and R-Value. I compared the AI model load prediction to real eQUEST results. The results were mostly within 1-8% of accuracy, with a few outliers—a win for a proof of concept with limited data.
- Exporting the AI Model The AI model was then ready for export so anyone could run it.
Instant Simulations
What exactly is the AI Model I created? It is a tiny calculator that can instantly predict the load for the "CAD Captivity Center" for infinite combinations of LPD, Shading Coefficient, and R-value. Effectively a little machine brain that computes the load for the Captivity Center, using 3 variables (and assuming EVERYTHING else stays the same). It is very good at that, and it cannot do anything else.
Practical Applications
This approach offers plenty of practical uses. For example, we could set the variables to a cost function (e.g., Insulation Cost = Install Fee + Factor * R-Value), and we can solve for the lowest-cost options to achieve a target load—without running 5,000 iterations.
Rapid Expandability
One of the great things about this method is its expandability. There is no strict limit on input and output variables—only the availability of a broad dataset. The model works for any combination of inputs and outputs. For example, we could train it to predict energy consumption using any input variables. One simply needs to obtain enough data and define reasonable variables to make a conclusive output, and understand its limits. For example, we could make a model that predicts the energy consumption of a building based on LPD and plug loads. However, it would be limited to one specific building, assuming no other variables.
Of course, you can expand variables as long as you have enough data! For example, to expand the load calculator of the CAD Captivity center, we could add a new variable, such as glass R-value, to the initial models. This requires re-running initial simulations with the four variables. We might need to run 80 iterations (or more) to maintain the same accuracy as we obtained with three variables in 50 simulations.
Challenges
1. Data Collection The biggest hurdle is building a dataset to train the model, involving two key steps:
- Obtaining Raw Data
- Converting Data to CSV Format
In my example, the load outputs were found in eQUEST simulation files. To extract these values, I wrote a Python script to pull outputs from 50 SIM files. This was tricky for a few of them. Another option is to plug the data into CSV manually.
2. Model Expansion We can expand the model. The expanded model requires data. If we wanted to add complex variables such as climate zone, we'd need to simulate all iterations for each climate zone—an 8x increase if the input is Climate Zone 1-8 (not including a, b, c). This would push the iteration requirement to a minimum of 400. In reality, location has so many variables, that it might require thousands of models. The point is that it is expandable.
Below is a screenshot of TensorFlow training from the data. I set it to run 100 "Epochs" and you can choose more or less. The machine learns more and more as Epochs progress; here you can see that most epochs took 3 milliseconds, so this is not a significant limitation.
Limitations
The model is limited to the variables on which it was trained and isn't designed to replace traditional modeling software. Instead, it acts as a powerful, complementary tool, enabling one to explore thousands of iterations of variables.
Sharing the AI Model
Once created, the AI model is a compact file, easily shareable—a small but powerful calculator. I considered embedding it here, but why would I want to answer a bunch of emails about it?
Final Takeaway
The key takeaway is that AI modeling is readily available and it is highly specialized. With sufficient training data, it can handle certain tasks exceptionally well—and extremely fast. I see a future where robust AI simulations emerge. At first, they will be used in conjunction with models, and eventually, AI engines can become energy modeling software. We simply need a large enough dataset.
From what I've seen, an AI model could produce "above average" results using 20 inputs. This is because I have seen many models that are GIGO (garbage in garbage out). An AI engine would ensure users only input the relevant items. It could easily replace a weak modeler. On the other hand, AI is far away from competing with expert models.
Interested in the Steps? The step-by-step procedure? If you're curious about the exact steps I took to build this AI model, feel free to connect with me on LinkedIn below.
Nov 6, 2024
A Gold mine for Energy Efficient buildings in the USA
What Is the 179D Deduction?
The 179D deduction, officially known as the Energy-Efficient Commercial Buildings Deduction, is a federal incentive designed to encourage energy savings in commercial and government buildings. Qualifying building upgrades, whether in HVAC systems, lighting, or overall building envelope improvements, can mean significant tax savings. For organizations that want to demonstrate sustainability while directly benefiting the bottom line, 179D is a valuable tool.
Why CFOs Should Care
1. Substantial Tax Deductions
Qualifying for the 179D deduction can mean up to $1.88 per square foot in deductions. For larger buildings, these savings can scale quickly, directly reducing taxable income. Imagine the impact of these savings across a portfolio of properties—it’s a straightforward way to turn energy-efficient initiatives into real financial gains.
2. Cash Flow Optimization
By leveraging 179D, businesses can free up capital that would otherwise be spent on taxes. This can provide a boost in cash flow, allowing your organization to reinvest in other areas. Whether it's funding additional projects or improving operational flexibility, this deduction offers a tangible way to keep more cash on hand.
3. ESG Compliance and Enhanced Corporate Reputation
With investors and stakeholders increasingly prioritizing environmental, social, and governance (ESG) criteria, participating in 179D projects can support your organization's ESG goals. Achieving these benchmarks not only improves the company’s image but can also be advantageous in securing funding and enhancing stakeholder trust.
How to Determine Eligibility
For many, navigating the requirements of the 179D deduction may seem daunting. Eligibility involves meeting specific energy-saving benchmarks verified by a licensed professional. Key areas often include:
- Lighting Systems: Efficient lighting reduces energy consumption, contributing to significant long-term savings.
- HVAC Systems: Heating and cooling systems are a major energy expense. Upgrades in this area can quickly add up in deductions and energy savings.
- Building Envelope: Improvements in insulation, windows, and walls can make a building more efficient, minimizing both heating and cooling costs.
How Designers Can Claim the 179D Deduction
For designers and architects who work on energy-efficient upgrades for government-owned buildings, the 179D deduction presents a unique financial opportunity. Since government entities typically don't benefit from tax deductions, the IRS allows designers responsible for the energy-efficient elements of these buildings to claim the deduction instead. This includes architects, engineers, and other design professionals involved in key improvements.
1. Eligibility Criteria for Designers
To qualify, designers must be directly involved in creating and implementing energy-efficient features, such as lighting, HVAC, and building envelope components, that meet 179D’s efficiency standards. The government agency owning the building must allocate the deduction to the designer, and only one designer per project can claim the deduction. The energy savings achieved must be certified by a licensed professional to ensure compliance with IRS guidelines.
2. Financial Impact for Design Professionals
The 179D deduction allows designers to receive up to $1.88 per square foot on qualifying projects, which can quickly add up, especially on larger government facilities. This benefit not only rewards sustainable design work but also helps firms offset project costs and improve overall profitability.
3. Advantages Beyond the Financials
Beyond the deduction itself, claiming 179D can set design firms apart by demonstrating a commitment to sustainable practices. Government contracts often look for firms with a track record in energy-efficient design, making 179D a potential differentiator in competitive bidding processes.
Getting Started: If you're a designer looking to leverage the 179D deduction, it’s essential to understand the technical requirements and work closely with energy modeling experts who can guide you through the certification and allocation process. Our team specializes in helping designers maximize their deductions by ensuring accurate modeling and compliance, taking the guesswork out of the process.
Getting Started: What to Look for in a Partner
Partnering with the right team can simplify the process. Look for experts who can provide energy modeling, technical expertise, and end-to-end management of the certification process. We work closely with clients from various sectors, ensuring accurate modeling and compliance with 179D requirements.
Our team brings over 150 years of experience in energy-efficient tax incentives, successfully guiding CFOs and finance teams through every step, from initial consultation to final certification. With a hands-on approach, we help ensure that your building upgrades not only qualify for the deduction but also maximize the financial benefits.
Conclusion: Transform Your Tax Strategy with 179D
The 179D deduction is more than a tax incentive; it’s a strategic asset. As energy efficiency continues to grow in importance, now is the time to explore how your organization can benefit. By aligning with an experienced partner, you can unlock significant savings, boost cash flow, and strengthen your company’s commitment to sustainability.
Ready to explore how 179D can transform your tax strategy? Contact Us Today for a free consultation, and let's make energy efficiency work for your bottom line.
Oct 24, 2024
Rebates and tax deductions deductions are two pillars of energy modeling.
Everyone should know about the 179D tax deduction. It's basically like a LEED model without the LEED review, and you get paid if the results are promising.
With the help of eSai LLC experts, we will teach you how to receive as much as $5/square foot in federal tax deductions for your energy efficiency projects from 2006 to 2024. The session includes a Q & A discussion from an expert
Bob Fassbender and guest expert Nandini Mouli, PhD will work together to bring you up to speed. You can choose one of the following time slots:
- October 28, 2-3 p.m. EST
- November 4, 3-4 p.m. EST
- Option to request an online copy
The webinar will answer the basic questions like:
- What is the E179D tax deduction and who can claim it?
- How did the E179D deduction increase to a maximum of $5.00 per square foot?
- When can a designer or architect claim the deduction?
- What new building categories allow for the designer deduction?
Like all tax incentives, there is fine print that you must know. We’ll cover the nuances to ensure you can maximize your deduction. Items such as:
- Prevailing wage and apprenticeship requirements
- Data requirements to review and qualify under the 2024 guidelines
- Qualifying ASHRAE standard updates
- Integrate alternative energy tax credits with EPAct 179D tax deductions
This webinar will show you how to improve your building's energy efficiency and claim the federal benefits you're entitled to.
Don't miss this opportunity to learn how to leverage 179D and save on energy costs! Build better buildings and lower carbon emissions without breaking the bank
Looking forward to seeing you there.
Just fill out the brief Google form, and you're in
Oct 8, 2024
Top 5 Current Trends in Energy Modeling (With Case Studies)
Energy modeling is undergoing significant changes, driven by technological innovations, evolving codes, and a push for greener buildings. As we adapt, understanding these key trends can help us refine our practices and stay competitive. Below, we explore the top five current trends in energy modeling, each backed by a real-world case study for context.
1. Artificial Intelligence and Machine Learning Integration
Trend Overview: AI and machine learning are now key players in energy modeling, offering faster and more accurate energy consumption predictions. These tools analyze vast datasets and automate complex simulations, reducing manual iterations and accelerating the modeling process. The integration of AI allows for more predictive analytics, especially useful in large projects or urban planning.
Case Study 1: AI Integration/Calibration with EnergyPlus and TensorFlow:
A leading architecture firm in New York integrated EnergyPlus with TensorFlow, a popular machine learning framework, to predict energy consumption for a mixed-use high-rise development. By coupling TensorFlow’s AI capabilities with EnergyPlus's detailed simulation engine, the team could predict energy loads based on historical weather data, material properties, and occupancy patterns. The model delivered results within a 3% margin of error, significantly cutting down the time required for manual iterations. This hybrid approach reduced labor by 40% and allowed the project to be completed six weeks ahead of schedule. Moreover, this AI-augmented EnergyPlus model optimized the HVAC system design, delivering more precise control settings, which resulted in considerable energy savings during operation.
2. Real-Time Energy Modeling
Trend Overview: Energy modeling is shifting from a static design-phase tool to a dynamic process, with real-time energy models that adjust based on live data from smart meters and IoT sensors. This allows for ongoing building performance optimization and instant identification of energy inefficiencies.
Case Study 2: Real-Time Energy Modeling with EnergyPlus and Microsoft Azure Digital Twins:
A hospital in Seattle developed a real-time digital twin by integrating EnergyPlus with Microsoft Azure Digital Twins, a powerful IoT platform designed for dynamic system monitoring. This integration allowed the hospital’s building management system to send real-time data from thousands of IoT sensors, such as temperature, lighting, and occupancy, directly into EnergyPlus for continuous energy simulation.
The EnergyPlus-driven digital twin model enabled dynamic adjustments to the HVAC and lighting systems based on real-time occupancy and environmental conditions. During a critical period of high patient load, the system maintained optimal indoor air quality and comfort while reducing energy use by 15%. Additionally, the digital twin identified inefficiencies in energy consumption patterns, leading to targeted system improvements that resulted in an overall 10% reduction in the hospital’s annual energy costs.
This project showcased how the combination of EnergyPlus with Azure Digital Twins created a powerful, real-time energy modeling solution that continuously optimizes building performance and minimizes energy waste.
3. Whole-Building Life Cycle Assessments
Trend Overview: Traditional energy models typically focus on operational energy use. But life cycle assessments (LCAs) take it a step further, evaluating the energy consumed during the entire life of the building, from the manufacturing of materials to demolition. Incorporating LCAs into energy models provides a more holistic view of a building’s environmental impact.
Case Study 3: Revit combined with Cove.tool and Tally combined to calculate LCA with Embodied Energy
A university in Denmark undertook a new campus project using Cove.tool alongside the Tally plugin for a comprehensive life cycle assessment approach. The energy model in Cove.tool focused on operational energy use, while Tally calculated the embodied energy of construction materials throughout the building’s lifecycle. This included not only the energy used in material production but also projected energy for demolition and recycling at the end of the building’s life. By leveraging Tally to select materials with lower embodied energy and Cove.tool to model optimized energy performance strategies, the project is expected to reduce total energy consumption by 25% compared to traditional methods. This integrated approach has set a new standard for sustainable campus developments across Europe.
4. Integration of Renewables and Energy Storage
Trend Overview: With renewable energy sources like solar and wind becoming mainstream, energy models now need to account for their integration along with energy storage solutions. Modeling how a building will interact with on-site generation and storage systems is key to optimizing performance and ensuring buildings remain net-zero or net-positive in energy consumption.
Case Study 4 Net-Zero Energy Corporate Office Park using eQUEST and HOMER Pro:
A corporate office park in California pursued a net-zero energy goal by integrating on-site solar panels and battery storage. The energy modeling team used eQUEST to simulate the building’s overall energy consumption, HVAC loads, and lighting systems. For modeling the renewable energy generation and battery storage system, they used HOMER Pro, a software specialized in optimizing distributed energy resources and microgrids.
By combining eQUEST for the building's energy consumption and system performance with HOMER Pro for renewable energy generation and battery storage, the team was able to simulate the interaction between solar power, battery storage, and grid dependence. The model helped identify the optimal battery size and storage capacity needed to maximize self-consumption and minimize reliance on the grid, especially during peak hours.
After one year of operation, the building achieved 95% grid independence during peak demand, with excess energy from solar panels being stored in the battery and sold back to the grid. This integration of eQUEST with HOMER Pro enabled the office park to reach its net-zero energy goal efficiently, with future plans to replicate the strategy in additional buildings across the campus.
5. Compliance with Stricter Energy Codes and Standards
Trend Overview: As global awareness of climate change grows, energy codes and standards are becoming more stringent. Energy modeling is now critical in demonstrating compliance with these updated regulations, particularly for programs like LEED, ASHRAE 90.1, and others. This means modelers need to stay updated on evolving standards and integrate them into simulations to ensure compliance.
Case Study 5: Energy Simulation for Mixed-Use Development with OpenStudio and ASHRAE 90.1-2019:
A mixed-use development in Chicago needed to meet the latest requirements of ASHRAE 90.1-2019, which sets higher standards for building energy efficiency, particularly in lighting, HVAC, and building envelope performance. The energy modeling team used OpenStudio, an open-source platform built on top of EnergyPlus, to simulate the building’s energy performance in detail.
Through the use of OpenStudio, the team performed iterative simulations, focusing on key aspects of the building's performance, including HVAC efficiency, Low-E high-performance glazing, and advanced lighting controls. The team used a publicly available OpenStudio Measure to generate the ASHRAE 90.1-2019 Proposed and Baseline Buildings. This allowed the team to model and compare different energy conservation measures (ECMs) and identify the most cost-effective strategies for exceeding compliance thresholds.
By optimizing insulation, upgrading to high-efficiency windows, and implementing demand-controlled ventilation, the final design exceeded the ASHRAE 90.1-2019 requirements by 18%. This level of performance not only met the local green building code but also earned the project LEED Gold certification. OpenStudio’s robust integration with the ASHRAE 90.1-2019 standards played a critical role in achieving these results, demonstrating the value of advanced energy modeling for meeting stricter energy codes.
Conclusion
As energy modeling continues to evolve, embracing these trends is not optional—it’s necessary to stay competitive. AI and machine learning are making models more predictive, real-time data is refining operational efficiency, and full life cycle assessments are helping us think long-term. At the same time, the integration of renewables and compliance with stricter codes ensures that we’re not just meeting today’s standards but building for the future. Each of these case studies demonstrates the value of staying current with these trends, and how they can drive better, more efficient designs. Whether you’re working on optimizing geothermal systems or helping buildings meet the latest energy codes, understanding these trends will keep you ahead of the curve.