eQUEST Technical

Evaluating Open-Loop Geothermal Cooling for Data Centers

Jun 24, 2026

As data center power density increases, owners are evaluating alternatives to conventional cooling towers and air-cooled heat rejection systems. One option that deserves more attention is open-loop geothermal cooling.

In an open-loop geothermal system, groundwater is extracted from an aquifer, passed through heat exchangers, and returned to the ground through injection wells. For data centers, this can provide access to a large, stable thermal sink that may reduce mechanical cooling energy and water consumption compared to traditional evaporative cooling systems.

Why Open-Loop Geothermal Is Attractive

The primary benefit is the relatively stable temperature of groundwater. Instead of rejecting heat to hot summer air, the system can reject heat to groundwater that may remain near 10°C to 15°C in many climates.

Potential benefits include:

  • Reduced chiller energy
  • Reduced or eliminated cooling tower water consumption
  • Improved cooling efficiency during hot weather
  • Potential for heat recovery
  • Lower peak electrical demand
  • Improved resilience compared to systems dependent only on outdoor air temperature

Key Constraint: Return Water Temperature

One of the most important constraints is the maximum temperature allowed for water returned to the ground.

In many projects, the return water temperature may need to remain below approximately 30°C, depending on local regulations, aquifer conditions, and permitting requirements.

This limit is critical because data centers reject heat continuously. If the return water temperature is too high, the project may face permitting challenges, long-term aquifer warming, or reduced system performance over time.

Long-Term Thermal Balance

Open-loop geothermal should not be evaluated only on peak-day performance. The long-term thermal balance of the aquifer is often more important.

A data center is a year-round heat source. Without a strategy to restore or rebalance the ground temperature, the aquifer may gradually warm over time, reducing cooling capacity and efficiency.

One important strategy is the use of winter dry coolers. During cold weather, heat can be rejected directly to outdoor air, reducing the thermal load sent to the aquifer. In some configurations, winter operation may also help restore thermal capacity by allowing cooler water or lower heat rejection temperatures to rebalance the system.

Well Spacing Matters

The distance between extraction and injection wells is another critical design issue.

If wells are too close together, warm return water can migrate back toward the extraction well, a condition sometimes referred to as thermal short-circuiting.

Thermal short-circuiting can reduce the effectiveness of the system, increase cooling energy, and create long-term performance problems.

Proper well spacing depends on:

  • Aquifer flow direction
  • Groundwater velocity
  • Soil and rock properties
  • Injection and extraction rates
  • Temperature difference between extraction and injection water
  • Long-term operating profile

Hydrogeology Is Not Optional

Open-loop geothermal is not simply a mechanical system. It is also a hydrogeological system.

The movement of water underground determines whether the system will perform as expected. A successful design requires understanding the aquifer, groundwater flow direction, well capacity, and potential thermal migration between wells.

For large data centers, hydrogeological modeling should be part of the feasibility study. The goal is to confirm that the aquifer can support the required flow rate without unacceptable drawdown, thermal buildup, or short-circuiting.

Risks of Open-Loop Geothermal

Open-loop geothermal can be highly effective, but it introduces risks that must be evaluated carefully.

  • Thermal buildup: Continuous heat rejection can gradually warm the aquifer.
  • Thermal short-circuiting: Warm injection water can migrate back to production wells.
  • Permitting risk: Local regulations may limit groundwater temperature, flow rate, or injection strategy.
  • Water quality issues: Groundwater chemistry can create scaling, fouling, corrosion, or filtration requirements.
  • Well performance risk: Actual well capacity may differ from early assumptions.
  • Operational complexity: The system requires coordination between mechanical design, controls, groundwater systems, and monitoring.

Where Open-Loop Geothermal May Make Sense

Open-loop geothermal may be most attractive when:

  • The site has a productive aquifer with sufficient flow capacity
  • Groundwater temperatures are favorable
  • Water return temperature limits can be met
  • Well spacing can be designed to avoid thermal short-circuiting
  • Winter dry coolers can reduce annual thermal loading
  • The owner values long-term efficiency and reduced water consumption

Independent Review Is Important

For data centers, open-loop geothermal should be evaluated as part of a broader cooling strategy rather than as a standalone technology.

The best solution may combine:

  • Open-loop geothermal
  • Dry coolers
  • Water-assisted dry cooling
  • Thermal storage
  • Liquid cooling or immersion cooling
  • Heat recovery

The key question is not whether geothermal can work. The better question is whether the site, aquifer, operating profile, and controls strategy can support long-term reliable operation.

If you are evaluating geothermal, dry cooling, liquid cooling, or other advanced cooling strategies for a data center, an independent technical review can help identify benefits, risks, and practical design considerations before major capital decisions are made.

Contact Fassbender Energy Advisory to discuss an independent review of your data center cooling strategy.

What Is PUE and Why Does It Matter for AI Data Centers?

Jun 24, 2026

As artificial intelligence drives unprecedented demand for data center capacity, one challenge is becoming increasingly clear: power is difficult to obtain.

In many regions, utilities are struggling to keep pace with new data center development. Electrical interconnections that once took months may now require years. New substations can cost millions of dollars and significantly delay projects.

When power becomes the limiting factor, every kilowatt matters.

This is where Power Usage Effectiveness, or PUE, becomes important.

PUE is one of the most widely used metrics in the data center industry because it helps owners understand how much of their electrical power is actually reaching the servers that generate revenue.

What Is PUE?

Power Usage Effectiveness is defined as:

PUE = Total Facility Power ÷ IT Equipment Power

Where:

  • Total Facility Power includes servers, cooling systems, lighting, UPS systems, pumps, fans, and all supporting infrastructure.
  • IT Equipment Power includes only the equipment performing computing functions.

A perfect data center would have a PUE of 1.0, meaning every watt entering the facility is used by the IT equipment.

In reality, every data center requires supporting infrastructure, so actual PUE values are higher.

Why PUE Matters

Consider a data center with a utility service capacity of 10 MW.

PUE = 2.0

Total Facility Power: 10 MW

IT Equipment Power:

10 MW ÷ 2.0 = 5 MW

Only half of the available power reaches the servers.

PUE = 1.5

Total Facility Power: 10 MW

IT Equipment Power:

10 MW ÷ 1.5 = 6.7 MW

PUE = 1.2

Total Facility Power: 10 MW

IT Equipment Power:

10 MW ÷ 1.2 = 8.3 MW

The difference is significant.

Improving PUE from 2.0 to 1.2 effectively increases available computing capacity by approximately 66% without requiring additional utility power.

When utility capacity is difficult to obtain, improving PUE may be one of the fastest ways to increase usable computing capacity.

What Is a Good PUE?

PUE values vary significantly depending on facility age, climate, cooling strategy, IT density, redundancy requirements, and operating conditions.

Typical ranges include:

Facility Type Typical PUE
Older Enterprise Data Centers 1.8 – 2.5
Modern Enterprise Facilities 1.4 – 1.8
Hyperscale Data Centers 1.1 – 1.4
Best-In-Class Facilities Near 1.1

While a lower PUE is generally desirable, the lowest PUE is not always the best business decision.

Owners must balance:

  • Capital cost
  • Water consumption
  • Reliability
  • Maintenance
  • Utility costs
  • Future flexibility

The optimal solution often depends on project goals rather than achieving the lowest possible PUE.

The Largest Drivers of PUE

While many factors influence data center efficiency, several design decisions typically have the greatest impact.

1. Cooling System Design

Cooling is often the largest non-IT electrical load in a data center.

The cooling strategy may include:

  • Computer Room Air Handlers (CRAHs)
  • Computer Room Air Conditioners (CRACs)
  • Chilled water systems
  • Cooling towers
  • Dry coolers
  • Direct-to-chip liquid cooling
  • Immersion cooling

As rack densities increase, cooling systems become increasingly important drivers of overall facility efficiency.

A small improvement in cooling efficiency can produce substantial reductions in facility power consumption.

Cooling strategy also affects water consumption. A low-PUE design may use significant water if it relies heavily on evaporative cooling, while a slightly higher-PUE design may dramatically reduce water use through dry coolers or hybrid cooling strategies.

For a deeper discussion of water-efficient cooling strategies, see Reducing Data Center Water Consumption with Dry Coolers: A Practical Approach for the AI Era.

2. Airflow Management

Poor airflow management forces cooling equipment to work harder than necessary.

Common optimization strategies include:

  • Hot aisle containment
  • Cold aisle containment
  • Blank-off panels
  • Cable management
  • Air leakage reduction

These relatively simple improvements can often produce meaningful reductions in cooling energy.

3. Higher Cooling Water Temperatures

One of the most effective ways to improve efficiency is operating cooling systems at higher temperatures.

Benefits may include:

  • Increased economizer hours
  • Reduced compressor operation
  • Improved chiller efficiency
  • Improved heat rejection performance

Many modern facilities are designed to operate at temperatures that would have been considered aggressive only a decade ago.

4. Liquid Cooling

The rapid growth of AI workloads is driving interest in liquid cooling technologies.

Traditional air cooling becomes increasingly difficult as rack densities increase.

Today it is common to see:

  • 30 kW racks
  • 50 kW racks
  • 100 kW racks

Some AI deployments are already exceeding these values.

Liquid cooling technologies can reduce fan energy while improving heat removal effectiveness.

Common approaches include:

  • Direct-to-chip cooling
  • Rear-door heat exchangers
  • Immersion cooling

These technologies are expected to play an increasingly important role in future data center designs.

5. Heat Rejection Systems

Ultimately, every watt consumed by a server becomes heat that must be removed.

Heat rejection systems may include:

  • Cooling towers
  • Dry coolers
  • Hybrid systems
  • Adiabatic systems

Each approach involves tradeoffs between energy consumption, water consumption, capital cost, and reliability.

6. Electrical Distribution Efficiency

Not all losses occur within the cooling system.

Electrical infrastructure also consumes power through:

  • UPS losses
  • Transformer losses
  • Power conversion losses
  • Distribution losses

While individual losses may seem small, they can become significant in large facilities.

Is PUE the Only Metric That Matters?

No.

PUE remains a useful metric, but it should not be evaluated in isolation.

For example, a facility may achieve an excellent PUE by using large amounts of water. Another facility may operate at a slightly higher PUE while dramatically reducing water consumption through dry coolers, thermal storage, or other cooling strategies.

For a deeper discussion of water-efficient cooling strategies, see my article: Reducing Data Center Water Consumption with Dry Coolers: A Practical Approach for the AI Era.

Similarly, some facilities may prioritize reliability, redundancy, or operational flexibility over achieving the absolute lowest PUE.

Owners should evaluate the complete picture, including:

  • PUE
  • Water Usage Effectiveness (WUE)
  • Reliability
  • Utility costs
  • Sustainability goals
  • Future expansion requirements

The Future of PUE in AI Data Centers

As AI continues driving higher rack densities and increasing power demand, efficient infrastructure becomes increasingly valuable.

The challenge is no longer simply reducing operating costs.

In many markets, improving efficiency directly increases the amount of computing capacity that can be deployed within a fixed utility power allocation.

For owners and developers, that may represent one of the most valuable opportunities available.

Independent Review of Data Center Energy Infrastructure

Whether evaluating a new facility or upgrading an existing one, understanding the factors that drive PUE can help identify opportunities to improve efficiency, increase computing capacity, and reduce long-term operating costs.

I provide independent technical reviews of data center energy infrastructure, including cooling systems, electrical systems, utility constraints, thermal storage concepts, and emerging cooling technologies.

If you're evaluating a data center project and would like an independent perspective, visit Fassbender Energy Advisory.

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AI and Energy Analysis: What It Does Well, What It Doesn't, and What Should Worry Us

Jun 22, 2026

Every few weeks I see another prediction that AI will replace energy modelers. Having spent hundreds of hours using AI tools alongside eQUEST, EnergyPlus, OpenStudio, spreadsheets, and custom workflows, my conclusion is more nuanced.

AI is already useful in energy modeling. In some areas it is remarkably effective. In others it is unreliable. And in a few areas it introduces entirely new risks.

The key question isn't whether AI will impact energy modeling. It already has. The more important question is where it adds value and where human judgment remains essential.

Interested in AI and Energy Analysis?

I'm launching a series exploring practical AI applications, emerging products, training opportunities, prompts, and workflows for energy professionals.

Join the AI & Energy Analysis community to receive updates, share your experience, suggest products, and learn about future training opportunities.

Join the community here

Registration takes less than 3 minutes.

GUI Navigation and Software Support

Many modelers use AI as a substitute for software documentation. This can work for simple questions, but it has limitations.

Large language models will confidently tell you which menu item to click, even when the menu item does not exist. I have personally spent hours searching for fictional fields that AI repeatedly assured me were present.

This can be improved through careful prompting and by providing screenshots or documentation. Even so, GUI assistance remains one of the weaker applications of AI because software interfaces change frequently and hallucinations are difficult to detect until time has already been wasted.

When possible, always verify software-specific instructions against documentation or a known workflow (such as a training demo)

Geometry Creation

AI still struggles with geometry.

While image recognition continues to improve, AI has difficulty accurately interpreting complex CAD drawings, floor plans, and architectural documents. It performs much better when geometry can be described through text, dimensions, coordinates, and structured inputs.

Even when geometry is accurately defined, human judgment remains essential. Determining appropriate thermal zoning, identifying perimeter versus core spaces, understanding occupancy patterns, and simplifying geometry for simulation are often more important than reproducing every architectural detail.

The challenge in energy modeling is rarely drawing the building. The challenge is understanding how the building actually behaves.

Creating Components and Inputs

This is one area where AI performs surprisingly well.

If provided with a sample, AI can generate schedules, constructions, equipment definitions, and other text-based inputs for EnergyPlus, eQUEST, and similar tools.

For example, AI can convert an Excel schedule table into an EnergyPlus schedule object or an eQUEST schedule definition within seconds. It can create repetitive objects, generate template inputs, and help translate information between formats.

However, attention to detail remains important. AI frequently introduces extra line breaks, formatting artifacts, or subtle syntax errors that can cause import failures. Verification is still required.

The good news is that AI is usually much faster at creating these components than a human. The bad news is that the last 5% still matters.

Bulk Editing and Automation

In my opinion, this is one of the most underrated uses of AI.

Energy models often require repetitive edits across hundreds or thousands of objects. AI is very effective at helping create regular expressions, search-and-replace routines, and custom scripts.

Even modelers with limited programming experience can use AI to generate Python scripts that make large-scale modifications to models. Tasks that previously required hours of manual editing can often be automated in minutes.

I've found AI particularly useful for:

  • Editing large eQUEST input files
  • Creating regular expressions for bulk text replacement
  • Converting spreadsheets into simulation inputs
  • Writing Python scripts for model transformations
  • Automating repetitive QA/QC checks

For many practitioners, this may be the highest-return application of AI available today.

Want AI prompts, scripts, and workflow examples?

I'm collecting interest in practical AI workflows for eQUEST, EnergyPlus, OpenStudio, QA/QC, calibration, scripting, report writing, and energy analysis.

Join the AI & Energy Analysis community

Results Analysis

AI excels at processing large amounts of tabular data.

It can quickly identify trends, summarize utility data, compare alternatives, and highlight anomalies in simulation outputs. For large spreadsheets containing thousands of rows, AI often spots patterns that would take significant time to identify manually.

This is especially valuable when reviewing annual simulation outputs, calibration results, utility bills, or ECM comparisons.

That said, important findings should always be spot-checked. AI can occasionally misinterpret units, labels, or relationships within complex datasets.

Trust, but verify.

Calibration Support

With sufficient context, AI can provide useful calibration suggestions.

It is particularly good at recognizing patterns such as seasonal biases, load profile mismatches, occupancy assumptions, and operational inconsistencies.

For example, if a building consistently overpredicts winter gas consumption while matching summer electricity usage, AI can often suggest several plausible causes worth investigating.

The challenge is that calibration problems are frequently highly specific to individual buildings. AI tends to generate plausible explanations, some of which may be completely wrong.

Outliers, unusual operating conditions, maintenance issues, control sequences, and occupant behavior still require engineering judgment and field knowledge.

Calibration remains as much an investigative process as a technical one.

Results Interpretation

AI can be a useful sounding board for interpreting results.

It can help summarize findings, identify unusual relationships, explain trends, and suggest additional analyses worth exploring.

What it cannot reliably do is determine whether a result is physically reasonable.

Experienced modelers develop intuition over time. They know when an energy use intensity seems unrealistic. They recognize suspicious end-use breakdowns. They understand when a savings claim appears too good to be true.

AI can assist with interpretation, but it cannot replace engineering skepticism.

ECM Identification

AI can be an effective brainstorming partner for energy conservation measures.

Given enough information about a building, it can generate a broad list of potential improvements and identify interactions between systems.

However, the quality of recommendations depends heavily on the information provided.

If local labor availability, contractor capabilities, maintenance requirements, owner preferences, and budget constraints are not included, AI often proposes solutions that are technically feasible but impractical in the real world.

The best ECMs are not necessarily the most efficient. They are the measures that can actually be implemented.

Reporting

Reporting may be the area where AI provides the greatest immediate value.

AI can quickly organize findings, summarize results, draft narratives, create report structures, and help communicate technical information to non-technical audiences.

Tasks that previously consumed hours can often be completed in minutes.

The concern is that AI has become so good at generating professional-looking reports and filling out templates that it may become difficult to distinguish between a report based on actual analysis and one generated with very little underlying work.

In other words, AI can make a mediocre analysis look impressive.

As AI adoption increases, technical reviewers may need to spend less time evaluating how well a report is written and more time evaluating whether the underlying analysis is credible.

The Real Risk

The greatest risk is not that AI will replace energy modelers.

The greater risk is that AI will make it easier for inexperienced practitioners to appear competent.

Energy modeling has always depended on engineering judgment, understanding building systems, identifying bad inputs, recognizing questionable assumptions, and knowing when results do not make sense.

AI can accelerate many tasks.

It cannot replace the experience required to know when a model is wrong.

The modeler of the future will likely spend less time entering data and more time evaluating assumptions, validating results, and making decisions.

That's where the real value has always been.

What's Next?

This article has focused primarily on general-purpose AI tools such as ChatGPT, Claude, Gemini, and Copilot.

However, a new generation of products is emerging that combines AI, machine learning, and energy modeling in ways that were difficult to imagine just a few years ago.

Over the past year, I've had the opportunity to evaluate several of these technologies. Some are AI-assisted energy modeling platforms designed to accelerate model creation and analysis. Others leverage machine learning trained on thousands—or even millions—of simulations to predict building performance, identify optimization opportunities, or create building-specific energy prediction engines.

In one case, I worked with a firm that generated thousands of simulations for a particular building type and used the results to develop an AI-driven prediction engine capable of rapidly evaluating design alternatives. The technology was impressive, but it also raised important questions about transparency, training data, accuracy, and where engineering judgment still fits into the process.

Over the coming months, I'll be exploring these technologies in greater detail, including:

  • AI-assisted energy modeling platforms
  • Machine learning for building performance prediction
  • AI tools for calibration and analytics
  • AI-assisted commissioning and fault detection
  • AI workflows for eQUEST, EnergyPlus, and OpenStudio users
  • Product reviews and interviews with developers
  • Training opportunities for practitioners looking to leverage AI effectively

I've also launched an AI & Energy Analysis interest survey to identify the most promising use cases, products, and training opportunities.

If you're using AI in your workflow, developing a product, interested in AI-related training, or simply want to follow the series, I invite you to join the community:

https://energy-models.com/ai-and-energy-analysis-interest-survey

Registration takes less than 3 minutes.

The future of energy modeling may not be AI versus engineers.

It may be engineers who know how to leverage AI versus those who don't.

What Is a Building Decarbonization Roadmap? And Do You Need an Energy Model?

Jun 3, 2026

Over the past few years, I've noticed a shift in the questions building owners are asking.

Ten years ago, many projects were focused on reducing utility bills.

A few years ago, much of the discussion centered around incentives such as 179D, utility rebates, and LEED certification.

Today, more owners are asking about carbon emissions, electrification, net zero buildings, and long-term decarbonization plans.

That often leads to a term that sounds more complicated than it really is:

Building Decarbonization Roadmap.

So what is a decarbonization roadmap, and does it require an energy model?

What Is a Building Decarbonization Roadmap?

A building decarbonization roadmap is simply a plan for reducing greenhouse gas emissions over time.

In most cases, the roadmap identifies:

  • Where the building's emissions come from today
  • Which improvements will reduce emissions
  • The expected cost of those improvements
  • The expected carbon reduction
  • A recommended implementation sequence

Think of it as a long-term plan rather than a single project.

A building owner may not replace every system at once. Instead, they may plan improvements over 5, 10, or even 20 years as equipment reaches the end of its useful life.

Where Do Building Emissions Come From?

For most commercial buildings, emissions generally come from:

  • Electricity consumption
  • Natural gas consumption
  • Fuel oil or propane, if applicable
  • District heating or cooling systems

The largest contributors often include:

  • Heating systems
  • Cooling systems
  • Ventilation systems
  • Lighting
  • Plug and process loads

The exact breakdown depends heavily on the building type, operating schedule, and climate.

A hospital, office building, school, and data center can have very different emission profiles even if they are the same size.

Common Decarbonization Strategies

Many roadmaps include some combination of the following strategies.

Lighting Upgrades

Reducing lighting power often provides immediate savings while also reducing cooling loads.

HVAC Improvements

Variable speed drives, improved controls, heat recovery, and higher-efficiency equipment can significantly reduce energy use.

Electrification

Many organizations are evaluating electric heat pumps as an alternative to fossil-fuel heating systems.

Renewable Energy

Solar PV is commonly evaluated to offset a portion of annual electricity consumption.

Building Envelope Improvements

Insulation, glazing improvements, and air sealing can reduce heating and cooling requirements.

Why Utility Bills Alone Are Not Enough

One of the biggest misconceptions is that utility bills can identify the best path forward.

Utility bills tell us what happened.

They do not tell us what will happen if we change the building.

For example:

  • What happens if the boilers are replaced with heat pumps?
  • What happens if ventilation rates change?
  • What happens if lighting is upgraded?
  • What happens if solar is installed?

Utility bills cannot answer those questions.

This is where energy modeling becomes useful.

When Is an Energy Model Helpful?

Not every decarbonization study requires a detailed energy model.

For relatively simple projects, benchmarking and utility analysis may provide enough information.

However, an energy model becomes increasingly valuable when:

  • Multiple energy conservation measures are being considered
  • Electrification is being evaluated
  • Future utility costs are important
  • Carbon reductions must be quantified
  • Funding or incentives require documentation
  • The project involves large capital expenditures

An energy model allows the team to test multiple scenarios before spending money in the field.

In many cases, the model helps identify which measures provide the greatest reduction in energy use, operating cost, or carbon emissions.

A Simple Example

Suppose a building owner wants to reduce emissions by 40%.

Several options might be considered:

  • LED lighting upgrades
  • Improved controls
  • Heat recovery
  • Heat pump conversion
  • Solar PV

The challenge is that these measures interact with each other.

Reducing lighting power reduces cooling loads.

Improving ventilation control affects heating and cooling energy.

Solar production changes the building's net electrical consumption.

An energy model allows these interactions to be evaluated together rather than estimating each measure independently.

The Goal Is Not Just Lower Carbon

One mistake I occasionally see is focusing exclusively on carbon reduction.

Most building owners are trying to balance several objectives:

  • Carbon reduction
  • Operating cost reduction
  • Occupant comfort
  • Reliability
  • Maintenance requirements
  • Capital cost

A good decarbonization roadmap considers all of these factors.

Final Thoughts

A building decarbonization roadmap is simply a plan for reducing emissions in a practical and cost-effective way.

For some buildings, utility analysis may be sufficient to identify opportunities.

For larger projects, electrification studies, or major capital investments, an energy model can provide valuable insight into how different measures interact before money is spent.

As carbon reporting requirements, electrification initiatives, and sustainability goals continue to evolve, I expect more owners to evaluate decarbonization strategies over the coming years.

The question is no longer whether improvements are possible.

The real question is which improvements provide the greatest benefit for the investment.

Questions About Building Decarbonization?

Every building is different, and the best path forward depends on factors such as climate, utility rates, building type, operating schedule, and existing equipment.

If you're considering a decarbonization study, electrification project, carbon reduction plan, or simply want to understand where your building's emissions are coming from, we'd be happy to discuss your project.

Click the Contact Us icon in the lower-right corner of this page and tell us a little about your building. I'll review the information and let you know whether a utility analysis, energy model, or more detailed study makes sense for your situation.

Which States Offer the Highest HOMES and HEAR Rebates?

May 4, 2026

Which States Offer the Highest HOMES and HEAR Rebates?

Some states are now offering very large residential energy rebates under the Inflation Reduction Act. In a few cases, combined HOMES and HEAR incentives can reach up to $34,000 per residence, depending on the state, income level, project scope, and savings pathway.

I put together the map below to make it easier to compare the top states, including program status, maximum rebate amounts, multifamily availability, and where energy modeling may help unlock larger incentives.

What This Map Shows

The map highlights selected states with notable HOMES and HEAR rebate opportunities. It includes estimated maximum rebate levels, whether multifamily projects are currently a good fit, and whether energy modeling is likely to be required, helpful, or less important.

This is not a final eligibility determination. State programs are still evolving, and many programs are launching in phases. The real rebate amount depends on the project, income category, building type, energy savings, and state-specific rules.

What Are HOMES Rebates?

HOMES, also known as Home Efficiency Rebates, is the performance-based rebate program. Instead of paying only for specific pieces of equipment, HOMES rewards projects that reduce whole-building energy use.

In many states, the larger rebate tiers are tied to modeled or measured energy savings, commonly at the 20% or 35% savings level. That makes HOMES especially important for deeper retrofits, multifamily projects, and projects where multiple upgrades work together.

What Are HEAR / HEEHRA Rebates?

HEAR, often referred to by its formal name HEEHRA, is the electrification rebate program. It focuses on upgrades such as heat pumps, heat pump water heaters, electric cooking equipment, electrical panels, wiring, insulation, and air sealing.

HEAR rebates are generally more equipment-based and income-limited. They can be simpler to understand than HOMES rebates, but they may not capture the full value of a project if whole-building energy savings are also available.

Why the Best States Are Not Always Obvious

The federal government created the rebate programs, but each state is responsible for implementation. That means the rules can vary significantly from one state to another.

Some states are live. Some are partially live. Others have approved plans but are not yet accepting applications. Multifamily rules can also vary, and some programs may be available only through specific pathways, administrators, or pilot phases.

This is why two similar projects in two different states can have very different rebate opportunities.

Why Energy Modeling Matters

Energy modeling can help determine whether a project is likely to meet the savings thresholds required for higher HOMES rebates. It can also help compare retrofit packages before construction decisions are finalized.

For many projects, the question is not simply whether a rebate exists. The better question is whether the project is being designed and documented in a way that captures the largest reasonable rebate.

  • Estimate whether a project can reach 20% or 35% energy savings
  • Compare HVAC, envelope, controls, and electrification options
  • Identify which measures are driving the savings
  • Support rebate applications where modeled savings are allowed or required
  • Help avoid leaving available rebate money on the table

Why Multifamily Projects Deserve Special Attention

Multifamily projects can be especially valuable because incentives may apply across many dwelling units. However, they can also be more complicated. Income qualification, ownership structure, common areas, central systems, and program availability can all affect eligibility.

For larger residential buildings, energy modeling can often provide a clearer path through the incentive rules and help determine whether a performance-based approach is worth pursuing.

Common Mistakes with HOMES and HEAR Rebates

  • Assuming every state offers the same rebate amounts
  • Assuming a program is live just because funding has been approved
  • Missing the higher rebate tier because savings were not evaluated early
  • Relying only on equipment rebates when performance rebates may also be available
  • Overlooking multifamily restrictions, pilot phases, or income requirements
  • Not coordinating rebates with utility programs, tax credits, or other incentives

Need Help Estimating Your Rebate?

Programs are still changing, and final eligibility depends on the state, building type, income category, project scope, and energy savings pathway.

If you have a project in mind, we can review the details and help estimate your potential rebate.

Contact us to estimate your rebate

FCM Green Municipal Fund: What Energy Modeling You Actually Need (And Where Projects Go Wrong)

Apr 22, 2026

Many projects applying to the FCM Green Municipal Fund include an energy model.

Far fewer include one that actually reflects how the building operates.

On paper, the model may look reasonable. The report is clean. The savings appear solid.

But if the assumptions are off, the results are off. And when funding decisions are based on those results, that becomes a real risk.

What FCM Is Really Looking For

Programs supported by the Federation of Canadian Municipalities (FCM), including the Green Municipal Fund (GMF), are designed to support projects that deliver measurable environmental and energy performance improvements.

At a high level, that means:

  • a credible baseline
  • realistic energy savings
  • defensible assumptions

In most cases, the energy model is the primary tool used to demonstrate all three.

That makes the quality of the model critical.

Where Energy Models Typically Go Wrong

In practice, many models are built to satisfy a requirement, not to reflect actual building performance.

Common issues include:

  • models not tied to utility data
  • ventilation rates that do not match real operation
  • unrealistic occupancy and schedule assumptions
  • plug and process loads that distort results
  • stacked energy conservation measures that overstate savings

The model runs. The outputs look reasonable.

But the building does not behave that way.

That disconnect is where problems start.

Why This Matters for Funding

Funding decisions rely on projected performance.

If the model overstates savings, the project can look stronger than it actually is.

If the model misrepresents system behavior, the wrong measures may be prioritized.

And if the assumptions cannot be defended, the entire application becomes weaker.

For publicly funded projects, this is more than a technical issue. It is a credibility issue.

The Role of Calibration

While calibration is not always explicitly required, it is often the most reliable way to establish a defensible baseline.

Calibration aligns the model with actual utility data and observed building performance.

At a practical level, this typically involves:

  • matching energy use to monthly utility bills
  • separating base load from weather-driven loads
  • validating key assumptions about system operation

This process does not need to be overly complex, but it does need to reflect reality.

Once the baseline is grounded in actual performance, projected savings become far more reliable.

Why Calibration Strengthens FCM Applications

For projects pursuing funding through FCM, the goal is not just to show savings. It is to show savings that can be trusted.

A calibrated model helps:

  • demonstrate realistic and defensible projections
  • support better decision-making around energy conservation measures
  • reduce the risk of underperformance after implementation

In many cases, calibration also changes which measures actually provide value.

Connecting the Dots

If you want a deeper look at why energy models often miss the mark, you can read more here:

Why Your Energy Model Matters When Applying for Funding in Canada

Final Thought

An energy model is one of the most important inputs into a funded project.

If it does not reflect how the building actually operates, the results are difficult to trust.

When real funding and real decisions are on the line, that matters.

Need a Second Set of Eyes?

If you are working on an FCM-funded project and want to make sure your model reflects reality, I offer focused model reviews and calibration support.

Learn more about energy modeling support and services

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