The building energy efficiency industry faces a significant data management challenge. As energy audits, models, and efficiency programs proliferate, we’re generating unprecedented amounts of EEM data. However, without standardized naming conventions, meaningful comparison becomes nearly impossible. What one program calls an “apple,” another might label an “orange” or “pear,” creating confusion and inefficiency.
A recent ASHRAE Journal article highlights this issue, presenting research conducted at the University of Cincinnati that analyzed over 3,490 EEMs from various industry resources including ASHRAE Standard 100, Research Project 1651, and Building EQ. The findings reveal both best practices and common pitfalls in EEM documentation.
Best Practices for Naming EEMs
The research identifies four key best practices and eight common errors to avoid. Above all, clarity emerges as the most critical factor in EEM naming.
Consider these contrasting examples:
- Poor naming: “High performance motors”
- Effective naming: “Replace all pump and fan motors with premium efficiency motors”
The effective example clearly communicates:
- The action being taken (replace)
- The specific elements affected (pump and fan motors)
- The improvement being made (premium efficiency motors)
Table 1: EEM Naming Conventions – Best Practices vs. Common Errors
| Best Practices | Common Errors |
|---|---|
| Use precise, actionable verbs (install, replace, upgrade) | Using vague terminology (“high performance”) |
| Specify affected equipment/systems clearly | Omitting the specific elements being modified |
| Include performance targets or efficiency levels | Lacking measurable improvement metrics |
| Maintain consistent terminology across documentation | Mixing different naming conventions |
| Indicate location or application scope | Failing to specify where the EEM applies |
| Include control strategies when applicable | Omitting operational parameters |
| Separate distinct measures into individual EEMs | Bundling multiple unrelated measures |
| Use industry-standard terminology | Creating proprietary or non-standard terms |
This precision eliminates ambiguity and ensures all stakeholders understand exactly what’s being proposed, implemented, or analyzed.
Table 2: Sample EEM Naming Transformations
| Poor EEM Description | Improved EEM Description |
|---|---|
| “Lighting upgrade” | “Replace T8 fluorescent fixtures with LED panels in office areas” |
| “Efficient HVAC” | “Install VFDs on AHU supply fans and implement static pressure reset” |
| “Better controls” | “Implement nighttime temperature setback (68°F heating, 78°F cooling)” |
| “Envelope improvements” | “Add R-30 blown cellulose insulation to attic spaces” |
| “Water conservation” | “Replace restroom fixtures with low-flow (1.28 GPF) toilets and (0.5 GPM) faucets” |
Leveraging AI for EEM Management
As building performance standards and energy auditing laws become increasingly common, the volume of EEM data continues to grow exponentially. This creates both a challenge and an opportunity.
Table 3: AI Applications for Building Energy Management
| Application Area | Current Challenge | AI/LLM Solution | Benefit |
|---|---|---|---|
| EEM Categorization | Inconsistent naming across sources | Automated recognition of similar measures | Enables cross-project comparison |
| BAS Point Labels | Vendor-specific naming conventions | Standardized point recognition | Streamlines digital twin integration |
| Audit Report Analysis | Manual extraction of recommendations | Automated extraction of key measures | Accelerates implementation planning |
| Energy Savings Verification | Difficult comparison across programs | Standardized measurement approaches | Improves reliability of savings claims |
| Implementation Cost Analysis | Variable cost reporting methods | Normalized cost comparison | Better budget forecasting |
| Measure Prioritization | Complex multi-factor decisions | Data-driven ranking algorithms | Optimized implementation sequencing |
Large Language Models (LLMs) offer a promising solution for managing this data deluge. These AI tools can automatically recognize similar measures even when they’re described differently, enabling true “apples-to-apples” comparisons across thousands of buildings and audits. Such capabilities allow for more comprehensive analysis of EEM savings and implementation costs across diverse projects and jurisdictions.
Beyond EEMs: Broader Applications
The standardization principles and AI approaches developed for EEMs have applications throughout building systems management. Building Automation System (BAS) point labels represent another area plagued by inconsistent naming conventions. With different vendors using disparate labeling systems, integrating BAS points into digital twins becomes unnecessarily complex and time-consuming.
By applying similar LLM approaches to BAS point standardization, the industry could overcome a major bottleneck in building performance optimization. This would enable:
- More efficient digital twin creation
- Improved trend analysis
- Streamlined implementation of control sequences
- Reduced manual data processing
The Path Forward
As the building industry continues its digital transformation, the standardization of terminology and categorization becomes increasingly critical. The research highlighted in this article represents an important step toward creating a common language for energy efficiency measures.
Table 4: Resources for EEM Standardization
| Resource | Type | Value to Practitioners |
|---|---|---|
| ASHRAE Standard 100 | Industry Standard | Provides baseline EEM categories |
| ASHRAE Research Project 1651 | Research Reference | Details methodology for EEM analysis |
| Building EQ | Assessment Program | Offers standardized approach to building evaluation |
| LEED v4.1 | Rating System | Provides categorization of energy-related credits |
| ISO 50001 | Management Standard | Establishes framework for energy management |
| DOE Building Energy Asset Score | Assessment Tool | Standardizes building energy performance metrics |
By adopting clear naming practices and leveraging AI tools to manage the growing complexity of building data, practitioners can spend less time sorting through messy information and more time actually improving building performance—the ultimate goal of any energy efficiency initiative.
