# Energy Engineers, Except Wind and Solar

> Design, develop, or evaluate energy-related projects or programs to reduce energy costs or improve energy efficiency during the designing, building, or remodeling stages of construction. May specialize in electrical systems; heating, ventilation, and air-conditioning (HVAC) systems; green buildings; lighting; air quality; or energy procurement.

- **SOC code:** 17-2199.03
- **Canonical URL:** https://singulariki.com/roles/role-17-2199-03
- **Also known as:** Energy Efficiency Engineer, Energy Engineer, Industrial Energy Engineer, Test and Balance Engineer, Measurement And Verification Engineer, Alternative Energy Engineer, Carbon Analyst, Carbon Specialist
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Identify and recommend energy savings strategies to achieve more energy-efficient operation.
- Conduct energy audits to evaluate energy use and to identify conservation and cost reduction measures.
- Monitor and analyze energy consumption.
- Monitor energy related design or construction issues, such as energy engineering, energy management, or sustainable design.
- Inspect or monitor energy systems, including heating, ventilating, and air conditioning (HVAC) or daylighting systems to determine energy use or potential energy savings.
- Analyze, interpret, or create graphical representations of energy data, using engineering software.
- Advise clients or colleagues on topics such as climate control systems, energy modeling, data logging, sustainable design, or energy auditing.
- Verify energy bills and meter readings.
- Collect data for energy conservation analyses, using jobsite observation, field inspections, or sub-metering.
- Manage the development, design, or construction of energy conservation projects to ensure acceptability of budgets and time lines, conformance to federal and state laws, or adherence to approved specifications.
- Perform energy modeling, measurement, verification, commissioning, or retro-commissioning.
- Review architectural, mechanical, or electrical plans or specifications to evaluate energy efficiency.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Problem Sensitivity _(ability)_
- Critical Thinking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Deductive Reasoning _(ability)_
- Inductive Reasoning _(ability)_
- Information Ordering _(ability)_
- Mathematics _(knowledge)_
- Active Listening _(essential_skill)_
- Writing _(essential_skill)_

**Skills in demand:**
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Mathematics _(Common Skill)_
- Writing _(Common Skill)_
- Systems Analysis _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Listening _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Active Learning _(Common Skill)_

**Tools & technology:**
- Autodesk AutoCAD _(hot technology, in demand)_
- C++ _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- The MathWorks MATLAB _(hot technology, in demand)_
- Microsoft Access _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Project _(hot technology)_
- Microsoft Visio _(hot technology)_
- Microsoft Word _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 80th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 67th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 71st percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 9th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 2.1% growth (About average); 9.3k annual openings; 158.8k → 162.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $117,750; 150,750 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 39% automation, 52% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Analyze, interpret, or create graphical representations of energy data, using engineering software. _(2.9% of measured AI use; directive)_
- Prepare project reports and other program or technical documentation. _(2.3% of measured AI use; task iteration)_
- Promote awareness or use of alternative or renewable energy sources. _(0.6% of measured AI use; learning)_
- Identify energy savings opportunities and make recommendations to achieve more energy efficient operation. _(0.5% of measured AI use; directive)_
- Monitor and analyze energy consumption. _(0.4% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me analyze, interpret, or create graphical representations of energy data, using engineering software.
- Help me prepare project reports and other program or technical documentation.
- Help me promote awareness or use of alternative or renewable energy sources.
- Help me identify energy savings opportunities and make recommendations to achieve more energy efficient operation.
- Help me monitor and analyze energy consumption.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-17-2199-03_