Abstract
Retrofitting buildings is usually an expensive and labor-intensive process. Weatherization measures can improve comfort and energy affordability to some extent, but deep energy retrofits are needed to optimize performance and comfort, and to achieve significant energy cost savings. Barriers to deep energy retrofits include a limited supply of skilled labor, different building types, planning complexity, split incentives, and a long or non-existent ROI horizon. The “Simple Panel System” (SPS) workflow developed and demonstrated in this effort streamlines deep energy retrofits by applying advanced site capture, machine learning, and mixed reality to panelized construction. The result is a one-stop, product-independent solution for rapidly scalable retrofits with the potential to reduce construction time and project costs by 50%. Soft costs are reduced by more than 66%, total costs by more than 50%, and field construction time by more than 50%-not to mention the reduction in construction waste, improvement in working conditions, and the ability to scale without an influx of skilled labor. This paper presents the SPS and the preliminary results and findings from the pilot project.
| Original language | American English |
|---|---|
| Number of pages | 9 |
| DOIs | |
| State | Published - 2025 |
| Event | 2025 Buildings XVI International Conference - Clearwater, FL Duration: 8 Dec 2025 → 11 Dec 2025 |
Conference
| Conference | 2025 Buildings XVI International Conference |
|---|---|
| City | Clearwater, FL |
| Period | 8/12/25 → 11/12/25 |
Bibliographical note
See NLR/CP-5500-93482 for preprint.NLR Publication Number
- NLR/CP-5500-101368
Keywords
- ABC
- advanced building construction
- automation
- deep energy retrofit
- DER
- industrialized construction
- machine learning
- ML
- panel
- panelized
- prefab
- prefabrication
- simple panel system
- SPS
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