Energy and Cost Benefits of DC Power in ZNE Buildings
Bibliographic record
Abstract
Improvements in building end-use efficiency have significantly reduced the energy intensity of new buildings, but diminishing returns make cost-effective zero-net energy (ZNE) buildings a challenge. A largely untapped efficiency strategy is to improve the efficiency of power distribution within buildings. Direct current (DC) distribution with modern power electronics has the potential to eliminate much of the power conversion loss in alternating current (AC) building distribution networks. Previous literature suggests up to 15% energy savings in DC ZNE buildings with onsite generation and battery storage. Nonetheless, DC faces a market disadvantage against AC, and the benefits of DC distribution must be compellingly demonstrated before wide adoption. This paper presents recent modeling of DC vs. AC distribution in buildings, based on detailed load and generation profiles, wire losses, and power conversion efficiency curves. Our analysis shows that annual energy savings can range from approximately 8% of baseline electricity use in an office with PV and no battery, to approximately 15% in a building with a large PV array and battery. This paper also presents a techno-economic analysis framework that evaluates the cost-effectiveness of DC systems in several commercial buildings based on commercially available products. Based on a Monte Carlo analysis, we find that DC systems can be cost effective in all scenarios that include battery storage and onsite solar, whereas for systems without storage, DC distribution is not cost effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".