Novel method for power flow optimization in commercial buildings
Bibliographic record
Abstract
Energy consumption of Commercial Buildings represents around 40% of the total energy produced in North America. Total rated power of the small dry type transformers (DTT) installed within a typical Large Office Building (LOB) is 1.5 MVA at minimum. Typically DTT are energized 24/7 being loaded at 4%-7% of their rated power, running about 4800-5500 hours/year at efficiency values of 52%-94%. This paper presents the outcomes of a demonstration project that considered `smart' configurations of DTT as an alternate to existing methods. This project is intended to become one of the enablers of LEED Volume Program for Design and Construction and Program of Operation and Maintenance launched by USA and Canada Green Building Councils (USGBC and CGBC) in 2010/11. The design proposed in this paper is an “out-of-the-box” approach that can change traditional design of power grids in LOB. This work demonstrated that power flow within the LOB grid can be optimized by dynamic reconfiguration of the power grid topology, the static and dynamic loading of the DTTs leading to improvement in DTT performance while reducing the DTT power demand. The LOB smart grid will adapt itself to the optimum power flow with no human intervention. This paper also proposes new concepts of designing 600V/480V/208V/120V distribution power grid within LOB that will result in reduction of capital costs (Capex), increase operational energy savings, and reduce maintenance costs (Opex).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".