A framework for the design of representative neighborhoods for energy flexibility assessment in CityLearn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The electricity grid in the U.S. continues to undergo system-wide changes due to electrification of end-uses, as well as the adoption of intermittent on-site renewable energy systems. When effectively managed, distributed energy resource in buildings can provide energy flexibility to alleviate grid loads at critical periods. However, it is important to understand the role of geographic, climatic and occupant behavioral differences on the effectiveness of their flexibility. Thus, we provide a framework that uses an open-source U.S. building-stock database with clustering techniques to design representative neighborhoods for distributed energy resource control algorithm benchmarking. We demonstrate an application in three neighborhoods that use reinforcement learning control for energy storage system management and simulation results show up to 42% reduction in peak demand, amongst other key performance indicators.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it