Analysis of grid interaction indicators in net zero-energy buildings with sub-hourly collected data
Bibliographic record
Abstract
This paper aimed to contribute to the discussion about the role of net zero-energy buildings (ZEBs) or nearly ZEBs in future energy systems, from the perspective of the resulting import/export interaction with the surrounding energy grid (commonly named grid interaction (GI)). This investigation analyses three buildings with measured data at sub-hourly time resolution. The goal of this paper was to quantify the effect of using high-resolution data (one or a few minutes) versus hourly resolution in the GI analysis of buildings with an on-site generation system. A limited set of quantitative GI indicators have been selected: the generation multiple, the dimensioning rate and the connection capacity credit. Additionally, this paper presents graphical representations describing in an intuitive way the yearly or daily variation of the indicators. Some general trends have been identified and the usefulness of the selected indicators is demonstrated. Findings show conclusively that sub-hourly analysis will give more accurate information. Differences between peak values measured with hourly and sub-hourly time resolution can be significant. If detailed GI analysis at the individual building level is required, one should consider going for detailed sub-hourly analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".