Control-oriented model of a solar community with seasonal thermal energy storage: development, calibration and validation
Bibliographic record
Abstract
The development of a control-oriented model of a solar community with seasonal storage (the Drake Landing Solar Community) is investigated. The proposed approach, intended to facilitate the development and testing of control strategies and targeting an actual predictive control implementation, is based on grey-box models, and enables the prediction of the system state (temperatures at key locations). This paper discusses the concept of state update procedure (whereby the system state is periodically corrected with measurements), which plays a fundamental role for control purposes. Firstly, the DLSC is presented and both operation and monitoring system are described. Secondly, a simplified model is developed for each sub-system: district and solar loops, short-term (water tanks) and seasonal (borehole) thermal energy storage, and existing operation rules are encoded. Finally, the model is calibrated and validated by using measurements at 10-min intervals over two years of operation (2015–2016, 2016–2017) and accurately predicts the system performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".