Energy Flexibility for an Institutional Building with Integrated Solar System: Case Study Analysis
Bibliographic record
Abstract
Abstract Buildings with renewable energy systems and sizeable energy storage capacity can provide significant energy flexibility. Defining appropriate load management strategies requires estimating this flexibility accurately. However, this is no easy task, since the energy flexibility depends on the building structure, heating/cooling system, meteorological conditions, occupant activities, demand response strategy, among other factors. This paper focuses on the thermal storage flexibility of a building component: a concrete floor with an embedded radiant hydronic system. An approach based on a combined adaptive Auto Regressive model with exogenous inputs (ARX) is used to quantify the thermal energy available in the concrete slab. The model describes the relationship between: (a) the geothermal pump system and weather variables and (b) the temperatures of the indoor air and the concrete surface slab. These temperatures are then used to assess the state of charge (SOC) of the slab. The SOC indicator is critical to reducing heating energy use during peak periods. Results show that the proposed model can accurately quantify the energy flexibility of the building through performance indicators. Finally, tests using real data confirm the validity of the model as a tool to estimate energy flexibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".