Digitalization & Facility Management: Energy Flexibility of Existing Buildings
Bibliographic record
Abstract
Future energy systems will require buildings to be able to manage their energy demand and generation in dynamic ways. The technical realization of such energy flexibility in buildings in response to local climate conditions, occupant needs and grid requirements is currently quantified in research and development projects, many of which take place in newly erected buildings as flagships for the related digitalization and seamless automation of technical building services. However, building stock and facility management portfolios tend to consist of existing buildings with differing, highly diverse performance qualities that may pose a problem for generic solutions. The objective of this paper is to highlight potential options, and to sketch an engineering perspective of making existing buildings energy-flexible. For this purpose, on-going work in various control research projects is selected to present issues in (1) the development of a suitable controller, (2) home and building automation design, and (3) building commissioning and diagnostics for future building controls. Non-technical requirements for quality of performance in these cases are summarized. In conclusion, and based on the reviewed projects, a potential strategy for building management is the avoidance of risks and costs associated with the introduction of energy flexibility by using published standards and open protocols for automation, and by documenting their as-operated status in a digital format in all buildings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".