Using Electric Water Heaters (EWHs) for Power Balancing and Frequency Control in PV-Diesel Hybrid Mini-Grids
Bibliographic record
Abstract
Aim of this paper is the analysis of the energy performance of secondary school buildings in North Greece and the investigation of proposals for their energy upgrade.The survey was carried out by monitoring the energy performance of 20 secondary school buildings in the prefecture of Evros in Thrace and by simulating representative school buildings.Energy data both for heating and electricity, together with other information concerning structural details and operational characteristics for a period of 5 years (2001)(2002)(2003)(2004)(2005) were collected.The energy data for the secondary schools are presented and compared with data from other regions in Greece.The mean heating energy consumption of secondary school buildings in the prefecture is 70.6 kWh/m 2 , with insulated buildings performing with 27% less energy consumption than non-insulated buildings.Simulation of representative school buildings in this area suggests that measures for natural lighting, reduction of infiltration losses, controlled ventilation during the winter, shading and natural ventilation during summer and the effective functioning of heating and lighting system are the major priority for the school building stock.Especially in the 'old school buildings', this type of interventions are necessary not only for achieving energy efficiency but for obtaining thermal comfort conditions in their interior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".