Analysis of House Space Heating System with Under-Ground Seasonal Energy Storage using PV Electricity Generation
Bibliographic record
Abstract
- In this article, the heat storage for small dwellings with seasonal storage system is in Estonia modelled. The system consists of low temperature underground insulated unit, auxiliary buffer tank inside the building and PV panels as an energy resource. In order to evaluate the heat storage and extraction processes, a more detailed seasonal storage model has been designed. This work's novelty is the usage of solar PV panels as an electricity producer for supplying energy for space heating with seasonal storage. For the energy storage media sand/soil is used, but it is acknowledged that modelling with other materials is also. The energy is carried to the storage unit and also extracted by the register of water pipes. The media around the pipes in the model is divided into slayers, which simplifies the calculation of heat exchange processes in the unit. The buffer water tank is used for short-period energy storage and for heating the tap water. The system is designed such, that energy is not supplied to the main unit in spring and summer. The main storage is used in summer and early autumn. By this design energy loss within the main unit is minimised. The results show, that when the main unit has sufficient capacity and proper insulation thickness, it is likely to adequately cater residents´ the heating and warm water demand throughout the year. Energy loss in case of a shorter periods of high temperature in the storage tank is smaller.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".