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Analysis of House Space Heating System with Under-Ground Seasonal Energy Storage using PV Electricity Generation

2023· article· en· W4387986012 on OpenAlexaff

Bibliographic record

VenueInternational Journal of Renewable Energy Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEnergy and Environmental Systems
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsElectricityEnergy storageEnvironmental sciencePhotovoltaic systemSpace (punctuation)Electricity systemElectricity generationElectrical engineeringMeteorologyEnvironmental economicsEngineeringArchitectural engineeringComputer scienceGeographyPhysicsEconomicsPower (physics)

Abstract

fetched live from OpenAlex

- 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.079
GPT teacher head0.359
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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