Effects on Environmental Impact and Economics of Component Efficiencies for a Heating System with Seasonal Thermal Storage
Bibliographic record
Abstract
Background and Objective: The use of intermittent thermal energy sources for heating, in combination with seasonal thermal energy storage, may be advantageous compared to conventional heating systems.The analysis of heating systems with seasonal thermal energy storage is complex, as there are many variables that potentially affect overall design and operation.The effects of subsystem characteristics on overall system economics and environmental impact are not fully understood at present.This study investigates how subsystem efficiencies, pipe losses and peak consumer load affect economics and carbon dioxide emissions. Materials and Methods:A method for analyzing the economic and environmental aspects of a heating system with seasonal thermal energy storage is developed and presented.The present study focuses on the influence of subsystem efficiency values and losses on system performance, rather than on detailed thermodynamic analyses.Values of subsystem efficiencies and thermal losses are varied within ranges reported in the literature.The system utilizes a solar thermal source, an underground thermal energy storage and a natural gas backup boiler, and is taken to serve a residential building in Ottawa, Canada.Results: The thermal supply piping and seasonal thermal energy storage are found to have the highest capital cost followed by the solar collectors and backup boiler.The consumer load has the greatest effect on economics and carbon dioxide emissions.The backup system efficiency has little effect on system economics due to the high solar fraction.Conclusions: The study provides insight into the importance of the characteristics of various subsystems of the system on its operation, cost and carbon dioxide emissions.The results and trends developed can aid design and feasibility studies.Future work is merited to analyze heating systems using alternative subsystem technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".