Modeling and simulation of solar water heating system with Thermal storage
Bibliographic record
Abstract
With the increase in electricity prices and environmental concerns, new technologies are being developed to extract energy from every available source and store the excess energy generated for later usage. One such solution is provided by Thermal Energy Storage Systems. Solar radiation in summer can be stored inter-seasonally to provide heating in winter, while the cold from winter air can be used to run air conditioning in summer. This thesis studies in detail the solar thermal energy storage system used for domestic water heating purposes in a typical detached home in St. John’s, Newfoundland, Canada. It introduces the topic, discusses the background and development of the systems, and presents the basic concept of what a solar thermal energy storage system is and how it works. As well, it focuses on the availability of solar radiation, which is important for analyzing how effective the system can be, considering that the amount of solar radiation is not constant throughout the earth. In-depth information on how thermal energy storage system functions and operates, along with an extensive review of the literature, is also featured. Studies including experimental and simulation models are reviewed, which helps to compare earlier approaches to the present handling of the problem. Additionally, to establish the findings of the thesis, a simulation model of solar thermal energy storage for domestic water usage is created with the help of SAM software. Various parameters of MATLAB software are taken into consideration as well to establish the desired design of the system. These design parameters are extensively explained, together with a discussion on the assumptions and design technologies considered for creating the model for the MATLAB and BEopt simulations. Overall, this thesis demonstrates a method of designing a solar water heating system with thermal storage that can provide hot water for a small house. SAM and HOMER, which are design models that calculate the consumption of hot water and cost for a system, are extensively utilized in the study.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".