Future Energy Paradigm of Microgeneration Technologies for Decentralized Smart Home and Office Buildings
Bibliographic record
Abstract
Korea Institute of Energy Research (KIER) and Canmet Energy have initiated the joint research project in carrying out investigation and analysis of energy savings of load sharing applications featuring single detached residential and commercial buildings. Moreover, the energy savings of the system which employ ground source heat pump (GSHP), and the hybrid systems of Fuel Cell (FC) – GSHP and Photovoltaic thermal (PVT) – GSHP were also investigated and analyzed. The simple sum of residential and commercial building conventional case was considered as the reference case. The systems' performance was simulated over one full year in TRNSYS software environment under typical weather conditions for Ottawa, Canada and Incheon, Republic of Korea. Reference and advanced load sharing cases were simulated and analyzed in terms of thermal/cooling load satisfied and operational and capital energy savings. These cases were systematically summarized into seven cases, as following: Case 1 is utilizing conventional set up – boiler and chiller to meet heating and cooling demands of a single detached house. Case 2 is utilizing the same conventional set up as Case 1 – boiler and chiller to meet heating and cooling demands of an office building that has the same layout as the house in Case 1. Case 3 is a simple summation of Case 1 and Case 2 systems and loads. Case 4 is a load sharing setup featuring common boiler and chiller used to meet the combined loads of the house and office. Case 5 is a load sharing case utilizing a ground source heat pump (GSHP) to meet the combined loads of the house and office. Case 6 is a load sharing case where a hybrid PEM fuel cell (FC)/ground source heat pump (GSHP) system is used to meet the combined loads of the house and office. Case 7 is a load sharing case where a hybrid Photovoltaic Thermal (PVT)/ground source heat pump (GSHP) system is used to meet the combined loads of the house and office. The simulation results show that by using the same equipment with different capacities the load sharing system can achieve operational energy saving of 3–4 % in comparison to the reference system. Furthermore, Case 5, Case 6, and Case 7 were able to save energy up to 40%, 25%, and 57% in comparison to the case 3 respectively.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".