Modelling and Energy Management Control Study for a Net Zero Energy Home
Bibliographic record
Abstract
This study analysis an energy model developed for a net zero energy (NZE) home based on the building characteristics of the Archetype Sustainable House (ASH), located in Toronto (Ontario, Canada). The model presents a complete energy consumption profile which includes space heating/cooling, domestic hot water heating (DHWH), appliance/lighting, mechanical ventilation and electric-vehicle (EV) battery charging. The house heating and cooling demand was evaluated against actual heating and cooling load targets and the energy consumption over a full year was determined to have been modelled with 90.73% accuracy. Energy production was then introduced in the model and the distribution of energy consumption and generation over summer and winter solstice days were compared with a similar study. The results indicated that the net energy consumption and production deviation was approximately -18% for the summer season and 18% for the winter season. Lastly, a control study was developed to evaluate the energy performance achieved by implementing a rule-based (RB) control strategy, with traditional optimization, based on peak-load shedding, GHG emissions and TOU energy cost (EC). Battery utilization (BU) was analogised with control strategies performance and a correlation was identified. The results obtained from the altered energy consumption profiles showed a 79.8% and 21.5% improvement in peak load shedding, 2.35% and 7.63% increase in GHG emissions and 8.35% and 61.14% reduction in EC for the summer and winter seasons 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".