Monitoring and Evaluation of Nearly-Zero Energy House (NZEH) with Hybrid HVAC for Cold Climate – Canada
Bibliographic record
Abstract
Abstract A Nearly Zero Energy Home (NZEH) in Strathroy, Ontario, Canada was monitored and studied to evaluate its performance for both heating and cooling seasons. The house is a new built and is equipped with an electric-natural gas hybrid spacing heating system. A high efficiency natural gas furnace and an electric air source heat pump (ASHP) were coupled to meet the space heating demand of the house. The house also benefits from on-site renewable energy generation (solar PV). The original system was controlled by a simple switch over thermostat that drives furnace or ASHP based on the outdoor temperature as a single decision-making factor. The system then was upgraded with a cloud based Smart Dual Fuel Switching System (SDFSS) controller that considers time-of-use (TOU) pricing, fuel cost, weather forecast, and equipment efficiencies and capacities. This multi-variable decision-making process defines an optimal schedule for the hybrid system to run more efficiently and more economically. A detailed monitoring system, including sensors, meters, and data acquisition system, was installed to collect all required information at a 2-minute interval. The furnace and ASHP were studied separately to verify their capacities and efficiencies. Then the overall hybrid system and its controller were monitored to identify its effectiveness. A complete model of the house and the hybrid system were developed and validated with experimental data. Thereafter, the system was run by the SDFSS controller. All of these scenarios were compared against each other and benchmarked. In addition, the factors affect indoor air quality (IAQ) were studied in detail. The preliminary result has shown that SDFSS controller provides a cost effective, feasible, cleaner and healthier IAQ options to run the hybrid system in a NZEH.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".