Development of a simulation tool to assess heat pump integration in Canadian homes
Bibliographic record
Abstract
Air-to-air heat pumps (ASHPs) can play an important role in reducing the greenhouse gas (GHG) emissions of Canadian homes, but their performance is highly dependent on sizing, integration, ASHP type, and building location and construction. Currently, the Heating Seasonal Performance Factor (HSPF2) is used to provide homeowners with an estimate of seasonal performance, but neglects many of the key factors above. More accurate estimations of ASHP performance for a given project are needed to provide homeowners with an improved understanding of performance potential for their unique context.Simulation tools can more accurately assess ASHP performance in a specific installation, taking into account variations in climate, building characteristics, and system configurations. However, many commonly used building simulation tools (e.g., TRNSYS, EnergyPlus) require significant effort and expertise to operate. This complexity can be a barrier, including homeowners, contractors, and even some engineers, resulting in a non-optimal heat pump sizing and selection. While simpler tools exist (RETScreen, HOT2000), they still require minimum modelling knowledge. Additionally, these tools often use monthly average data to estimate the performance of ASHP, which tends to poorly capture the impact of sizing and part load performance. To drive further system adoption, new tools are needed that can accurately estimate ASHP performance while remaining accessible to users with limited technical knowledge.This paper presents the development of a novel simulation tool designed to simplify the selection and performance analysis of ASHPs in single-family Canadian homes. The tool leverages the capabilities of TRNSYS and enhances usability through an intuitive interface. It employs Type 660 models, which use a resistance-capacitance approach to building modelling, to provide a more computationally efficient approach vs. detailed TRNSYS building models (e.g, Type 56). To ensure accuracy, these simplified models were validated against detailed Type 56 models by comparing their energy consumption and zone temperatures at hourly and annual scales. Using Type 660 allows for faster simulation while also allowing users to easily modify the building geometry and envelope. This provides a basis to simulate a wide range of homes with minimal adjustments to key inputs.The tool outputs detailed results on annual building energy consumption and evaluates various heat pump capacities, providing information on the savings potential of properly sized ASHP. Several case studies are presented to compare the performance of the developed tool with detailed TRNSYS simulations, and showcase its potential as a decision support for Canadian homeowners and interested stakeholders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".