Sustainability evaluation of seasonal snow storage for building cooling systems : a life cycle approach
Bibliographic record
Abstract
In Canada, the residential building sector consumes 17% of the total energy and contributes 15% of the total GHG emissions. Predominantly, the energy demand for cooling in the residential sector is increasing due to large occupancy floor area and high usage of air-conditioning. Minimizing energy use and GHG emissions is one of the highest priority goals set for national energy management strategies in developed countries including Canada. In this research, a sustainability assessment framework is developed to evaluate the techno-economic and environmental performance of different building cooling systems, namely conventional snow storage system, watertight snow storage system, high-density snow storage system, and the conventional chiller cooling system. The framework is implemented in a low-rise residential building in Kelowna (BC, Canada) to appraise its practicality. The Life cycle assessment (LCA) approach is used to assess the environmental impacts of different building cooling systems. LCA results revealed that the systems have varying energy requirements and associated environmental impacts during the different life cycle phases (extraction and construction, utilization, and end of life). The annual cooling energy demands for different cooling systems are also estimated. The LCA is carried out using SimaPro 8.1 software and the TRACI 2.1 method. Multi-criteria decision analysis is employed using the ‘Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II)’ to evaluate the sustainability of different cooling systems over their life cycle. The results showed that the snow storage systems tend to reduce the greenhouse gas emissions and associated environmental impacts more than the conventional cooling system. A probabilistic feasibility evaluation tool is developed to evaluate the techno-economic performance of different cooling systems. The incremental economic performance of alternatives is estimated in terms of the total cooling cost per kWh at the facility. Monte-Carlo simulation was performed to consider the uncertainty factors involved in the techno-economic parameters of cooling systems. Results of this analysis verified that the snow storage systems are more energy efficient and low-cost options for building cooling systems. The developed frameworks will support decision-makers in evaluating the sustainability of building cooling systems. Moreover, socio-economic benefits, i.e. improving affordability, equity, and enhancing energy sustainability, could be achieved.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".