Multi-energy systems simulator for hourly management and optimization of GHG emissions and fuel costs
Bibliographic record
Abstract
Many legacy infrastructures are reaching the end of their service life. The necessary replacement of these infrastructures creates an opportunity to replace them with environmentally friendly and innovative systems. The steam plant at the University of British Columbia is one of those cases requiring replacement due to aging. The steam generation boilers are, on average, 53 years old and have short expected remaining service. The boilers process is fed by natural gas as main fuel. It was identified that almost 80% of the CO₂e emissions on campus are produced from the use of gas for heating purposes. UBC is worldwide recognized for being one of the most sustainable university campuses, and the first university in Canada awarded a gold rating in sustainability. UBC’s GHG emissions targets for Kyoto protocol were reached in 2007; at that point, new aggressive reduction targets were established, aiming for 33% by 2015, 67% by 2020 and 100% by 2050. These reductions are expressed in tonnes of CO₂e. The situation described offers an opportunity to explore alternatives for the Steam Plant potential replacements. The Infrastructures Interdependencies Simulator (I2Sim) was selected as simulation platform for this study. The simulator allows real-time resource management using hourly historical operational data. To meet the campus thermal requirements, the system considers biomass cogeneration, heat pump, and excess electricity to offset traditional natural gas fuel sources. All technologies take advantage of real-time management of fuels allocation to reduce GHG emissions. A parallel distribution system based on hot-water is modeled, because of the potential in increasing the overall heating system performance. Four modeling scenarios are constructed, showing that fuel costs can be reduced by 51%, GHG emissions reduced by 76% and overall energy consumption reduced by 29%. The simulator is a first step in integrating all critical infrastructures into a Smart Energy MicroGrid paradigm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".