Predictive and transactive controls for EVE park net-zero community with AI/ML models
Bibliographic record
Abstract
A net-zero community is a development that balances the amount of energy consumed with the amount of energy generated on-site, resulting in a net-zero energy consumption. This typically involves integrating renewable energy sources, such as solar panels and/or wind turbines, along with energy-efficient building designs and technologies. The Government of Canada aims at net-zero emission by 2050. In alliance with this goal, EVE Park, the first community of its kind; a net-zero energy community is being developed in west London, Ontario. Currently the functional part of the community’s energy demand is coming from solar PV. This research work focuses on developing the AI/ML models for hourly, weekly and monthly forecasts based on the community generation and consumption data for predictive and transactive controls for the whole community. Deep learning models like RNN, LSTM and GRU are developed for this purpose. The goal is to minimize GHG emission, curb peak demand during peak hours, and reduce electricity costs. In future investigation wind energy and battery storage will be added with solar PV for further optimization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".