CityEnergy Suite: A Holistic Approach to Modeling Occupant Behavior, Electric Vehicle Charging, and Demand Response in Urban Environments
Bibliographic record
Abstract
Quebec's ambition to achieve net-zero emissions by 2050 poses significant challenges in decarbonizing the building and transportation sectors while managing increasing electricity demand. A major obstacle is handling the temporal dynamics of electricity usage, especially unpredictable peak periods influenced by weather, economic activities, and consumer behavior. Efficient demand-side management (DSM) strategies, such as demand response (DR) and Vehicle to Grid (V2G), are essential to shift and flatten peak demand, ensuring a more sustainable and economically viable energy transition. This thesis introduces the CityEnergy Suite, a comprehensive modeling framework for simulating residential energy-related behavior, electric vehicle (EV) charging, and DR strategies. Leveraging open-access datasets like Census data and energy surveys, the model generates high-resolution load profiles. The CityEnergy Suite employs a modular, agent-based approach to assess the aggregate impact of occupant behavior on the electrical grid and explore future energy scenarios. It comprises three key components: CityAgent, CityLoad, and CityCharge. CityAgent creates a detailed synthetic population model tailored to the Montreal region, incorporating diverse household compositions and socio-economic characteristics. CityLoad produces stochastic energy load profiles that consider household attributes and appliance usage. CityCharge models urban EV charging demand, enabling analysis of different charging behaviors and penetration scenarios on the grid. The findings provide crucial insights for developing decarbonization and DSM strategies, highlighting the potential of engaging small customers to support grid stability through load shifting, peak shaving, and emerging V2G technologies. The CityEnergy Suite offers a robust framework for designing inclusive and effective energy policies, contributing significantly to Quebec's decarbonization efforts.
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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 | 0.000 | 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".