Built form and function as determinants of urban energy performance: An integrated agent-based modeling approach and case study
Bibliographic record
Abstract
The pursuit of urban energy sustainability is driving growing efforts and pledges to achieve low-energy or net-zero energy performance at urban scales. However, studies covering urban energy performance and renewables integration often lack a systematic consideration of the urban built context (form and function) and its resulting effects on energy demand and supply dynamics. This paper presents a holistic and scalable agent-based modeling framework that incorporates contextual factors in the energy demand and supply evaluation of urban areas using multi-dimensional performance metrics. It leverages the local climate zone (LCZ) classification of typical urban built types, which is commonly used in urban planning or transportation applications (as opposed to energy planning). The framework is demonstrated through a comparative case study of compact high-rise and low-rise urban areas with different proportions of lodging and office buildings equipped with rooftop solar photovoltaic (PV) systems. Results show that the average energy self-sufficiency of the compact low-rise area exceeded 25%. In contrast, the self-sufficiency of the compact high-rise area remained below 5% despite achieving a self-consumption ratio of 99% from locally generated solar energy. The uncovered trends are critical to inform context-sensitive urban energy solutions and policies.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".