Visualizing and Analyzing Urban Energy Consumption: A Critical Review and Case Study
Bibliographic record
Abstract
Sustainability of urban energy systems is among the main concerns of planners. Improving energy efficiency is a major sustainability issue considering the population growth, limited energy resources and global climate change. Energy mapping enables decision makers and planners to visualize and evaluate the spatial patterns of energy consumption and to analyze future scenarios for improving energy performance (EP), such as renovation alternatives. The main input for energy mapping is the measured or estimated energy consumption of each building in a specific area. Energy meters are the preferable source to provide these data in an accurate and simple way. However, getting access to these data from utility companies in a disaggregated format is usually difficult, mainly because of privacy concerns. Therefore, it is often necessary to estimate the energy consumption of buildings using simulation combined with aggregated metering data. This paper will review recent approaches to develop energy mapping at the urban scale, as well as simplified energy simulation tools and computer modeling tools and formats including geographical information systems (GIS), CityGML and building information modelling (BIM). A case study, focusing on the energy map of Concordia University SGW Campus, is provided to demonstrate some of the reviewed approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".