The Impact of Urban Morphology and Construction Standards on the Energy Consumption of Neighborhoods
Bibliographic record
Abstract
With the growing urban population, energy consumption and the CO2 emission of cities are constantly rising, and thus, it becomes important to address the cities' energy use. Multiple factors are important for urban scale energy consumption, mainly urban design and construction standards. Urban design and choice of morphology design are considered influential factors in urban scale energy consumption. This study tries to understand the impact of urban morphology and construction standards on urban scale heating and cooling demand by evaluating two case studies in two districts in Montreal, Canada. One of the case studies is in downtown Montreal; buildings have different height variations in this case study, while the second case study represents a neighborhood with uniform heights. Both case studies are different in density and surface-to-volume ratio. Previous studies have shown that morphological parameters significantly affect radiation received on external surfaces, affecting energy consumption. On the other side, building compactness, construction, and insulation standards are important factors in reducing energy consumption. To run the energy simulations, a software workflow was developed where the geometry of buildings is modified using Rhino and imported to grasshopper; the energy simulation for the buildings was completed using a combination of plugins of Ladybug tools which uses OpenStudio, EnergyPlus, and Radiance as backend engine. \nTo understand the effect of radiation on cooling and heating loads, two window locations are studied, showing 15% changes in heating loads when the windows are on one side of the buildings and 4% changes in heating loads when windows are located on both sides of the buildings in different orientations. Based on previous studies, two main morphological parameters are selected for a neighborhood to study. This study investigated the influence of orientation and surface-area-to-volume ratio (S/V represents changes in density and simplification in the model) for an as-build and multiple defined scenarios. Besides these morphological parameters, five different construction types using ASHRAE standards are selected, and heating and cooling demand are calculated for all the 330 models. Then the approximate solar potential for the models is calculated considering how much energy in all the models can be produced and covered by photovoltaic (PV) panels on roofs. Results indicate that even not ideal morphological design with very good insulation standards could produce 1.3 times more than the energy needed. In contrast, the best morphological model with a not insulated scenario will cover around 0.6 times the energy loads. The load calculation results show that the morphological parameters change the heating and cooling demand by a maximum of 45%. The results also show that when the insulation properties are modified to meet the statutory requirements of energy codes and even beyond the energy codes, a reduction of almost 80% in heating/cooling demand can be achieved. Thus, the results show that although morphological parameters are important in energy consumption and radiation received on surfaces, for example, if the surface-to-volume ratio changes significantly, the effects of construction standards are far more relevant for urban scale heating and cooling loads. To expand the conclusion, this study can be continued in the future by studying different case studies in different climate zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".