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[90] Land use type allocation informed by urban energy performance: A use case for a semantic-web approach to master planning: A use case for a semantic - Web approa ch to master planning

Seed Paper Energy Estimation & Digital Twins 2021

Paper Description extracted from PDF · abstract · 136 words

Cities are growing fast and facing unprecedented challenges as urban populations grow and resources are becoming scarce. A city’s master planning involves a series of decision-making processes and requires knowledge from various domains. Urban planners are seeking computational support. We present a use case of land use type or building function allocations informed by urban energy performance as a pilot demonstrator for a semantic-web approach to these challenges. The software used for energy performance assessment was the City Energy Analyst. Using a quarter in downtown Singapore as an example, the results indicated 70% to 80% residential supplemented by other land use types favours efficient use of district cooling systems and photovoltaic panels. Urban planners may use the results to narrow down the search space of land use type ratios for the selected mixed-use area in Singapore.

AuthorsZhongming Shi and Pieter Herthogs and Shiying Li and Arkadiusz Chadzynski and Mei Qilim and Aurel von Richthofen and Stephen Cairns and Markus Kraft
Year2021
VenueProjections - Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021
DOI10.52842/CONF.CAADRIA.2021.2.679
Source DatabaseSeed (CumInCAD)
Bridge-to-GNNCategory Seed
GNN ArchitectureKnowledge Graph Embedding (inferred from title)
Graph EncodingNot yet extracted from PDF
AEC TaskEnergy Estimation (inferred from title)
CohortEarly Infrastructure (2020–2021)

Extracted Methodology heuristic — verify before citing

Primary MetricAccuracy (heuristic — verify)
Key Findinguse case of land use type or building function allocations informed by urban energy performance as a pilot demonstrator for a semantic-web approach to these challenges (heuristic — verify)
Quality Assessment1/6 · rigor: Low ·
Code/Data: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Quality (heuristic, 6-flag)0/6 · rigor: Low
Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: —

Lineage Families

bim-gis-integration (auto-suggested) energy-digital-twin (auto-suggested)

Enabled By / Precursors

None tracked

Extended By

None in corpus

Infrastructure Dependencies

None

Research Frontier Tags

energy-estimation

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026