Drones and community-powered rapid neighborhood energy modeling: Demonstrated in a real-world case study
Bibliographic record
Abstract
• Introduced RENSA, a rapid energy modeling of neighborhoods through split automation • RENSA enables scalable and high-precision neighborhood energy modeling • Combines drone imagery, LiDAR, and ML with community-sourced data • Community-engaged workflow enhances model realism and access to energy planning • Provides a replicable framework for sustainable and net-zero urban future Accurate evaluation of energy performance in neighborhood energy modeling (NEM) has long been challenged by the lack of representative ground-truth data and high-resolution 3D geometry. Traditional approaches often rely on simplified shoebox archetypes, semantic 3D city models, and standardized assumptions for occupancy, internal loads, and envelope characteristics, limiting model fidelity. To overcome these constraints, this study presents a holistic approach to NEM by introducing Rapid Energy modeling of Neighborhoods through Split Automation (RENSA) – a novel, semi-automated workflow that integrates community engagement, multi-source data fusion, and machine learning to generate various Level of Detail (LoD) building models from LoD0–LoD3 and enable high-precision, context-sensitive NEM simulations. RENSA is applied to 297 structures in Georgetown, Prince Edward Island (PE), Canada, producing LoD3 models for the entire community and conducting detailed energy simulations for 71 residential buildings with available utility data. A structured, JavaScript Object Notation (JSON)-based data pipeline automates simulation inputs gathered through community engagement. Buildings are classified into five energy system categories, and model outputs are validated against real utility records. Geometric validation shows mean absolute percentage errors of 4.27% for footprint area (LoD0), 3.55% for peak height (LoD1), 7.48% for bottom chord height (LoD2), 6.80% for volume (LoD2), and 12.88% for fenestration areas (LoD3). Further, integrating community-led data into the calibration workflow allowed 65% of the simulated buildings to meet the ASHRAE Normalized Mean Bias Error (NMBE) requirement of ±5%. The RENSA generalized framework demonstrates a replicable, scalable, and community-driven approach to NEM, enabling user-defined LoD generation and effectively bridging the gap between theory and real-world application in support of net-zero energy transitions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".