A holistic approach to model electricity loads in cities
Bibliographic record
Abstract
Abstract Time-resolved, occupancy-dependent electricity load profiles at building level for city quarters or entire cities are important for planning authorities, project developers, utilities or other stakeholders in order to develop energy saving strategies and meet climate targets. Firstly, this information enables a more accurate modelling of renewable energy systems. Secondly, aspects like sector coupling, storage decisions and the impact of technologies such as electric vehicles or heat pumps on the grid can be considered. Thirdly, it allows a more detailed economic analysis. This paper contains the newly added features to the simulation environment SimStadt, which is used for strategic modelling of sustainable urban or regional areas with a spatial resolution at the building level. SimStadt interlinks 3D CityGML models with parameters for buildings physics to simulate energy demands and renewable energy potential. It was enhanced by the development of an electricity load profile generator with variable resolution and the addition of an hourly resolved PV potential analysis including a variable economic analysis. This enables e.g. the evaluation of photovoltaic potential with the associated investment, operating and levelized costs over the lifetime of hundreds of individual buildings in parallel. Together with additional electric building demand from heat pumps, electric vehicles or load shifting options through the use of battery storage, it will be possible to assess and compare the feasibility, benefits and economic viability of energy/electricity-related urban renewal measures in even greater detail and with a holistic perspective. The simulation platform enables the development of granular sustainable urban (sub)strategies and energy concepts through a holistic, time-resolved, building-specific approach to support transformation of the building stock to a sustainable, low-carbon one.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".