Automatic energy demand and system simulation at district level
Bibliographic record
Abstract
Abstract The importance of climate protection and sustainability is steadily increasing all over the world. However, there is a large potential for reducing emissions in the heating demand reduction and renewable heat supply of buildings that needs to be addressed. Therefore, a method was developed within the scope of this work that allows local decision-makers such as energy supply companies, project developers and the public sector to calculate, evaluate and compare different scenarios to make buildings and city districts more sustainable based on few and widely available input data. It includes both the determination of the heat demand and measures for its reduction as well as the selection and simulation of centralised and decentralised supply systems. A combination of different methods from the fields of geoinformatics, heuristic decision-making and object-oriented modelling is used. The latter forms a focal point in the work with the development of a data model for energy system components to enable automatic simulation. The applicability as well as the transferability of the method is shown in several case studies. Based on the simulations results, which can be related to CO2emissions as well as costs, recommendations for the implementation of measures can be given and implemented. The paper is a summary of the dissertation with the title “Automatische Simulation von Wärmebedarf und -versorgung auf Quartiersebene” by the first author at Karlsruhe Institute for Technology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".