A Data-Driven Simulation-Based Case Study of The Green Village’s Hybrid Energy Hub
Bibliographic record
Abstract
This paper presents a simulation-based case study of a hybrid energy hub located at The Green Village (TGV), a living lab for sustainable innovations in Delft, The Netherlands. The energy hub integrates photovoltaic (PV) generation, battery storage, hydrogen production, seasonal storage, and usage to provide a fully electrified one-person residence, serving as a realistic testbed for the integration of renewable energy. A model of the hub is developed in Simulink/Matlab using historical operational data to simulate system behaviour under various edge case scenarios and system configurations. The model enables the evaluation of system-level interactions, operational strategies, and the impact of design choices on energy efficiency, self-sufficiency, and hydrogen integration. The simulation results show the sensitivity of the system performance to component sizing and EMS settings. This study provides valuable insights into the control and optimisation of The Green Village’s energy hub and its integrated energy systems, contributing to the practical deployment of resilient and sustainable energy hubs in the built environment.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".