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Audit every document in the repository, convert the non-markdown ones into markdown reports, and split current documentation from outdated material. docs/ — 31 markdown documents in seven numbered sections. Twenty are new reports generated from .docx / .pdf / .xlsx / .mlx / .m sources that were previously unreadable in the browser and undiffable in git. Each report carries a provenance block (source path, format, MD5) and links back to its original; all 13 recorded checksums verify against the files on disk. Machine-extraction losses (PDF table column interleaving, Word OMML equations, embedded figures) are called out explicitly rather than silently smoothed over. superseded/ — outdated material with a documented reason per entry: two byte-identical ClickUp re-exports, an older revision of the BIDMC/UCSD energy-flow doc (the retained copy adds the SoC Violation Rate KPI), a duplicate of Shift input data.docx, the May 2026 simulation plan, the root PV+Battery.md now covered by a fuller report, GitHub's stock demo-repository template, and a zero-byte placeholder. Its README also records what was deliberately NOT retired and why — the "Old Frameworks" and "Old Simulations" folders hold unique Simulink revisions, and "Big Ugly Folder" holds the only copy of framework revision 1.3. Findings worth flagging, all documented in the reports: - Simulink lineage recovered from each .slx's internal coreProperties.xml revision counter. The current model is Current Framework/Bobert0206_Initial_Simulation_Framework.slx (rev 2.7); the top-level copy is rev 1.3, five revisions behind. - Simulations/Constants.m is a truncated byte-prefix of the Current Framework copy, silently missing H2_leak, H2_cap and E_H2_vol_h. - The PEM electrolyser and fuel cell are unmodified MathWorks Simscape examples still at vendor defaults; the "10x bigger" sizing TODO recorded in Constants.m was never carried out. - controller-claude.m does not compile — undefined P_Electro_max, outputs unassigned on several paths. - The specification set uses two incompatible variable naming conventions and disagrees on action-space size (5 vs 16). - MA_hourly_load.csv (13.7 MB) is the same 35,040 rows as 89993-0.parquet (2.4 MB). - Clinical data is the MIMIC-IV *demo* (ODbL, 100 patients), not full MIMIC-IV — redistributable, but the licence and citation are unrecorded. Housekeeping: untrack 21 Simulink build artefacts (slprj/, *.slxc) and add ignore rules for them. Root README rewritten around the new layout. Recruitment notes naming individual candidates are excluded from version control via .gitignore rather than committed; the generic question template is kept in docs/07-team-and-operations/. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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WP1: Literature Review & System Design: https://www.sciencedirect.com/science/article/pii/S0360319924031628
- Define system architecture
- Define Research questions
- Extract and Analyse Data
- Report findings
- Research gap: Limited AI research in a real-world hydrogen system, and more case studies are required for realistic implementations https://www.sciencedirect.com/science/article/pii/S1364032125007944 WP2: Forecasting Models
- Use pvlib to generate PV features (irradiance, temperature, angles).
- Train LSTM or Transformer models in PyTorch Lightning for both PV and hospital demand forecasting.
- Evaluate against scikit-learn baselines. https://www.youtube.com/watch?v=SIEaLBXr0rk WP3: Simulation Environment
- Implement a simulation framework (e.g., Python/Simulink/GridLAB-D).
- Model physical components (efficiency curves, storage losses, demand profiles). WP4: RL Control Development
- Build hospital microgrid environment in Gymnasium or SimPy.
- Train RL agents (PPO or SAC) using Stable-Baselines3.
- Integrate forecasting models from the ML stage to provide predictive states.
- Benchmark RL vs. rule-based control (as in H2B2–PowiDian). WP5: Multi-Objective Evaluation
- Evaluate system performance across multiple KPIs: cost, emissions, renewable fraction, hydrogen efficiency, crop-supporting reliability.
- Conduct sensitivity analysis on different grid pricing and weather conditions. WP6: Innovation Extensions
- Integrate a basic crop growth/CO₂ enrichment module to test closed-loop optimization.
- Simulate greenhouse-to-grid interactions (selling surplus electricity/hydrogen).
- Implement explainability metrics for RL decisions.
- Conduct scalability tests for multiple greenhouses connected as a distributed resource. WP7: Results & Reporting
- Summarize findings in terms of innovation compared to existing hydrogen optimizers.