ALLSHIFT/docs/01-project/ai-efficiency-improvements.md
pepe 72dd781dbc Organize documentation into docs/ and superseded/
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markdown reports, and split current documentation from outdated material.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superseded/ — outdated material with a documented reason per entry: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>
2026-07-25 21:20:33 -07:00

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# AI improvements
# **Reducing Electrolyzer Losses (biggest loss: 3040%)**
1. Using electrolyzer only when efficiency is highest (at ~3060% load for PEM, ~70100% for alkaline)
* Avoiding operation at low part-load where efficiency drops
* Scheduling hydrogen production during periods of high PV surplus + good cell efficiency
1. Preheat/stabilize electrolyzer temps
* Predict when PV will be high
* Warm up the electrolyzer in advance
* Avoid frequent shutdown/start cycles
1. Shift hydrogen production to midday
Midday sunlight has:
* Higher irradiance
* Lower PV conversion losses
* More stable power
# **Reducing Fuel Cell Losses (40%)**
### 1\. Run fuel cell in its optimal operating window
* Smooth transitions
* Avoiding rapid up/down ramps
* Running fuel cell at stable, moderate load
# **Reduce PV-related losses**
* Curtailment
* Mismatch between PV peaks and electrolyzer operation
* Power clipping
# Add heat recovery
* Heating water
* Building heating
* Sterilization pre-heat
* Space heating
### Smarter scheduling
* Only run electrolyzer at high-efficiency periods
* Use predictive control for tomorrows solar
* Avoid overproducing hydrogen unnecessarily
### Digital twin tuning
Simulated training avoids real-world risk and gives more optimal strategies.