# GridMind — Technical Project Report

Document ID: MF-2026-05-TPR-01  
Status: local development prototype documented; results not publication-approved  
Institution: MindforgeAI · Chatake Innoworks Private Limited

## Executive summary

GridMind is a bounded power-grid digital-twin and ML management-intelligence
development prototype. It accepts synthetic scenario inputs for demand, renewable
contribution, thermal availability and network stress; local regression models
produce illustrative net-load, available-generation and stability indicators for a
human review screen. It is not connected to live grid data and does not advise or
perform dispatch.

## 1. Problem definition

Primary engineering question: **How can operational signals support earlier, safer and more explainable grid decisions?**

### Required completion evidence

- user and stakeholder definition;
- measurable functional and non-functional requirements;
- explicit scope and exclusions;
- responsible-use and safety boundary.

## 2. Domain research and requirements

Insert reviewed domain sources, competing methods, user workflow, constraints and acceptance criteria. Every source must be cited and retained in the project research register.

## 3. Dataset, preprocessing and features

The current implementation deliberately uses a deterministic synthetic corpus (960
generated examples, fixed random seed) rather than a utility dataset. Features are
scenario period, demand index, renewable index, thermal availability and network
stress. This is adequate for interface and workflow development only; it is not
evidence of operational accuracy. Any field-data phase requires provenance, licence,
schema, privacy/security review, leakage controls and an approved evaluation split.

## 4. System and model architecture

Add a traceable architecture diagram covering inputs, storage, processing, model or rule layer, API, interface, monitoring and human decision points. Record environment versions and reproducible run commands.

Implemented development surface: Python, FastAPI, scikit-learn and a static
HTML/CSS/JavaScript interface. The local API provides health, metadata, default
scenario and scenario-evaluation endpoints. Two `RandomForestRegressor` models are
trained at startup only on the deterministic synthetic corpus.

## 5. Implementation and verification

Verified on 18 August 2026: the local FastAPI health endpoint, public interface,
application route, documentation route and scenario-evaluation endpoint returned
HTTP 200. Scenario assertions confirmed that increased thermal availability and
lower demand/stress produce higher synthetic available-generation and stability
outputs. These are software-behaviour checks, not grid validation.

The supplied student candidate remains a documented CardiOath duplicate. The local
prototype is separate and must not be represented as the candidate repository or as
student-source evidence.

Do not add accuracy, latency, scale, safety or deployment claims until the corresponding test artifact is linked in the evidence matrix.

## 6. Deployment and operations

Reserve evidence for environment configuration, secrets handling, infrastructure, health checks, logs, monitoring, rollback, privacy, accessibility and operating cost. A proposed Android application must remain a proposal until a reproducible build exists.

## 7. Results and discussion

No operational result is reported. The next evaluation table must identify data
source, baseline, task definition, metric, uncertainty, scenario, artifact path and
reviewer. A power-flow, reliability or dispatch claim requires appropriate domain
simulation or utility-approved evidence.

## 8. Limitations and future work

Current limitations: synthetic examples only; no canonical student implementation;
no load-flow engine; no telemetry; no forecast benchmark; no safety or deployment
approval. Human review remains essential for every future decision context.

Capstone continuation remains open after repository reconciliation, reproducibility review and programme approval.

## 9. Conclusion

The local prototype establishes a reviewable product direction while retaining the
source and validation gates required for capstone continuation.

## References and appendices

Use a consistent citation style. Append dataset cards, model cards, API specification, test report, screenshots, diagram sources and change log.