GridForge
Projects
Student contributions organised by cohort year. Each year's work builds on the last.
Three groups running in parallel. Two are implementing and extending the platform; one is a masters research group exploring new foundational methods for topology comparison and evaluation.
Digital Twin Infrastructure
[4 members — names to be added]
Implementing established research methods for topology estimation and phase grouping within the GridForge platform. Alongside the research integration, this group is upgrading the platform's infrastructure: migrating to a locally-hosted deployment model and introducing database-backed storage for analysis results and network data.
AI Topology Evaluation
[Student Name]
Developing a machine learning approach to evaluate the quality of topology estimates produced by different estimation methods, using AMI data as the signal. When multiple topology estimation methods are in use, this work provides a principled way to assess which estimated topology most likely reflects the true network structure — without requiring ground-truth labels for training.
Topology Comparison & Metrics
[5 masters students — names to be added]
New research into how topology estimates should be compared and evaluated. The central question: what makes an estimated topology "close to" or "far from" the ground truth? The group is developing formal similarity metrics and exploring how those metrics can guide inference — enabling simpler, more computationally tractable methods that still produce high-quality topology estimates.
The first GridForge cohort. One student focused on pilot-building the platform from scratch: establishing the Streamlit-based GUI, connecting Python analysis components, and writing backend code for synthetic data generation and simulation.
GridForge Prototyping
[Student Name]
Built the initial GridForge platform from the ground up: a Streamlit-based frontend wiring together a set of Python analysis components, a backend for generating synthetic LV network data, and simulation infrastructure using OpenDSS. Established the project's architecture and development patterns for future cohorts to build on.