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Public-source grid intelligence

GridPath

Solo prototype2026

GridPath is the operator-altitude half of a paired public-data exploration with Quiet Hours. It started with a systems question: what happens when everyone connected to the electric grid is working from a different partial truth? Consumers see bills, outages, and conservation requests; operators see load, forecast error, and reliability signals; planners see capacity and interconnection constraints; communities feel the consequences. I designed GridPath as a shared-context experiment: a way to layer public grid, weather, fire, disaster, queue, emissions, and community signals into a more legible intelligence surface.

Role
Self-directed product designer and builder of a public-data systems prototype.
Builder signal
Turned grid, weather, fire, disaster, queue, emissions, and community datasets into map, dashboard, project, insight, and reporting surfaces.
Legibility signal
Used the build to learn grid infrastructure by making the system inspectable, not by flattening it into a single dashboard.
Why it belongs
Shows systems thinking, technical curiosity, source-aware data synthesis, and the ability to turn fragmented public information into shared context.
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Grid command surface
Dashboard, map, and queue surfaces
Grid command surfaceDashboard, map, and queue surfaces
Case study path

What this case study covers

  1. 01

    How the dashboard frames a complicated public infrastructure system without pretending one view contains the whole truth.

  2. 02

    How the map becomes the command surface for regional orientation.

  3. 03

    How queue and project-detail pages turn public sources into inspectable infrastructure evidence.

  4. 04

    How insights and reports support inquiry while keeping source traceability and uncertainty visible.

Region map
Dashboard, selected grid region, and plant queue
Region mapDashboard, selected grid region, and plant queue
Map command surface

The region view makes the data spatial and inspectable.

In early layouts I led with the dashboard, then kept noticing that the first thing I did on my own prototype was hunt for my region anyway. So the map became the command surface — the one full-screen view every other tab answers back to — and the dashboard learned to compose itself around the region you picked there. A grid statistic is meaningless until you can orient in space: where before how much.

Dashboard
Grid signals as an operating surface
DashboardGrid signals as an operating surface
Dashboard layer

The dashboard turns partial truths into shared context.

The dashboard is where map context becomes decision context: anomalies, comparisons, and the public source behind every number. Wiring up the feeds was only the start. I still had to decide which signals had earned a place on the same screen. Grid load, interconnection queue, weather, outages, emissions, transmission, and community context come from sources that were never designed to be read together. I treated 'can these honestly sit side by side?' as a design question, because the value was not more data. It was helping a user see how incomplete signals might relate.

Planning intelligence
Projects, scenarios, insights, and reports
Planning intelligenceProjects, scenarios, insights, and reports
Planning intelligence

The planning surfaces support inquiry, not false certainty.

Projects, scenarios, insights, and reports are where the prototype shifts from orientation into decision support. The design challenge is keeping the user aware of what kind of evidence they are looking at: a public feed, a modeled read, a comparison, or a report-ready summary. Each surface has to preserve source confidence while still helping someone decide what to inspect next: why this may be happening, who may be affected, and which signal deserves a closer look.

Evidence path
Grid planning and reporting surfaces
Evidence pathGrid planning and reporting surfaces
What it taught me

Complex systems need shared context before action.

GridPath is the operator-altitude version of a question I tested with Quiet Hours: what has to be true before public infrastructure data can become useful guidance? The useful answer was orientation, source visibility, comparison, and a clear relationship between the map, the dashboard, and the next decision. Quiet Hours asks the same question one floor down, in a household, and runs on GridPath's API. Together, they made the energy story sharper for me: Quiet Hours asks what a person should do when the grid is stressed; GridPath asks how we know the grid is stressed in the first place.

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