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gridwatch-ai

GridWatch AI is an explainable AI platform for electricity-demand forecasting, anomaly detection, load-pattern clustering, and grid-risk assessment. It combines LSTM, XGBoost, Isolation Forest, K-Means, SHAP, and RAG-based AI assistance with a FastAPI backend and React-TypeScript frontend.

Details

External ID
1394708713
Source
GITHUB
Company
—
Product
gridwatch-ai
Website domain
github.com
Launched
Sept. 29, 2026
Cohort
—
Upvotes
20
Upvotes percentile
0.6029976940814757
Tags
—
Fetched at
Sept. 30, 2026, 1:02 a.m.
Updated at
Sept. 30, 2026, 1:02 a.m.

Enrichment

Theme
specialized AI models and agent reasoning tools
Vertical
Energy & climate
Function
Analytics & BI
Audience
B2B
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
ai platform for electricity demand forecasting and grid risk assessment
Manually corrected
False

Could you build this?

Partial The dashboard and basic ML wrappers (FastAPI, scikit-learn, XGBoost, SHAP) are straightforward, but real-world electrical grid risk assessment requires complex power systems domain modeling and real utility data pipelines.

What it would actually take: The architecture combines a FastAPI backend running time-series forecasters (LSTM, Prophet, XGBoost) and explainability libraries (SHAP) connected to a React frontend. The difficult part is sourcing realistic or live SCADA grid telemetry data, engineering domain-specific weather/holiday features, and calibrating risk thresholds to actual electrical distribution constraints.

Competitors

Other products that read as similar to this one — 91 launches clear the similarity bar, closest 8 shown.

Attention rank: #33 of 92 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 332 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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