Data sources
The platform uses outage feeds, weather forecasts, vegetation observations, critical asset data, crew context, and GIS layers.
Methodology
How GeoGridIQ turns operational data into outage intelligence.
GeoGridIQ combines GIS data, environmental signals, prediction models, and validation controls to support utility operations.
outage prediction methodology
utility data sources
prediction confidence
Prediction process
Features are built for locations and forecast windows, then evaluated by trusted XGBoost models or deterministic fallback scoring.
Confidence scoring
Confidence considers data freshness, missing signals, model validation, probability calibration, and reactive versus predictive timing.
Validation framework
Prediction audit views track coverage, lead time, false positives, false negatives, timing, and model trust metadata.
Frequently asked questions
Frequently asked questions for operators and planners.
What makes GeoGridIQ different?
GeoGridIQ combines GIS, outage prediction, model validation, critical assets, weather, vegetation, and crew readiness in one operational workflow.
How does GeoGridIQ handle low model confidence?
It falls back to rules-based decision support when model trust, data quality, or confidence safeguards do not pass.
Related GeoGridIQ resources
Continue exploring utility intelligence.
Documentation
Read GeoGridIQ documentation for platform overview, data sources, prediction engine, GIS engine, weather intelligence, NDVI, and crew optimization.
Open Data Reports
Public utility intelligence reports covering Quebec outage risk, vegetation threats, storm impact, and critical infrastructure exposure.
Outage Prediction Platform
Explore how GeoGridIQ combines weather signals, vegetation risk, historical outages, and explainable prediction models to identify areas at higher outage risk.
Vegetation Risk Analysis
GeoGridIQ uses NDVI, satellite imagery, vegetation density, and infrastructure context to help identify vegetation pressure near electrical assets.