GridWatch: An Intelligent Early Warning System for Predicting Urban Power Outage Risks Using Machine Learning

GridWatch is an open-source project based on the Random Forest model, focusing on predicting power outage risks in ten cities in Virginia, USA. The system integrates the OpenMeteo weather API to obtain key meteorological data and implements interactive visualization via Leaflet maps. It provides data-driven early warning solutions for power companies, urban managers, and emergency departments, helping to enhance the resilience of power infrastructure.

The stability of power systems is the cornerstone of modern society's operation. However, frequent extreme weather events, aging power grids, and fluctuations in electricity demand make power outage risk prediction a key challenge. Traditional outage responses are mostly post-event remedies, but GridWatch introduces machine learning to shift from passive response to active prevention. Focusing on ten cities in Virginia, the project analyzes the correlation between historical outage data and meteorological conditions, providing prediction capabilities for planners and power companies to support advance deployment of resources, optimization of maintenance routes, and resident warnings.

GridWatch uses Random Forest as its core algorithm due to its strong interpretability (facilitating understanding of how factors like wind speed and temperature affect outages) and good anti-overfitting ability. On the data side, it integrates free meteorological data from the OpenMeteo API, including key variables such as temperature extremes, wind speed and direction, precipitation and humidity, and barometric pressure changes. OpenMeteo's high-resolution forecasts and key-free interface lower the threshold for project deployment.

The project uses Leaflet.js to build an interactive map interface; Leaflet.js is a mobile-friendly open-source library. The map displays content including: real-time risk ratings for each city (color-coded: green for low risk, yellow for medium risk, red for high risk), heatmaps of historical outage events, and the confidence intervals and uncertainty ranges of the prediction model. This visualization helps users quickly identify risk-concentrated areas and understand the geographical spread patterns of outage risks, such as predicting the future risk status of downstream cities when a storm moves.

GridWatch emphasizes continuous learning capabilities; the model is retrained regularly to adapt to dynamic changes: 1. Seasonal adaptation (differences in load patterns between summer and winter); 2. Infrastructure changes (adjusting the weight of historical data when lines are upgraded or backup power sources are added); 3. Extreme event learning (such as rare events like once-in-a-century ice storms). The project uses sliding window or incremental learning strategies to retain old knowledge while absorbing new information, avoiding catastrophic forgetting.

The prediction outputs of GridWatch generate value at multiple levels: Power companies can optimize the pre-deployment of maintenance teams, postpone non-urgent maintenance, and warn key customers; Urban managers can coordinate backup plans for traffic lights, prepare emergency shelters, and deploy mobile base stations and charging facilities; Ordinary residents can use community apps or SMS alerts to prepare supplies in advance, charge devices, and adjust work schedules.

Limitations: The model focuses on ten cities in Virginia; when extrapolated to other regions, it may face migration challenges due to differences in infrastructure aging levels, vegetation density, and building codes. Future directions include: Integrating satellite imagery to monitor vegetation growth and snow cover; Introducing graph neural networks to model power grid topology for understanding fault cascade propagation; Developing a federated learning version to enable cross-regional privacy-preserving collaborative training.

GridWatch is a practical application of machine learning in the field of traditional infrastructure management. Instead of pursuing cutting-edge model architectures, it combines mature Random Forest methods, open weather data, and open-source map tools to solve specific high-social-value problems. Against the backdrop of energy transition and climate change, such projects will become increasingly valuable—making key systems more resilient and reducing public anxiety during extreme weather events.