Dynamic Rhythms

Challenge Overview

Predicting power outages, especially those linked to extreme weather, is crucial for public safety, economic stability, and emergency response. I recently tackled this complex problem in the Dynamic Rhythms challenge, aiming to build robust models for anticipating grid disruptions.

The task was to develop a reliable system for predicting power outages and their correlation with rare and extreme weather events. Key challenge aspects included:

• Dual data sources: The model needed to integrate these distinct yet interconnected sources.
• Unstructured evaluation: Train/test/hold-out sets and metrics were not fixed but could be customized, encouraging innovative approaches.
• Holistic prediction: The goal was to predict if, where, and when outages would happen, with sufficient lead time, and to forecast their severity and duration.

Exploratory Data Analysis Insights

To tackle this problem, we first have to have a good understanding of the data. To make a long story short, a couple of key insights were found:

• Target variable skewness: The target variable (customers without power) was highly skewed with occasional large spikes, confirming the rare but impactful nature of significant outage events.
• Missing values: Missing values in event count columns, particularly for rare events (some with >95% missing values), were imputed with 0.
• Strongest correlations: The tropical storm and hurricane events had the most pronounced positive correlation with the target variable, highlighting their severe impact.