Avista Predictive Repairs

Avista Corporation, a prominent energy company faced a critical challenge in predicting and preventing failures in their electricity poles and components. The aim was to better understand correlations between various factors leading to replacements, ultimately enhancing service reliability and preventing power outages for residential, commercial, and industrial customers.

To address this challenge, we employed advanced data science methodologies, including XGBoost and feature importance analysis. Our approach involved identifying key variable correlations that signaled the need for pole or component replacement and predicting the likelihood of replacement for each part.

To optimize the model’s performance, we conducted a variance analysis, streamlining the variables for a more efficient and accurate prediction. Weather data integration accounted for environmental factors accelerating pole degradation.

The implemented solution had a profound impact on Avista’s operational efficiency. By generating a replacement schedule based on predictive modeling, Avista significantly decreased overall power failures in the region.

This proactive approach not only improved customer satisfaction by minimizing service interruptions but also demonstrated the tangible benefits of leveraging data science for infrastructure management.

The project showcased the potential of predictive analytics in enhancing the reliability and resilience of energy distribution systems.