AES Corporation is exploring the practical applications of data science to enhance the accuracy of Estimated Time of Restoration (ETR) predictions in utility companies.

With utilities facing increasing pressure to improve operational efficiency and customer satisfaction, accurate and timely ETR predictions are more crucial than ever.

AES Corporation will cover how advanced data science techniques, including machine learning models and predictive analytics, are revolutionizing ETR estimations.

Participants joining this demonstration by AES Corporation will:

• Learn about the tools and methodologies that data scientists employ to harness this data, and to improve prediction accuracy.
• Gain actionable insights on implementing these practices in their operations to drive real-world change.
• Understand the key data science techniques that are revolutionizing ETR predictions, including machine learning models and predictive analytics.
• Explore the integration of diverse data sources to enhance the accuracy of restoration time forecasts. • learn practical applications of these technologies in real-world utility scenarios, including how to overcome common challenges.
• Engage with case studies demonstrating successful implementation of data science in improving ETR and the resulting tangible benefits to both utilities and customers such as reduced downtime and improved customer satisfaction.