The Analytic Transformation of the Energy Value Chain

Machine Learning Use Cases for the Energy Industry

Machine Learning use cases to boost your digital investments.

Over the past decade the energy industry has undergone a rapid transformation from a conservative past reliant on fossil fuels to a nimble future focused on decarbonization, decentralization, and digitalization.

The market potential is so vast that investment in AI and machine learning within the energy sector is expected to top $7.8 billion by the end of 2024.

This investment includes efforts to make generation and transmission assets smarter and more resilient, lower costs for consumers through better energy management, and make the energy grid more agile.

In an industry with already thin margins, efforts to reduce expenses are of the utmost importance.

Download the Guide

About the Guide

As the energy industry becomes more digitized and more data becomes available for analysis, it is important for utilities and generators to develop a roadmap for analytics to position themselves for success.

In this guide Elder Research, machine learning advisors and experts in forecasting and analytics, identifies the massive potential for analytics in the market and the use cases that will provide the most impact in each step of the energy value chain.

Key Takeaways

1. Use cases of machine learning within the energy value chain.

2. Three keys to securing success in analytics projects.

3. Elder Research case studies within the energy industry.

Download the guide to explore the machine learning use cases that will make the biggest impact on your bottom line.