Market Research Report
Machine Learning for the Digital Utility
|Published by||Navigant Research||Product code||592434|
|Published||Content info||15 Pages; 2 Tables, Charts & Figures
Delivery time: 1-2 business days
|Machine Learning for the Digital Utility|
|Published: December 14, 2017||Content info: 15 Pages; 2 Tables, Charts & Figures||
It is hard to escape the hype surrounding artificial intelligence (AI). Blockchain is possibly the only other technology to command as many headlines as AI. Yet, there is a tangible difference between what is written about the two technologies. On the one hand, despite plenty of issues with blockchain, many in the media still effusively claim it will revolutionize the world in the same way as the internet. AI receives similarly gushing praise from some quarters-typically in technology and trade press, which extol the virtue of an increasingly automated future-but can be heavily criticized in mainstream press as a threat to employment or civilization itself.
Machine learning is an AI technology that is rapidly moving into the mainstream and is high on the agenda of many utilities. This report discusses machine learning in the context of other AI and analytics technologies, provides a brief description of how it works, and examines some recent high profile achievements. While machine learning is not new in parts of the utility value chain, despite some inherent limitations to the technology, various drivers will bring it out of pockets of excellence and thrust it into many other areas of the business.
This Navigant Research report describes several use cases for machine learning and examines why machine learning has an advantage over existing analytics techniques, including customer segmentation, pricing forecasts, anomaly detection, fraud detection, and predictive maintenance. Future requirements for machine learning-specifically for distributed energy resources (DER) management and transactive energy-are also discussed, as are several recommendations for utilities developing their machine learning strategies.