Predictive and Prescriptive Analytics Using
Machine Learning

Abstract

The complexity, criticality, and real-time demands of the energy sector make it a prime candidate to benefit from applying machine learning. This session presents two case studies of machine learning automating decisions for energy companies.

For the largest windfarm operator in North America, machine learning applies predictive and prescriptive analytics to the complex task of scheduling crews for maintenance and repairs. Automating the scheduling process across multiple windfarm sites saves the operator millions in labor costs per year and frees managers and crews to do actual work. Machine learning also evaluates ever-changing conditions and automatically reschedules workers and tasks as necessary.

For a large European energy company, online machine learning provides a systematic and automated approach to commodities trading, including creating and executing trading strategy and predicting prices.

Speaker Bio

As Chief Technology Officer at Space-Time Insight, Dr. Paul Hofmann draws on more than twenty years of experience in enterprise software, analytics and machine learning. He has held executive roles at BASF and SAP, where he was vice president of R&D. He has conducted academic research at MIT, Technical University in Munich, and Northwestern University. Most recently, Paul served as CTO for Saffron Technology, which is now part of Intel. He is a Computer Science Advisory Board Member at Stony Brook University and on the Advisory Board of the Dean’s Advisory Council at UC Santa Cruz’s Baskin School of Engineering. His Ph.D., in Physics, is from Technical University Darmstadt.

Note : The doors close at 7:30 PM