Predictive and Prescriptive Analytics Using Machine Learning
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  Paul Hofmann   Paul Hofmann
Chief Technology Officer
Space-Time Insight


Tuesday, January 31, 2017
01:30 PM - 02:15 PM

Level:  Business/Strategic

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.

Attend this session to learn:

• The benefits of automating decisions
• The types of decisions to target for automation
• Guidelines for implementing machine learning in complex operating environments

Paul Hofmann is responsible for Space-Time Insight's technology direction and product management. With more than 20 years’ experience in enterprise software and services, Paul’s background combines academic thought leadership with international executive leadership at global companies. Before joining Space-Time Insight, Paul was CTO at Saffron Technology, now an Intel company, and Vice President of Research at SAP Labs at Palo Alto.

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