Overview

Machine learning and other artificial intelligence technologies have been proved to be powerful tools in extracting and processing information from large sets of data and have been used extensively for different applications in energy, such as Load forecasting and demand management, yield optimization, predictive maintenance in the utility industry; energy trading nad customer insights in the retail field; and supplier selection, consumption insights, and building energy management in the consumers sector. We will bring both academic and industry experts in energy and machine learning fields to share ideas, opportunities, and best practices, as well as challenges and caveats in applying machine learning in energy.

We will bring both academic and industry experts in energy and machine learning fields to share ideas, opportunities, and best practices, as well as challenges and caveats in applying machine learning in energy. Every speaker has ~20 mins to present their work.  

 

Hosted by Stanford IEEE and SCEA, assistant hosted by CSEE North American Chapter.

SUMMIT 2019

Artificial Intelligence in Energy Summit 

Date: Feb. 24th, 2019

Venue: Stanford University, CA

2019 US-China Energy & Climate + AI Summits

Agenda

 

AM

8:30-­9:00            Registration with breakfast 

9:10-­9:15            Welcome 

· Na Yu, Energy Efficiency Program Director of SCEA 

                               Introduction to SCEA and Stanford IEEE

· Yang Hu & Jing Ge, Co-presidents of SCEA  & Brian Lui, President of Stanford IEEE

9:15-­9:40            AI for Sustainability 

· Scott Mauvais, Director of Technology & Civic Innovation at Microsoft

9:40-10:05          Convex reformulations for nonlinear control and estimation

                            problems in power systems 

· Ming Jin, postdoctoral researcher in the Department of Industrial Engineering and Operations Research at University of California, Berkeley 

10:05-10:30        An Operating IoT/ML-driven Real-time Predictive Asset

                            Maintenance Application Case Study

· Eric Hsieh, Senior Technical Product Manager at AutoGrid

10:30-10:55        Power system operation control based on AI

· Zhiwei Wang, President of GEIRI North America 

10:55-11:15        Tea, Coffee & Networking 

 

11:15-11:40         Analyzing AMI Data to Predict Network Connectivity 

· James Hansell, Associate Director, Navigant Consulting 

 

11:40-12:05         Intelligent Sensor Applications in Distribution Grids 

· Ye Tao, Sr. Advanced Applications Scientist, Sentient Energy

PM

 

12:05-1:20          Lunch & Networking 

 

1:20-1:45            Bias in data and the impact on AI 

· Scott Mauvais, Director of Technology & Civic Innovation at Microsoft 

 

1:45-2:10            Fleet Management of Internet of Energy

· Yang Bai, Senior Software Engineer, Google 

 

2:10-2:35            Physical model or data model? The use cases of AI in building

                            design and operation

· Weili Xu, Chief Product Officer, BuildSimHub

 

2:35-2:55            Tea, Coffee & Networking 

2:55-3:20             Integration of electric vehicles into the grid

· Marta Gonzalez, Associate Professor of City and Regional Planning at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab)

3:20-3:45             Rebuilding climate and weather risk models around physics

                             informed AI

· Adrian Albert, Energy/Environmental Policy Project Scientist/Engineer, at Energy Storage & Distributed Resources Division in LBNL

 

​3:50-4:05              Machine vision for methane emissions detection using an

                              infrared camera

· Jingfan Wang, Ph.D. Candidate in the Department of Energy and Resource Engineering at Stanford University

 

4:05-4:20              Closed-loop optimization of battery fast charging procedures

· Aditya Grover, Ph.D. candidate in the Computer Science Department at Stanford University, Stanford Artificial Intelligence Laboratory

 

4:20-5:20              Open Discussion 

 

5:20-6:20              Networking 

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