Joint Session 65 Machine Learning Applications in the Energy Sector

Thursday, 16 January 2020: 10:30 AM-12:00 PM
156A (Boston Convention and Exhibition Center)

Cochairs:
Tyler C. McCandless, NCAR, Research Applications Laboratory, Boulder, CO and Sue Ellen Haupt, NCAR, Research Applications Laboratory, Boulder, CO

Description:

Machine learning applications related to energy.


Papers:
10:30 AM
J65.1
Machine and Deep Learning Methods for Fault Detection and Classification in Photovoltaic Modules
Warren James Brettenny, Nelson Mandela University, Port Elizabeth, South Africa; and C. W. Dunderdale, C. M. Clohessy, E. E. van Dyk, and G. D. Sharp

10:45 AM
J65.2
New developments in weather-based power outage prediction modeling
Diego Cerrai, Univ. of Connecticut, Storrs, CT; and P. Watson, M. Koukoula, F. Yang, and E. Anagnostou

11:00 AM
J65.3
Performance of Alternate Machine Learning Configurations for the 0-120 Hour Prediction of Extreme Wind Gusts for Outage Management in the Consolidated Edison Company of New York Service Area
John W. Zack, MESO, Inc., Troy, NY; and J. M. Freedman, M. Berlinger, and C. Cheng

11:15 AM
J65.4
Comparing Implicit vs Explicit Regime Identification in Machine Learning Approaches to Short-Range Solar Power Forecasting
Tyler C. McCandless, NCAR, Boulder, CO; and S. Dettling and S. E. Haupt

11:30 AM
J65.5
Optimizing Training Windows for Wind and Solar Generation Forecasting
Daniel B. Kirk-Davidoff, UL, Albany, MD; and P. Tardaguila and T. Melino

11:45 AM
J65.6
A Deep Learning Framework for Forecasting Power in a Full-Scale Wind Farm
Rajitha Meka, University of Texas at San Antonio, San Antonio, TX; and K. Bhaganagar and A. Alaeddini

Abstract Submissions Closed



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