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Tutorial
May 28, 2006, 13:30-15:30, Room E
From Neural Networks to the Intelligent Power Grid: What it Takes to Make Things Work
| National Science Foundation, Washington, USA |
Abstract
In 2002, US National Science Foundation (NSF) and the Electric Power Research Institute (EPRI) jointly sponsored a workshop on how to achieve a radical paradigm shift in the management, design and planning of electric power grids. The goal was to develop rigorous tools to allow us to manage all pieces of the larger grid in an integrated way, so at the maximize the value-added over time of the entire system, rather than piece-by-piece design and control. The intelligent grid will be one crucial piece of achieving a sustainable global energy system. To achieve this kind of optimizing adaptive system, we need to extend and use new algorithms which full address the nonlinear general case; this in turn requires that we use the best available universal function approximators -- artificial neural networks (ANN). This tutorial will map out the "ladder" of designs using neural networks as part of larger systems for prediction and control, starting from the simple but weak tools well-known outside the neural network community, through to newer tools which generally outperform the most well-known statistical and control methods, all the way through to new designs and challenges for research related to the truly intelligent grid.
For additional information, see www.werbos.com/energy.htm
Biosketch
Paul J. Werbos holds four degrees from Harvard and the London School of Economics in: (1) economics; (2) international political systems, emphasizing European economic institutions; (3) applied mathematics, with a major in quantum physics and a minor in decision and control; (4) applied mathematics, towards an interdisciplinary Ph.D. thesis. Prior to that, during high school, he obtained an FCC First Class Commercial Radiotelephone License, and took undergraduate and graduate mathematics courses at Princeton and the University of Pennsylvania.
For about four years after the PhD, he taught courses at Maryland in quantitative methods and global futures, and performed research in intelligent systems for policy application. Then for nine years he worked at the Department of Energy evaluating and developing a wide range of energy forecasting models. In 1989 he joined NSF as a program director in the ECS Division managing Neuroengineering (neural networks) and Emerging Technology Initiation. Within CNCI area, his main goal is to maximize the development and dissemination of step-by-step advances in systems design which will lead to an understanding and replication of the general kind of learning-based intelligence described in the recent NSF workshop on learning and approximate dynamic programming. In essence, this involves an equal emphasis on general-purpose intelligent control issues, and on the prediction and learning issues important to developing the subsystems needed for such an integrated intelligent system. He also has a special interest in the use of computational intelligence to help provide new options in large-scale important areas such as energy, sustainability, and the like. He has also initiated a new area of quantum neural networks or quantum learning, an approach to the algorithm gap in quantum computing.
He has served as President of the International Neural Network Society, where he is still on the Governing Board. He also serves on the AdCom of the IEEE Industrial Electronics Society, and the chairs the Awards Committee of the IEEE Neural Network Society. He is also on the Planning Committee of the Millennium Project of the United Nations University (http://millennium-project.org), and on various cross-cutting working groups such as the working group on energy production and distribution of the interagency Climate Change Technology Program.
