This paper presents an artificial neural network(ANN) approach t o electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.

Abstract-The pattern recognition approach to transient stability analysis (TSA) has been presented as a promising tool for online application. This paper applies a recently introduced learning-based nonlinear classifier, the support vector machine (SVM), showing its suitability for TSA. It can be seen as a different approach to cope with the problem of high dimensionality. The high dimensionality of power systems has led to the development and implementation of feature selection techniques to make the application feasible in practice. SVMs' theoretical motivation is conceptually explained and they are tested with a 2684-bus Brazilian system. Aspects of model adequacy, training time, classification accuracy, and dimensionality reduction are discussed and compared to stability classifications provided by multilayer perceptrons.Index Terms-Feature selection, neural networks, support vector machine, transient stability analysis.

Abstract-Generalized time-frequency representations (GTFR's) which use cone-shaped kernels for nonstationary signal analysis are presented. The cone-shaped kernels are formulated for the GTFR's to produce simultaneously good resolution in time and frequency. Specifically, for a GFTR with a cone-shaped kernel, finite time support is maintained in the time dimension along with an enhanced spectrum in the frequency dimension, and the cross-terms are smoothed out. Experimental results on simulated data and real speech showed the advantages of the GTFR's with the cone-shaped kernels through comparisons to the spectrogram and the pseudo-Wigner distribution.

Abstract-Wireless multicast/broadcast sessions, unlike wired networks, inherently reaches several nodes with a single transmission. For omnidirectional wireless broadcast to a node, all nodes closer will also be reached. An algorithm for constructing the minimum power tree in wireless networks was first proposed by Wieselthier Ø Ð. The ÖÓ ×Ø Ò Ö Ñ ÒØ Ð ÔÓÛ Ö (BIP) algorithm suggested by them is a "node-based" minimum-cost tree algorithm for wireless networks. We propose an alternate search based paradigm wherein minimum-cost trees in wireless networks are found through a search process. Two computationally efficient procedures for checking the feasibility (viability) of a solution in the search space are presented. A straightforward procedure for initializing the search using stochastically generated trees is also proposed.

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research. Bradford Books imprint