Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks for pattern recognition
Neural networks for pattern recognition
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Expert Systems with Applications: An International Journal
Letters: Complex wavelet based texture classification
Neurocomputing
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Engineering Applications of Artificial Intelligence
Wavelet Transform application to the compression of images
Mathematical and Computer Modelling: An International Journal
IEEE Transactions on Neural Networks
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
IEEE Transactions on Neural Networks
Comparative study of different wavelets for hydrologic forecasting
Computers & Geosciences
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A growing number of countries all over the world are switching over to deregulated or the market structure of electricity sector with a view to enhance productivity, efficiency and to lower the prices. Barring a few cases, the deregulated structure is doing quite well in most of the countries. However a persistent issue that plagues the involved parties such as producers, traders, retailers etc., is the uncertainty that prevails in the system. Due to a number of known, unknown factors, the electricity prices exhibit fluctuating characteristics which is difficult to control as well as predict. Several forecasting techniques have been developed and successfully implemented for existing markets around the world with comparable performance. However, the uncertainty aspect of the point forecasts has not been analyzed significantly. In this work, an attempt is made to quantify such uncertainties existing in the market using statistical techniques like prediction intervals. Hybrid models using neural networks and Extreme Learning machines with wavelets as preprocessors are developed and applied for point as well as prediction interval forecasting for Ontario Electricity Market, PJM Day-Ahead and Real time markets.