Short Term Energy Forecasting with Neural Networks

McMenamin, J. Stuart; Monforte, Frank A.
October 1998
Energy Journal;1998, Vol. 19 Issue 4, p43
Academic Journal
Provides information on artificial neural networks. Neural network specification and terminology; Estimation approaches for neural networks; Summary statistics and test statistics for neural networks.


Related Articles

  • Artificial Neural Networks make their mark as a powerful tool for investors. Cheng, Wei; McClain, Bruce W. // Review of Business;Summer97, Vol. 18 Issue 4, p4 

    Presents information on Artificial Neural Networks (ANNs) investment systems, fast becoming a powerful tool for investment forecasting and achieving impressive results. Information on ANNs; Example of the success of ANN; Information on forecasting in investment decisions and its success;...

  • Think neural networks. Woelfel, Joseph // Marketing Tools;Apr/May94, Vol. 1 Issue 6, p62 

    Focuses on artificial neural network as a computer software that models some of the basic elements of the brain. Ability of artificial neural networks to recognize patterns and perform qualitative analysis; Description of artificial neural network system; Operation process; Application of...

  • Determination of cloud liquid water path over the oceans from special sensor microwave/imager... Jung, Thomas; Ruprecht, Eberhard; Wagner, Friedrich // Journal of Applied Meteorology;Aug98, Vol. 37 Issue 8, p832 

    Focuses on the observations made of a neural network (NN), developed to retrieve the cloud liquid water path (LWP). What the retrieval with NN depends on; Information on a data and radiative transfer model; Details on different neural network architectures.

  • Feedforward backpropagation neural networks in prediction of farmer risk preferences. Kastens, Terry L.; Featherstone, Allen M. // American Journal of Agricultural Economics;May96, Vol. 78 Issue 2, p400 

    Compares ordered multinomial logistic regression models with feedforward backpropagation neural network models. Prediction of logistic models; Accommodation of the models for loss functions; Empirical applications of ordered logits.

  • Predict difficult-to-measure properties with neural analyzers. Deshpande, Pradeep B.; Yerrapragada, Srinivas S. // Control Engineering;Jul97, Vol. 44 Issue 10, p55 

    Discusses the capability of artificial neural networks (ANNs) for predicting on-line process variables. Drawbacks of inferential analyzers; Basis of traditional approaches to inferential analyzers; Three layers of multilayer perception ANNs. INSET: Artificial neural networks, a review..

  • Building a better brain. Kirkwood, Craig // Australian Personal Computer;May98, Vol. 19 Issue 5, p99 

    Focuses on the technology associated with neural networks. Definition of neural networks; Information on how a neural network works; Indepth look at neural Internets. INSET: How to get your own neural network.

  • Silicon neural networks learn as they compute. Paillet, Guy // Laser Focus World;Aug96, Vol. 32 Issue 8, pS17 

    Discusses various aspects of neural networking. Parallel processing needed for real time tasking; Image recognition; Applications; Memory overview.

  • Beyond programming. Coffee, Peter // PC Week;03/10/97, Vol. 14 Issue 10, p64 

    Discusses alternative uses for neural nets. Several uses of neural nets beyond artificial intelligence; How a neural net differs from a conventional program; Advantages and disadvantages; Speculation for the future of neural nets.

  • What is a neural network? Wessner, Cecilia; O'Malley, Chris // Popular Science;Aug98, Vol. 253 Issue 2, p81 

    Describes neural networks. Techniques that neural networks use; Effects of neural network systems on humans; How neural networks are used.


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics