NDLI: A hierarchical hybrid neural model with time integrators in long-term peak-load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Carpinteiro, O.A.S. Leme, R.C. de Souza, A.C.Z. Filho, P.S.Q.
Copyright Year	2005
Description	Author affiliation: Res. Group on Comput. Networks & Software Eng., Fed. Univ. of Itajuba, Brazil (Carpinteiro, O.A.S.; Leme, R.C.; de Souza, A.C.Z.; Filho, P.S.Q.)
Abstract	A novel hierarchical hybrid neural model to the problem of long-term peak-load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets - one on top of the other -, and a single-layer perceptron. It has application into domains in which the context information given by former events plays a primary role. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results are presented and evaluated in the paper.
Starting Page	2960
Ending Page	2965
File Size	1403926
Page Count	6
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Load forecasting Power industry Multilayer perceptrons Time series analysis Data mining Context modeling Spatiotemporal phenomena Intelligent networks Computer networks
Content Type	Text
Resource Type	Article

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