NDLI: Power Signal Forecasting by Neural Model with Different Layer Structures

Content Provider	IEEE Xplore Digital Library
Author	Rey-Chue Hwang Yu-Ju Chen Shang-Jen Chuang Huang-Chu Huang Chuo-Yean Chang
Copyright Year	2006
Description	Author affiliation: Electr. Eng. Dept., I-Shou Univ., Kaohsiung (Rey-Chue Hwang)
Abstract	In this paper, the non-stationary power load forecasting by using neural model with different layer structures is presented. In the neural forecasting model we developed, the neuron types used in different layers are different. Each layer is composed of the same kind of neurons. A reliable and accurate neural forecasting model for the non-stationary power loads is trying to be found in this study. To demonstrate the superiority of the model we created, all simulations are executed by using the conventional neural model with same neurons as a comparison. From the results shown, it is clearly found that the neural model we constructed do have better nonlinear mapping and forecasting capabilities in comparison with the conventional neural model
Starting Page	1
Ending Page	4
File Size	4005154
Page Count	4
File Format	PDF
ISBN	1424405483
DOI	10.1109/TENCON.2006.343877
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Neural networks Neurons Load forecasting Transfer functions Signal processing Companies Energy management Feedforward systems Demand forecasting neuron type non-stationary power load forecasting neural model
Content Type	Text
Resource Type	Article

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