Power Signal Forecasting by Neural Model with Different Layer Structures

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 accur...

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Hauptverfasser:	Rey-Chue Hwang, Yu-Ju Chen, Shang-Jen Chuang, Huang-Chu Huang, Chuo-Yean Chang
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Companies Demand forecasting Energy management Feedforward systems forecasting Load forecasting neural model Neural networks neuron type Neurons non-stationary power load Predictive models Signal processing Transfer functions
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creator	Rey-Chue Hwang Yu-Ju Chen Shang-Jen Chuang Huang-Chu Huang Chuo-Yean Chang
description	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
doi_str_mv	10.1109/TENCON.2006.343877
format	Conference Proceeding
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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</abstract><pub>IEEE</pub><doi>10.1109/TENCON.2006.343877</doi><tpages>4</tpages></addata></record>
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subjects	Companies Demand forecasting Energy management Feedforward systems forecasting Load forecasting neural model Neural networks neuron type Neurons non-stationary power load Predictive models Signal processing Transfer functions
title	Power Signal Forecasting by Neural Model with Different Layer Structures
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