政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142030

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142030

Title:	Modeling industrial energy demand in relation to subsector manufacturing output and climate change: artificial neural network insights
Authors:	楊素芬;蕭又新 Yang, Su-Fen;Shiau, Yuo-Hsien Adha, Rishan;Muzayyanah, Syamsiyatul
Contributors:	統計系;應物所
Keywords:	energy demand; manufacturing output; climate change; artificial neural network
Date:	2022-03
Issue Date:	2022-09-21 11:46:28 (UTC+8)
Abstract:	The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.
Relation:	Sustainability, 14(5), 2896
Data Type:	article
DOI 連結:	https://doi.org/10.3390/su14052896
DOI:	10.3390/su14052896
Appears in Collections:	[統計學系] 期刊論文 [應用物理研究所 ] 期刊論文

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