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題名 Modeling industrial energy demand in relation to subsector manufacturing output and climate change: artificial neural network insights
作者 楊素芬;蕭又新
Yang, Su-Fen;Shiau, Yuo-Hsien
Adha, Rishan;Muzayyanah, Syamsiyatul
貢獻者 統計系;應物所
關鍵詞 energy demand;  manufacturing output;  climate change;  artificial neural network
日期 2022-03
上傳時間 21-九月-2022 11:46:28 (UTC+8)
摘要 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.
關聯 Sustainability, 14(5), 2896
資料類型 article
DOI https://doi.org/10.3390/su14052896
dc.contributor 統計系;應物所-
dc.creator (作者) 楊素芬;蕭又新-
dc.creator (作者) Yang, Su-Fen;Shiau, Yuo-Hsien-
dc.creator (作者) Adha, Rishan;Muzayyanah, Syamsiyatul-
dc.date (日期) 2022-03-
dc.date.accessioned 21-九月-2022 11:46:28 (UTC+8)-
dc.date.available 21-九月-2022 11:46:28 (UTC+8)-
dc.date.issued (上傳時間) 21-九月-2022 11:46:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142030-
dc.description.abstract (摘要) 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.-
dc.format.extent 98 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Sustainability, 14(5), 2896-
dc.subject (關鍵詞) energy demand;  manufacturing output;  climate change;  artificial neural network-
dc.title (題名) Modeling industrial energy demand in relation to subsector manufacturing output and climate change: artificial neural network insights-
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.3390/su14052896-
dc.doi.uri (DOI) https://doi.org/10.3390/su14052896-