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Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

Author

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  • Yuo-Hsien Shiau

    (Graduate Institute of Applied Physics, National Chengchi University, Taipei 11605, Taiwan
    Research Center for Mind, Brain and Learning, National Chengchi University, Taipei 11605, Taiwan)

  • Su-Fen Yang

    (Department of Statistics, National Chengchi University, Taipei 11605, Taiwan)

  • Rishan Adha

    (Graduate Institute of Applied Physics, National Chengchi University, Taipei 11605, Taiwan
    Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Syamsiyatul Muzayyanah

    (Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan)

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.

Suggested Citation

  • Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2896-:d:762295
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    Cited by:

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    2. Akshansh Mishra & Anish Dasgupta, 2022. "Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints," Forecasting, MDPI, vol. 4(4), pages 1-11, September.
    3. Syamsiyatul Muzayyanah & Cheng-Yih Hong & Rishan Adha & Su-Fen Yang, 2023. "The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach," Sustainability, MDPI, vol. 15(12), pages 1-20, June.

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