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

Author

Listed:
  • 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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    as
    1. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
    2. Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
    3. Dargay, Joyce & Gately, Dermot, 1997. "The demand for transportation fuels: Imperfect price-reversibility?," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 71-82, February.
    4. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    5. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    6. repec:aen:journl:2011v32-02-a03 is not listed on IDEAS
    7. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    8. Ouedraogo, Nadia S., 2017. "Modeling sustainable long-term electricity supply-demand in Africa," Applied Energy, Elsevier, vol. 190(C), pages 1047-1067.
    9. Rishan Adha & Cheng-Yih Hong, 2021. "How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 354-364.
    10. Adeyemi, Olutomi I. & Hunt, Lester C., 2014. "Accounting for asymmetric price responses and underlying energy demand trends in OECD industrial energy demand," Energy Economics, Elsevier, vol. 45(C), pages 435-444.
    11. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    12. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    13. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, November.
    14. Dargay, Joyce & Gately, Dermot, 1995. "The imperfect price reversibility of non-transport oil demand in the OECD," Energy Economics, Elsevier, vol. 17(1), pages 59-71, January.
    15. Adeyemi, Olutomi I. & Hunt, Lester C., 2007. "Modelling OECD industrial energy demand: Asymmetric price responses and energy-saving technical change," Energy Economics, Elsevier, vol. 29(4), pages 693-709, July.
    16. Nadia S. Ouedraogo, 2017. "Modeling sustainable long-term electricity supply-demand in Africa," WIDER Working Paper Series wp-2017-23, World Institute for Development Economic Research (UNU-WIDER).
    17. Harris, Tyler M. & Devkota, Jay P. & Khanna, Vikas & Eranki, Pragnya L. & Landis, Amy E., 2018. "Logistic growth curve modeling of US energy production and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 46-57.
    18. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.
    19. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    20. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    21. Berndt, Ernst R & Wood, David O, 1975. "Technology, Prices, and the Derived Demand for Energy," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 259-268, August.
    22. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    23. Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.
    24. Yu-Chen Yang & Cheng-Yih Hong & Syamsiyatul Muzayyanah & Rishan Adha, 2020. "Decomposition of Growth Factors in High-tech Industries and CO2 Emissions: After the World Financial Crisis in 2008," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 500-506.
    25. Lu, Shyi-Min & Lu, Ching & Tseng, Kuo-Tung & Chen, Falin & Chen, Chen-Liang, 2013. "Energy-saving potential of the industrial sector of Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 674-683.
    26. Greening, Lorna A. & Boyd, Gale & Roop, Joseph M., 2007. "Modeling of industrial energy consumption: An introduction and context," Energy Economics, Elsevier, vol. 29(4), pages 599-608, July.
    27. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    28. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2015. "A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand," Energy Economics, Elsevier, vol. 49(C), pages 599-609.
    29. Adha, Rishan & Hong, Cheng-Yih & Firmansyah, M. & Paranata, Ade, 2021. "Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia," MPRA Paper 110444, University Library of Munich, Germany.
    30. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    31. Haas, Reinhard & Schipper, Lee, 1998. "Residential energy demand in OECD-countries and the role of irreversible efficiency improvements," Energy Economics, Elsevier, vol. 20(4), pages 421-442, September.
    32. El-Shazly, Alaa, 2013. "Electricity demand analysis and forecasting: A panel cointegration approach," Energy Economics, Elsevier, vol. 40(C), pages 251-258.
    33. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
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    Cited by:

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    2. Rishan Adha & Cheng-Yih Hong & Somya Agrawal & Li-Hua Li, 2023. "ICT, carbon emissions, climate change, and energy demand nexus: The potential benefit of digitalization in Taiwan," Energy & Environment, , vol. 34(5), pages 1619-1638, August.
    3. Arpit Singh & Ashish Dwivedi & Dindayal Agrawal & Durgesh Singh, 2023. "Identifying issues in adoption of AI practices in construction supply chains: towards managing sustainability," Operations Management Research, Springer, vol. 16(4), pages 1667-1683, December.
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