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Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China

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  • Lai, T.M.
  • To, W.M.
  • Lo, W.C.
  • Choy, Y.S.

Abstract

The use of electricity is indispensable to modern life. As Macao Special Administrative Region becomes a gaming and tourism center in Asia, modeling the consumption of electricity is critical to Macao's economic development. The purposes of this paper are to conduct an extensive literature review on modeling of electricity consumption, and to identify key climatic, demographic, economic and/or industrial factors that may affect the electricity consumption of a country/city. It was identified that the five factors, namely temperature, population, the number of tourists, hotel room occupancy and days per month, could be used to characterize Macao's monthly electricity consumption. Three selected approaches including multiple regression, artificial neural network (ANN) and wavelet ANN were used to derive mathematical models of the electricity consumption. The accuracy of these models was assessed by using the mean squared error (MSE), the mean squared percentage error (MSPE) and the mean absolute percentage error (MAPE). The error analysis shows that wavelet ANN has a very promising forecasting capability and can reveal the periodicity of electricity consumption.

Suggested Citation

  • Lai, T.M. & To, W.M. & Lo, W.C. & Choy, Y.S., 2008. "Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China," Energy, Elsevier, vol. 33(5), pages 679-688.
    • Handle: RePEc:eee:energy:v:33:y:2008:i:5:p:679-688
    DOI: 10.1016/j.energy.2007.12.007
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    3. Chen, B. & Li, J.S. & Zhou, S.L. & Yang, Q. & Chen, G.Q., 2018. "GHG emissions embodied in Macao's internal energy consumption and external trade: Driving forces via decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4100-4106.
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    7. Li, J.S. & Chen, G.Q. & Wu, X.F. & Hayat, T. & Alsaedi, A. & Ahmad, B., 2014. "Embodied energy assessment for Macao׳s external trade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 642-653.
    8. Lam, K.H. & Lai, T.M. & Lo, W.C. & To, W.M., 2012. "The application of dynamic modelling techniques to the grid-connected PV (photovoltaic) systems," Energy, Elsevier, vol. 46(1), pages 264-274.
    9. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    10. Irsag, Bojan & Pukšec, Tomislav & Duić, Neven, 2012. "Long term energy demand projection and potential for energy savings of Croatian tourism–catering trade sector," Energy, Elsevier, vol. 48(1), pages 398-405.
    11. Özer, Betül & Görgün, Erdem & İncecik, Selahattin, 2013. "The scenario analysis on CO2 emission mitigation potential in the Turkish electricity sector: 2006–2030," Energy, Elsevier, vol. 49(C), pages 395-403.
    12. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, vol. 10(7), pages 1-16, June.
    13. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    14. Voyant, Cyril & Motte, Fabrice & Fouilloy, Alexis & Notton, Gilles & Paoli, Christophe & Nivet, Marie-Laure, 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies," Energy, Elsevier, vol. 120(C), pages 199-208.
    15. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    16. Cinar, Didem & Kayakutlu, Gulgun & Daim, Tugrul, 2010. "Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey," Energy, Elsevier, vol. 35(4), pages 1724-1729.
    17. To, W.M. & Lai, T.M. & Chung, W.L., 2011. "Fuel life cycle emissions for electricity consumption in the world’s gaming center–Macao SAR, China," Energy, Elsevier, vol. 36(8), pages 5162-5168.
    18. Li, J.S. & Chen, G.Q., 2013. "Energy and greenhouse gas emissions review for Macao," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 23-32.
    19. Xie, Yanhua & Weng, Qihao, 2016. "Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries," Energy, Elsevier, vol. 100(C), pages 177-189.
    20. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
    21. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    22. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    23. Lin, Boqiang & Wu, Ya & Zhang, Li, 2012. "Electricity saving potential of the power generation industry in China," Energy, Elsevier, vol. 40(1), pages 307-316.
    24. Lai, T.M. & To, W.M. & Lo, W.C. & Choy, Y.S. & Lam, K.H., 2011. "The causal relationship between electricity consumption and economic growth in a Gaming and Tourism Center: The case of Macao SAR, the People’s Republic of China," Energy, Elsevier, vol. 36(2), pages 1134-1142.

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