PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Forecasting Energy Consumption in Short-Term and Long-Term Period by Using Arimax Model in the Construction and Materials Sector in Thailand

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study aims to analyze the forecasting of energy consumption in the Construction and Materials sectors. The scope of the study covers the forecasting periods of energy consumption for the next 10 years, 2017–2026, 20 years, 2017–2036, and 30 years, 2017–2046, by using ARIMAX Model. The prediction results show that these models are effective in the forecast measured by RMSE, MAE, and MAPE. The results show that from the first model (2,1,1), which predicted the duration of 10 years, 2017–2026, indicates that Thailand has increased an energy consumption rate with the average of 18.09%, while the second model (2,1,2) with the prediction of 20 years, 2017–2036, Thailand arises its energy consumption up to 37.32%. In addition, the third model (2,1,3) predicted the duration of 30 years from 2017 to 2046, and it has found that Thailand increases its energy consumption up to 49.72%.
Rocznik
Strony
52--59
Opis fizyczny
Bibliogr. 32 poz., tab., rys.
Twórcy
  • Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Krung Thep Maha Nakhon, Bangkok, Thailand
  • Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Krung Thep Maha Nakhon, Bangkok, Thailand
Bibliografia
  • 1. Asian Development Bank (ADB). 2014. Environment, Climate Change, and Disaster Risk Management. Manila. Asian Development Bank.
  • 2. Assaad M., Boné R., Cardot H. 2008. A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inform Fusion, 9, 41–55.
  • 3. Azadeh A., Asadzadeh S., Saberi M., Nadimi V., Tajvidi A., Sheikalishahi M. 2011. A neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Appl Energy, 88, 3850–9.
  • 4. Azadeh A., Saberi M., Seraj O. 2010. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran. Energy, 35, 2351–66.
  • 5. Barak S., Dahooie JH., Tichy´ T. 2015. Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Syst Appl, 42, 9221–35.
  • 6. Ciabattoni L., Grisostomi M., Ippoliti G., Longhi S. 2014. Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario. Energy, 74, 359–67.
  • 7. Chienwattanasook K., Sutthichaimethee P. 2012. Trend of Thailand jewelry export to the USA market. International Academy of Business and Economics, 12(3).
  • 8. Dong B., Coa C., Lee S.E. 2005. Applying support vector machines to predict building energy consumption in tropical region. Energy Build, 37, 545–553.
  • 9. Ekonomou L. 2010. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35, 512–517.
  • 10. Hao J., Liu D., Li Z., Chen Z., Kong L. 2012. Power system load forecasting based on fuzzy clustering and gray target theory. Energy Proc, 16, 1852–1859.
  • 11. Jovanovic RZ., Sretenovic AA., Zivkovic BD. 2015. Ensemble of various neural networks for prediction of heating energy consumption. Energy Build, 94, 189–199.
  • 12. Lee Y-S., Tong L-I. 2011. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers Manage, 52, 147–152.
  • 13. Lee Y-S., Tong L-I. 2012. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl Energy, 94, 251–256.
  • 14. Leontief W.W. 1986. Input-Output Economics (2nd ed.). New York, Oxford University Press.
  • 15. Mamlook R., Badran O., Abdulhadi E. 2009. A fuzzy inference model for short-term load forecasting. Energy Policy, 37, 1239–1248.
  • 16. Office of the National Economic and Social Development Board. 2015. National Income of Thailand. Bangkok: NESDB.
  • 17. Osorio G., Matias J., Catalão J. 2015. Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew Energy, 75, 301–307.
  • 18. Pappas S.S., Ekonomou L., Karamousantas D.C., Chatzarakis G., Katsikas S., Liatsis P. 2008. Electricity demand loads modeling using Auto Regressive Moving Average (ARMA) models. Energy, 33, 1353–1560.
  • 19. Suganthi L., Iniyan S., Samuel AA. 2015. Applications of fuzzy logic in renewable energy systems – a review. Renew Sustain Energy Rev, 48, 585–607.
  • 20. Suganthi L., Samuel A.A. 2012. Energy models for demand forecasting – a review. Renew Sustain Energy Rev, 16, 1223–1240.
  • 21. Sutthichaimethee P. 2016. Modeling environmental impact of machinery sectors to promote sustainable development of Thailand. Journal of Ecological Engineering, 17(1), 18–25.
  • 22. Sutthichaimethee P., et al. 2016. Model of environmental problems priority arising from the use of environmental and natural resources in construction material sectors of Thailand. Advanced Engineering Forum, 14, 76–85.
  • 23. Sutthichaimethee P., et al. 2015. Environmental problems indicator under environmental modeling toward sustainable development. Global J. Environ. Sci. Manage., 14(1), 325–332.
  • 24. Sutthichaimethee P., Tanoamchard W. 2015. Carrying capacity model of food manufacturing sectors for sustainable development from using environmental and natural resources of Thailand. Journal of Ecological Engineering, 16(5), 1–8.
  • 25. Sutthichaimethee P., Sawangdee Y. 2016. Model of environmental impact of service sectors to promote sustainable development of Thailand. Ethics Sci Environ Polit, 16(1).
  • 26. Sutthichaimethee P., Sawangdee Y. 2016. Indicator of environmental problems priority arising from the use of environmental and natural resources in machinery sectors of Thailand. Environmental and Climate Technologies .17(1), 18–29.
  • 27. Sutthichaimethee P., Sawangdee Y. 2016. Indicator of environmental problems of agricultural sectors under the environmental modeling. Journal of Ecological Engineering, 17(2), 12–18.
  • 28. Sutthichaimethee P., Ariyasajjakorn D. 2017. Forecasting model of GHG emission in manufacturing sectors of Thailand. Journal of Ecological Engineering, 18(1), 18–24
  • 29. Thailand Development Research Institute (TDRI). 2007. Prioritizing environmental problems with environmental costs. Final report prepared the Thailand Health Fund. Bangkok.
  • 30. Xie N-m., Yuan C-q., Yang Y-j. 2015. Forecasting China’s energy demand and self sufficiency rate by grey forecasting model and Markov model. Int J Electr Power Energy Syst, 66, 1–8.
  • 31. Yu S., Wei Y-M., Wang K. A. 2012. PSO–GA optimal model to estimate primary energy demand of China. Energy Policy, 42, 329–340.
  • 32. Zhao H., Magoulès F. 2012. A review on the prediction of building energy consumption. Renewable Sustainable Energy Rev, 16, 3586–3592.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-add0afe2-7343-44a5-be9e-48b8182b253a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.