IDEAS home Printed from https://ideas.repec.org/a/ibn/ijbmjn/v11y2016i5p231.html
   My bibliography  Save this article

Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models

Author

Listed:
  • Abdoulaye Camara
  • Wang Feixing
  • Liu Xiuqin

Abstract

In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional.

Suggested Citation

  • Abdoulaye Camara & Wang Feixing & Liu Xiuqin, 2016. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(5), pages 231-231, April.
    • Handle: RePEc:ibn:ijbmjn:v:11:y:2016:i:5:p:231
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ijbm/article/download/57400/31650
    Download Restriction: no
    File URL: http://www.ccsenet.org/journal/index.php/ijbm/article/view/57400
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junttila, Juha, 2001. "Structural breaks, ARIMA model and Finnish inflation forecasts," International Journal of Forecasting, Elsevier, vol. 17(2), pages 203-230.
    2. Abhishek Singh & G. C. Mishra, 2015. "Application Of Box-Jenkins Method And Artificial Neural Network Procedure For Time Series Forecasting Of Prices," Statistics in Transition New Series, Polish Statistical Association, vol. 16(1), pages 83-96, March.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    4. Abhishek Singh & G. C. Mishra, 2015. "Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 83-96, May.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    7. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    8. Bodyanskiy, Yevgeniy & Popov, Sergiy, 2006. "Neural network approach to forecasting of quasiperiodic financial time series," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1357-1366, December.
    9. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wong, W.K. & Xia, Min & Chu, W.C., 2010. "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Elsevier, vol. 207(2), pages 807-816, December.
    2. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    3. Tsai, Ming-Feng & Wang, Chuan-Ju, 2017. "On the risk prediction and analysis of soft information in finance reports," European Journal of Operational Research, Elsevier, vol. 257(1), pages 243-250.
    4. Mark T. Leung & An‐Sing Chen & Ruben Mancha, 2009. "Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(4), pages 257-277, October.
    5. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    6. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    7. Bozos, Konstantinos & Nikolopoulos, Konstantinos, 2011. "Forecasting the value effect of seasoned equity offering announcements," European Journal of Operational Research, Elsevier, vol. 214(2), pages 418-427, October.
    8. Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
    9. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 761, Banco de la Republica de Colombia.
    10. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 10502, Banco de la Republica.
    11. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    12. Kritana Prueksakorn & Cheng-Xu Piao & Hyunchul Ha & Taehyeung Kim, 2015. "Computational and Experimental Investigation for an Optimal Design of Industrial Windows to Allow Natural Ventilation during Wind-Driven Rain," Sustainability, MDPI, vol. 7(8), pages 1-22, August.
    13. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    14. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    15. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    16. Yirigui Yirigui & Sang-Woo Lee & A. Pouyan Nejadhashemi & Matthew R. Herman & Jong-Won Lee, 2019. "Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    17. Alexander Vlasenko & Nataliia Vlasenko & Olena Vynokurova & Dmytro Peleshko, 2018. "A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction," Data, MDPI, vol. 3(4), pages 1-14, December.
    18. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    19. Vladimir Popov, 2009. "Why the West Became Rich before China and Why China Has Been Catching Up with the West since 1949: nother Explanation of the “Great Divergence” and “Great Convergence” Stories," Working Papers w0132, New Economic School (NES).
    20. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijbmjn:v:11:y:2016:i:5:p:231. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.