IDEAS home Printed from
   My bibliography  Save this article

A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting


  • Chahkoutahi, Fatemeh
  • Khashei, Mehdi


Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of decisions that generally has a direct non-strict relationship with the decisions quality. This is the most important reason that why the endeavor for enhancement the forecasting accuracy has never been stopped in the literature. Electricity load forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of the electricity load is essential for precise planning and implementation of the necessary policies. For this reason electricity load forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult for forecasting in comparison with other commodities is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets. Computational intelligence and soft computing approaches are among the most precise and useful approaches for modeling the complexity and uncertainty in data, respectively. In the literature, several hybrid models have been developed in order to simultaneously use unique advantages of these models. However, iterative suboptimal meta-heuristic based models are always used for combining in these models. In this paper, a direct optimum parallel hybrid (DOPH) model is proposed based on multilayer perceptrons (MLP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to electricity load forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems in a direct and optimal structure. It can be theoretically demonstrated that the proposed model due to use the direct optimal structure, can achieve non-less accuracy than iterative suboptimal hybrid models, while its computational costs are significantly lower than those hybrid models. Empirical results indicate that the proposed model can achieve more accurate results rather than its component and some other seasonal hybrid models.

Suggested Citation

  • Chahkoutahi, Fatemeh & Khashei, Mehdi, 2017. "A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting," Energy, Elsevier, vol. 140(P1), pages 988-1004.
  • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:988-1004
    DOI: 10.1016/

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    2. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    3. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
    4. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    5. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    6. 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.
    7. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
    8. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    9. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    10. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    11. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    12. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    Full references (including those not matched with items on IDEAS)


    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:eee:energy:v:140:y:2017:i:p1:p:988-1004. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.