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    Volume 42, Issue 6 (October 2014)

    Modeling Potential Future Energy Demand for Turkey in 2034 by Using an Integrated Fuzzy Methodology

    (Received 28 March 2014; accepted 26 August 2014)

    Published Online: 15 October 2014

    CODEN: JTEVAB

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    Abstract

    Since the decisions made regarding the future include uncertainty for operations, alternative predictions are needed to be developed in such decision-making processes. Accurate forecasting is a great help for companies in making the best decisions in terms of unit commitment, production, and maintenance planning. It is necessary for the companies to have prior foresight of future demand with adequate accuracy. Some data mining algorithms play the greatest role in predicting the demand forecasting. As a regular data-driven method, artificial neural networks (ANNs) are popular in energy forecasting. This paper investigated the application of the adaptive network based fuzzy inference system (ANFIS) as a forecasting tool for predicting the energy demand in Turkey. The benefit of the proposed model is forecasting energy needs through the evaluation of ANFIS application using data sets processed with principle component analysis (PCA) and collected from the energy forecast shootout (EFS) and Ministry of Natural Resources Turkey. The results showed that the hybrid ANFIS model based upon fuzzy logic (FL) and ANN performed efficiently in term of forecast accuracy. Thus, it could be regarded as an alternative method in energy forecasting. Finally, the application of ANFIS in a long term energy forecasting was provided, and the results were interpreted.


    Author Information:

    Balin, Abit
    Department of Industrial Engineering, Yildiz Technical Univ., Besiktas, Istanbul,

    Baracli, Hayri
    Department of Industrial Engineering, Yildiz Technical Univ., Besiktas, Istanbul,


    Stock #: JTE20140140

    ISSN:0090-3973

    DOI: 10.1520/JTE20140140

    Author
    Title Modeling Potential Future Energy Demand for Turkey in 2034 by Using an Integrated Fuzzy Methodology
    Symposium ,
    Committee E11