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Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

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  • Tuncay Özcan

Abstract

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.

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  • Tuncay Özcan, 2017. "Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 5(2), pages 329-338, December.
    • Handle: RePEc:anm:alpnmr:v:5:y:2017:i:2:p:329-338
    DOI: http://dx.doi.org/10.17093/alphanumeric.359942
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    References listed on IDEAS

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    More about this item

    Keywords

    Genetic Algorithm; Grey Prediction; Parameter Optimization; Short Term Load Forecasting;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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