Survey on short-term load forecasting using hybrid neural network techniques

  • Authors

    • Shaive Dalela
    • Aditya Verma
    • A L.Amutha
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10486
  • Short-term load forecasting, Artificial Neural Network (ANN), Fuzzy Logic (FL), Genetic Algorithm (GA), Support Vector Machine (SVM), Particle Swarm Optimization (PSO).

  • Abstract

    Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown.  The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data

  • References

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  • How to Cite

    Dalela, S., Verma, A., & L.Amutha, A. (2018). Survey on short-term load forecasting using hybrid neural network techniques. International Journal of Engineering & Technology, 7(2.8), 464-467. https://doi.org/10.14419/ijet.v7i2.8.10486

    Received date: 2018-03-22

    Accepted date: 2018-03-22

    Published date: 2018-03-19