ew 18: e16

Research Article

Day-ahead electricity price forecasting model based on artificial neural networks for energy markets

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  • @ARTICLE{10.4108/eai.23-12-2020.167660,
        author={S. Anbazhagan and Bhuvaneswari Ramachandran},
        title={Day-ahead electricity price forecasting model based on artificial neural networks for energy markets},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={12},
        keywords={Autocorrelation, back propagation neural network, Deregulated electricity market, Leverberg-Marquardt learning},
        doi={10.4108/eai.23-12-2020.167660}
    }
    
  • S. Anbazhagan
    Bhuvaneswari Ramachandran
    Year: 2020
    Day-ahead electricity price forecasting model based on artificial neural networks for energy markets
    EW
    EAI
    DOI: 10.4108/eai.23-12-2020.167660
S. Anbazhagan1,*, Bhuvaneswari Ramachandran2
  • 1: Department of EEE, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, India 608002
  • 2: Department of Electrical and Computer Engineering, University of West Florida, Pensacola, FL 32514, USA
*Contact email: s.anbazhagan@gmx.com

Abstract

Day-ahead electricity price forecasting is still an open problem in electricity markets. One major method is used in solving this problem is artificial neural networks (ANN). But they are usually trained slowly and need large numbers of patterns. NN trained using Levenberg-Marquardt (LM) learning is proposed and partial autocorrelation is applied on time series data to get correct input values. The functionality of the NN-LM is higher than the traditional ANN and some other hybrid approaches. To show the effectiveness and accuracy of the NN-LM method, the Indian and the Austrian energy exchange markets are considered. It is significant to note that for the very first time, the NN-LM based approach is being tested on both the energy markets. Finally, the flexibility of the proposed approach is checked using a 4-fold cross-validation technique. The 4-fold cross-validation strategy is capable of improving the generalization ability of the model and accomplishing higher forecast precision.