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Inventi Impact - Artificial Intelligence

Articles

  • Inventi:eai/24921/18
    WAVENET MODEL FOR SHORT TERM LOAD FORECASTING
    D A Kapgate*, P S Raut

    As the accuracy of the electricity load forecast is crucial in providing better cost-effective risk management plans, this paper proposes a Short-Term Electricity Load Forecast (STLF) model with a high forecasting accuracy. A cascaded forward BPN neuro-wavelet forecast model was adapted to perform the STLF. The model was composed of several neural networks whose data were processed using a wavelet technique. The data used in the model was electricity load historical data. The historical electricity load data was decomposed into several wavelet coefficients using the Discrete wavelet transform (DWT). The wavelet coefficients were used to train the neural networks (NNs) and later, used as the inputs to the NNs for electricity load prediction. The Levenberg-Marquardt (LM) algorithm was selected as the training algorithm for the NNs. To obtain the final forecast, the outputs from the NNs were recombined using the same wavelet technique.

    How to Cite this Article
    D A Kapgate, P S Raut. Wavenet Model for Short Term Load Forecasting. Inventi Impact: Artificial Intelligence, 2018(2):63-68, 2018.
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