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Inventi Rapid - Artificial Intelligence

Articles

  • Inventi:eai/27954/19
    LEVENBERG-MARQUARDT (LM) ALGORITHM BASED STLF NEURO-WAVELET MODEL
    D A Kapgate*, Soni Chaturvedi, P S Raut

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

    How to Cite this Article
    D A Kapgate, Soni Chaturvedi, P S Raut. Levenberg-Marquardt (LM) Algorithm Based STLF Neuro-Wavelet Model. Inventi Rapid: Artificial Intelligence, 2019(2):1-6, 2019.
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