Wind Power Forecasting Using LSTM Incorporating Fourier Transformation based Denoising Technique

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    Abstract

    Forecasting of wind power is necessary to remove the system operational uncertainties via ensuring more reliable inputs for power system scheduling and control. In order to achieve accurate forecast of wind power for up to 50 seconds (very short-term forecasting), this paper proposes the method Fourier Denoising combined with Long Short-Term Memory (FD-LSTM). The FD-LSTM cascades the output of the Fast Fourier Transform (FFT)-based denoising algorithm to the input of the Long Short-Term Memory (LSTM) forecaster. In this method, first, the inclusion of the FFT-based denoising algorithm ensures better and balanced performance for different forecasting horizons by removing the frequencies. Next, short-term forecasting by employing LSTM reduces the uncertainty and improves both the quality of the operation and the planning. In this paper, we first evaluate the performance of the proposed FD-LSTM method on increased forecasting horizons based on Mean Square Error (MSE), Mean Absolute Error (MAPE), and R-Squared, and compare the results against linear regression. Afterwards, the method is tested with data sets where false data is present. The results show that FD-LSTM outperforms the other forecasting methods under the presence of false data.
    Original languageEnglish
    Title of host publicationProceedings of Wind Integration Workshop 2021
    Number of pages5
    Publication date2021
    Publication statusPublished - 2021
    Event20th Wind Integration Workshop 2021 - Virtual event, Berlin, Germany
    Duration: 29 Sept 202130 Sept 2021

    Workshop

    Workshop20th Wind Integration Workshop 2021
    LocationVirtual event
    Country/TerritoryGermany
    CityBerlin
    Period29/09/202130/09/2021

    Keywords

    • Fourier transform
    • Long short-term memory
    • Wind power
    • Forecasting
    • False data

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