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Short-term photovoltaic output prediction based on advanced prediction error NGA-ELMAN cascade neural network
  • Zhaoke Wang
Zhaoke Wang

Corresponding Author:[email protected]

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Improving the accuracy of photovoltaic power prediction is crucial for grid scheduling planning and is essential for the safe, stable, and economic operation of power systems. Based on the statistical characterization of the data, a two-stage PV power prediction model with error correction is developed. First, an Elman neural network model optimized by a small habitat genetic algorithm is introduced; subsequently, a more accurate model for the preliminary prediction error probability distribution is established, based on its distribution characteristics. This model aims to achieve error correction of the preliminary prediction results. The empirical results, derived from actual PV power curves and meteorological data, demonstrate the effectiveness of the proposed method.
01 Feb 2024Submitted to Electronics Letters
08 Feb 2024Assigned to Editor
08 Feb 2024Submission Checks Completed
08 Feb 2024Review(s) Completed, Editorial Evaluation Pending
08 Feb 2024Reviewer(s) Assigned