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Application of Short-Term Load Forecasting Based on Improved Gray-Markov Residuals Amending of BP Neural Network

机译:改进BP神经网络的灰色马尔可夫残差修正法在短期负荷预测中的应用。

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For the characteristics of short-term load forecasting, we established load forecasting model based on BP neural network, combined the advantages of gray prediction and Markov forecasting, and make an amendment for the prediction residual, this has greatly improved the precision of prediction. Research has shown that neural network and gray - Markov residual error correction model has the value of popularization and application.
机译:针对短期负荷预测的特点,建立了基于BP神经网络的负荷预测模型,结合了灰色预测和马尔可夫预测的优点,并对预测残差进行了修正,大大提高了预测的准确性。研究表明,神经网络和灰色-马尔可夫残差校正模型具有推广应用价值。

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