基于信息量和紧凑性的短期电力负荷预测训练选择方法

2018 Dynamics of Systems, Mechanisms and Machines (Dynamics) Pub Date : 2018-11-01 DOI:10.1109/DYNAMICS.2018.8601454

V. Potapov, R. Khamitov, V. Makarov, A. Gritsay, A. Florensov, Denis Tyunkov

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引用次数: 3

摘要

本文研究了基于人工神经网络的电力负荷智能预测方法训练样本的形成方法。训练样本的形成考虑了信息量和紧凑性的标准。结果表明，采用这种方法可以提高预报的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Training Selection Method for Short-Term Prediction Electricity Loads with Criteria of Informativeness and Compactness

The paper considers the method of forming a training sample for intelligent methods of predicting electricity loads based on artificial neural networks. The training sample is formed taking into account the criteria of informativeness and compactness. It is shown how much the accuracy of the forecast can be increased with the approach used.

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来源期刊

2018 Dynamics of Systems, Mechanisms and Machines (Dynamics)

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