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Short-term load forecasting with chaos time series analysis

机译:混沌时间序列分析短期负荷预测

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This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behaviour. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear mapping of deterministic chaos is identified by the multi-layer perceptron of an artificial neural network. The proposed approach is demonstrated in an example.
机译:本文介绍了电力系统短期负荷预测的新方法。所提出的方法利用混沌时间序列分析,这是基于确定性混沌来捕获复杂负载行为的特征。确定性混沌允许我们重建时间序列并确定输入变量的数量。本文介绍了日常电力系统峰值负荷的混沌时间序列分析。确定性混沌的非线性映射由人工神经网络的多层映射识别。在一个例子中证明了所提出的方法。

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