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中文摘要：
根据非线性动力系统理论进行负荷建模和预测，并将预测精度作为一种辨识工具，用于分析电力负荷变化的动力特性。分析结果表明，可将负荷的变化特性描述为低维混沌系统。根据负荷的混沌特性及一步向前预测的精度提出一种优选重构参数的方法，并采用基于相空间重构的加权一阶局域法多步预测模型进行了负荷预测。相空间模型能识别负荷序列的内部特性并进行预测，因此是分析和预测负荷的有效工具。
英文摘要：
In this paper, nonlinear dynamical system theory is applied to the modeling and prediction of power load. As an identification tool, prediction accuracy is used to analyze dynamic characteristics of power load variation. Analysis results of load time series show that the variation of power load can be characterized as a low-dimensional chaotic system. According to chaotic characteristic of power load and the accuracy of one-step forward prediction, the authors propose a new method to implement optimal selection of reconstruction parameters, such as the best embedding dimension and delay time, and use weighted local-region multi-step forecasting model based on phase-space reconstruction to forecast short-term load. Because phase space model can identify the inherent characteristics of power load and can be used in load forecasting, the proposed method is effective in power load analysis and forecasting.
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