基于相关邻近点与峰谷荷修正的短期负荷时间序列预测

Short-term Load Time Series Forecasting Based on Correlative Neighboring Points and Peak-valley Correction

采用混沌相空间重构理论进行电力短期负荷预测,存在峰谷荷预测精度相对较差和预测参考点不易选取的问题。根据电力系统日负荷曲线构造了日峰谷荷时间序列,揭示了日峰谷荷时间序列的混沌特性,采用相空间重构直接预测未来峰谷荷,进而提高了峰谷荷和整点负荷的预测精度。针对相空间中相点的预测参考点确定问题,提出了按相点演化相关性进行选择的方法,首先根据模型要求的预测参考点数量选出邻近点,然后根据相点演化相关性排除伪邻近点,同时引入时间权重来反映相空间不同坐标的时间次序。实际电网负荷预测的仿真结果验证了文中提出的相空间相关邻近点的选择方法与峰谷荷修正思想的有效性。

Selecting the reference points of current phase point and gaining higher forecasting accuracy of the peak-valley is important for short-term load forecasting based on phase space reconstruction. The daily peak-valley load time series, which is proved chaotic by fractal dimension and Lyapunov exponent analysis, is constructed based on the daily load curves. The load per hour is then corrected through peak-valley load prediction by the phase space reconstruction. An effective method composed of rough search and fine search is presented to choose reference points with the purpose of improving the forecasting precision of chaotic time series. The rough search is used to search some neighboring points according to the number of reference points required by forecasting model, the false neighboring points are kicked off through fine search in terms of the time evolution relativity, and the time weights is also introduced to consider the time sequence of different coordinates in the phase space. The simulation results of practical load forecasting show that the proposed method to select reference points and peak-valley 10ad correction are more effective.