基于Haar小波与ARIMAX模型进行短期负荷预测

为了提高对非稳态负荷的预测精度,提出了基于Haar小波分析和ARIMAX模型的短期负荷预测方案。首先,通过Haar小波将高频信息序列与低频信息序列分别从电价与负荷序列中分解出来;其次,分别利用电价序列的高、低频序列对负荷序列的高、低频序列进行ARIMAX模型构建和预测;最后,将含有电价信息的高、低频负荷预测值进行Haar小波重构,得到负荷序列的预测值。通过实例验证表明,本文采用ARIMAX模型添加的电价信息,弥补了多次预测产生的误差,对短期负荷的预测精度高于传统时间序列方法。

Electric Load Forecasting Based on the Haar Wavelet Analysis and an ARIMAX Model for Electric Information

To improve the prediction accuracy of unstable electric load time series, a prediction method was proposed based on the decomposition of Haar wavelet and the coordination of ARIMAX model. First, the high and low frequency information sequences were separated by the Haar wavelet decomposition from the mixed information sequence, which consists of the informations of electricity price and electric load. Then, the high and low frequency sequences of the electricity price series were used to establish an ARIMAX model for prediction. Finally, the historic load series was obtained by the Harr wavelet reconstructian according to the prediction values, resulted from the ARIMAX analysis, and the results were compared with the traditional time series. The practical use of the method presented shows that it is helpful to increase the prediction accuracy of the instable load time series.