基于短期相关性和负荷增长的中长期负荷预测

Short-term Correlation and Annual Growth Based Mid-long Term Load Forecasting

现有中长期负荷预测非线性模型存在预测困难及精度偏低且不稳定的问题。文中提出了一种基于短期相关性和年度负荷增长的预测方法,将非线性问题转化为线性问题来解决。该方法首先根据上一年相邻点和相邻周负荷之间的短期相关性构建线性回归模型;然后采用递归的方法计算出下一年各周所有负荷点的预测值;最后考虑年度负荷增长,对预测值进行修正得到最终预测结果。结合实际电网数据验证了该方法的有效性和实用性,为中长期负荷预测提供了一条可行的新思路。

As the existing nonlinear models of mid-long term load forecasting are fairly difficult to apply and their results are not satisfactory, a novel method is presented, which transforms a nonlinear issue into a linear one based on short-term correlation and annual growth. First, linear regression models are constructed in terms of the strong short-term correlation of the preceding year＇s load. By using a recursive procedure, the weekly average load is then estimated for the next year. Finally, the predicted annual load growth is taken into consideration to modify the estimated values. The validity and practicability of the method proposed are tested with actual data. It is expected that this approach can provide a new feasible solution for mid-long term load forecasting.