基于非参数GARCH模型的电价预测

基于非参数条件异方差估计理论,提出了一种改进的电价曲线预测方法。文中从实际电价曲线出发,针对条件方差函数建模,并采用非参数估计方法确定其模型。另外,在非参数估计中,针对条件标准差不可测困难,引入了迭代估计算法,通过不断修正作为输入量的条件标准差估计值来提高条件方差函数的估计可信度。在研究加州电力市场2000年日前电价时间序列波动特性的基础上,对Humb节点的日前电价时间序列进行建模并模拟预测。试验结果表明,文中所提模型能够更好地体现电价时间序列波动集群性这一特征,利用非参数估计所确定的模型提升了尖峰电价的预测效果。

Electricity Price Forecasting Based on Nonparametric GARCH

Based on nonparametric theory for conditional heteroskedasticity function, an improved method of electricity price forecasting is proposed. On the basis of real electricity price time series, conditional variance function is modeled for stochastic volatility, and the model is determined by means of non-parametric estimation. In the nonparametric estimation process, an iterate algorithm is introduced to overcome the problem that volatility is unobserved latent variable so that the confidence of estimated conditional variance function is weak. On the study of stochastic volatility of day-ahead electricity price in Humb spot in California, the forecasting is made. And the results of test show that the proposed method has the capability of forecasting electricity prices characteristic of volatility clustering, and improves the accuracy of price spikes forecasting.