基于小波分析与广义自回归条件异方差模型的短期电价预测

由于电价波动具有非线性及波动集群现象，因此提出了一种基于小波分析和广义自回归条件异方差模型相结合的短期电价预测新方法。首先应用小波分解原理将电价序列分解成低频部分和高频部分，在此基础上对各子序列分别建立广义自回归条件异方差模型并进行预测；然后利用小波理论对各子序列的预测结果进行重构，实现对原始电价序列的预测；最后以美国加州电力市场历史数据为例进行了验证，结果表明本文方法是可行和有效的。

Short-Term Electricity Price Forecasting Based on Wavelet Transform and Generalized Autoregressive Conditional Heteroskedasticity Model

Based on the nonlinear and volatility clustering phenomenon of electricity price fluctuation, a new approach to forecast short-term electricity price, which is based on the integration of wavelet analysis with generalized autoregressive conditional heteroskedasticity (GARCH) model, is proposed. Firstly, by use of wavelet decomposition, the proposed approach divides the electricity series into low frequency part and high frequency part; secondly, on this basis the GARCH models for each sub-series are established respectively to carry out the forecasting; thirdly, the forecasting results of sub-series are reconstructed by wavelet theory to implement the forecasting for original electricity price series; finally, taking historical data of California electricity market for example, the proposed forecasting approach is verified. Verification result shows that the proposed forecasting approach is feasible and effective.