摘要
广义自回归条件异方差(GARCH)模型具有描述时间序列波动性的能力。文中将GARCH模型应用于短期负荷预测研究。通过分析经典时间序列模型随机扰动分量,论证了自回归条件异方差(ARCH)效应的存在性,探讨了经典模型同方差假设不满足的问题。进一步建立了GARCH模型,将时间序列的条件与方差纳入负荷预测模型,使用极大似然估计(MLE)获得模型参数估计。最后从实际预测效果的角度,将GARCH模型与经典模型进行了比较。实际算例结果表明该方法相比于经典模型有更贴近实际的理论假设和更强的预测能力。
The generalized autoregressive conditional heteroscedasticity （GARCH） model has the ability to describe the volatility of time series, A feasible approach to short-term load forecasting based on GARCH methodology is given. By analyzing the random disturbance series of the classical time series model, the existence of autoregressive conditional heteroscedasticity （ARCH） effect is demonstrated by the Lagrange multiplier （LM） test, and the non-satisfaction of homoscedasticity of the classical model is discussed. Furthermore, an improved autoregressive moving average （ARMA）-GARCH model is built, in which the conditional variance equation is included and the estimation of model parameters is obtained by maximum likelihood estimation （MLE）, A comparison between the improved model and the classical ARMA model is made by taking into account the actual forecasting effect. The results show that the new model has more practical preconditions and better forecasting performance.
出处
《电力系统自动化》
EI
CSCD
北大核心
2007年第15期51-54,105,共5页
Automation of Electric Power Systems
