基于时间序列模型的电价预测方法

Electricity price forecasting solution based on time series models

通过对美国PJM电力市场2006年8月到11月的日前电价的分析研究,提出了一种基于时间序列的自回归积分滑动平均模型(ARIMA)及自回归条件异方差(ARCH)模型和神经网络的组合模型来预测美国PJM电力市场未来24小时的日前电价,季节性ARIMA模型反映了电价趋势性、季节性,ARCH模型反映了电价的异方差性,因此该模型能够很好地反映电价的特点,预测结果良好,应用前景广阔。

Based on the day-ahead price research on the United States PJM electricity market from August to November in 2006,this paper proposes a ARIMA, ARCH and combination model of neural network based on the time series to predict the day-ahead price in future 24 hours of United States PJM electricity market, seasonal ARIMA model reflects the price trend, and seasonal, ARCH models reflect the price＇s heteroscedasticity, therefore the model can reflect the characteristics of electricity price better, predicted results is excellent and there is broader application prospects.