基于混合自回归滑动平均潜周期模型的短期电价预测

应用混合自回归滑动平均潜周期模型对短期电价序列进行了预测.对消除了趋势影响的电价序列,经离散傅里叶变换转换为复值潜周期模型,采用一种简单的周期图检测方法计算电价序列的周期特征参数.为了计及历史信息对当前状态的影响,采用自回归滑动平均模型拟合残差随机分量,采用赤池信息准则确定模型的阶数,参数则由矩估计得到.该模型不要求预先假设电价序列的周期尺度,周期的个数和大小由模型计算确定,方法简单.采用美国宾夕法尼亚、新泽西、马里兰电力市场的实际电价数据对模型进行了检验,验证了模型的有效性.

Short Term Forecast of Electricity Price Based on Mixed Autoregressive Moving Average and Hidden Periodic Model

The mixed autoregressive moving average(ARMA) and hidden periodicity model is chosen to predict a short term series of electricity price.Eliminating the trend influence,a complex hidden periodicity model of price sequences can be obtained with discrete Fourier transformation.The periodicity parameters of price sequences are calculated by a simple detection for periodogram.To take consider the impact of historical information on the present states into account,the ARMA model is used to fit the residual stochastic component.The Akaike's information criterion is employed to determine the number of order in autoregressive moving average model,whose parameters are estimated by moment approach,where the pre-assumption on periodicity scale gets unnecessary.The number and size of periodicities is only derived from a simple computation model.The model proposed is verified by actual data of electricity price in PJM power market in USA.