【摘要】 传统GM(1,1)模型在参数a的绝对值较小的情况下近期用电量预测精度较高,中期用电量预测往往误差较大,一定程度上是由于GM(1,1)模型的背景值x(1)(k)取前后2个时刻的平均值造成的。引入向量θ得背景值序列的精确计算式,将GM(1,1)模型推广为GM(1,1,θ)模型。应用微粒群优化这一智能算法求解最优向量,从而构建GM(1,1,θ)模型,并将该模型应用于山东省中期用电量预测。实例分析表明,与传统GM(1,1)预测模型相比,智能优化模型较好地得到了预测点的预测结果,更适用于中期用电量预测。更多还原

【Abstract】 The conventional GM（1,1） model is accurate in short-term electricity demand forecasting but has more errors in middle-term forecasting when the absolute value of the parameter a is relatively small.To some degree this is because that the background value x^（1）（k） of GM（1,1） is defined as the average of two sequent moments.The vector θ was introduced into the accurate calculation formula of background value array,and GM（1,1） was consequently generalized into GM（1,1,θ）.As particle swarm optimization has the virtue of optimum seeking,it was applied to solving the value of θ as well as the optimization model.The forecasting results demonstrate that the intelligent optimization model GM（1,1,θ） has higher forecast precision and adaptability for middle-term electricity demand forecasting.更多还原