基于黄金分割法优选的中长期负荷变权组合预测

为了提高中长期负荷预测的精度,避免单一的灰色模型预测和指数平滑法预测精度偏低的缺点,提出了基于黄金分割法优选的自适应变权组合预测方法。该方法首先对灰色预测方法和自适应三次指数平滑法进行了改进,以拟合值与实际值之间的相对误差绝对值之和最小为目标,利用黄金分割法优选出自适应三次指数平滑法的平滑系数,确定最优的三次指数平滑模型,然后以同样的方法确定灰色模型和自适应三次指数平滑法的权重。接着,对原始负荷数据进行新陈代谢,重复利用黄金分割法优选出新的平滑系数和各单一方法的权重,即可得到新的变权组合预测模型。仿真结果表明,所提出的自适应变权组合预测方法切实可行,与单一的灰色模型、三次指数平滑法及等权组合预测方法相比,有效地提高了中长期负荷预测的精度。

Variable Weight Combination Method for Mid-long Term Load Forecasting Based on Golden Section Algorithm

To improve the accuracy of mid-long term load forecasting, a variable weight combination forecasting method based on golden section algorithm is proposed, which avoids the shortcoming of single grey model and exponential smoothing method. Taking the minimum sum of absolute relative error between fitted value and true value as objective function, the golden section algorithm is used to select the optimal smoothing factor and the weight of grey model and self-adaptive cubic exponential smoothing method. With the power load data updated by metabolism, the new smoothing factors and weights of each single method are selected by golden section algorithm repeatedly. Then establish the new variable weight combination forecasting model. Results of simulation verify the feasibility of the proposed variable weight combination forecasting method. Compared with a single grey model, cubic exponential smoothing method or equal weight combination method, the accuracy of the mid-long term load forecasting is effectively improved.