特征空间闭操作驱动的短期电力负荷预测

为了获得更高的短期负荷预测精度,有必要充分考虑负荷变化趋势与区域整体用电行为模式之间的关联,提出一种特征空间闭操作驱动的短期电力负荷预测方法。在综合模型的基础上,首先利用特征提取模型将历史用电负荷分解成多个分量作为刻画区域用电行为的特征;然后使用特征选择模型对用电行为特征进行选择,减少冗余或无效特征的干扰,优化预测模型;最后将选择的特征子集作为预测模型的输入特征从而进一步估计出各时段负荷的分布。结果表明采用本方法预测精度更高。

Short-term Power Load Forecasting Driven by Feature Space Closed Operation

To obtain higher short-term load forecasting accuracy, it is necessary to fully consider the relationship between load variation trend and regional overall power consumption behavior pattern, so in this paper a short-term power load forecasting method driven by feature space closed operation is proposed. With this method we first divide the historical load data into multiple components to describe the characteristics of the electricity consumption behavior of the delineated area; then, according to the regional load change trend, the characteristics of the power consumption behavior are selected, the redundant input features are eliminated, and the prediction model is optimized; finally, the remaining input features are brought into the prediction model to further estimate the distribution of the load in each period. The results show that the prediction accuracy of the proposed method is higher.