基于Softmax概率分类器的数据驱动空间负荷预测

提出了一种数据驱动空间负荷预测方法。将网格化体系下的功能地块作为空间负荷预测的基本单元,并且通过多维指标体系进行属性描述。基于大量调研数据,通过数据挖掘方法对不同类型地块的空间负荷密度分布规律和负荷曲线典型形态进行提取。建立Softmax多元概率分类模型对未知地块的负荷水平类型进行匹配。自下而上对相邻地块负荷预测结果进行时域叠加,得到更大区域的预测信息,包括其负荷量和预测负荷曲线。算例仿真结果表明提出的空间负荷预测方法在预测精度上有一定提升。

Data-driven Spatial Load Forecasting Method Based on Softmax Probabilistic Classifier

A data-driven spatial load forecasting(SLF)method based on Softmax probabilistic classifier is proposed. The functional land plots in the grid system are used as SLF units of spatial load forecasting and the attribute is described through the multi-dimensional indicator system. Based on a large amount of research data, the law of spatial load density distribution and typical shape of load curve of different land plot types are extracted by data mining method. The Softmax probabilistic classifier is introduced to forecast load levels of unknown land plots. Bottom-up superposition for load forecasting results of adjacent land plots in time domain is conducted, which obtains forecasting information of larger area including load levels and forecasted load curve. The simulation results of the example show that the proposed spatial load forecasting method has a certain improvement in forecasting accuracy.