基于时序数据-BP神经网络的电力能耗分析研究

为了更好地对电厂中机组的能耗问题进行分析研究,根据电厂不同工况的参数指标,建立电力企业能耗仿真BP神经网络模型。归一化处理电厂运行过程中各类传感器采集的数据,采用负荷、环境温度、排烟温度、背压、含氧量等指标,并加入时序历史能耗作为输入参数;利用电力企业短时能耗作为输出参数;通过采用不同时间窗口的连续时序能耗参数指标和热力学相关参数作为输入,在神经网络中不同中间层的隐层节点数下进行仿真实验。结果表明,基于时序历史能耗数据的电厂指标参数在包含21个隐层节点数的BP神经网络模型上能够在线仿真出精度较高的短时供电能耗数据。所建的电力能耗预测模型将为后续电厂的节能减排、负荷优化,提供理论参数支撑。     

Track Association for System Based Dynamic Target Tracking on AP Algorithm

Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system, and its accuracy directly impacts on the performance of the whole tracking system. A multi-sensor data association is proposed based on aftinity propagation （AP） algorithm. The proposed method needs an initial similarity, a distance between any two points, as a parameter, therefore, the similarity matrix is calculated by track position, velocity and azimuth of track data. The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index. Furthermore, the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target. Finally, the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics, then filtering and target tracking are conducted. The experimental results show that the proposed method can ef- fectively achieve multi-sensor and multi-target track association.