基于主成分分析和BP神经网络的能源供需安全研究

基于主成分分析法,运用Matlab7.0软件中的BP神经网络工具箱对1985～2007年我国的能源供需安全状况进行了训练及测试.测试结果表明,主成分分析与BP神经网络的结果较吻合,BP神经网络运用于能源安全领域可行、有效.同时,得出自1985年以来我国能源供需安全状况大体上逐年提高,从2005年开始大幅提高,但仍未摆脱较差的境地,未来前景不容乐观.     

基于多源数据融合策略的配电网停电故障分析

配电网停电原因比较复杂,为精准地对停电故障进行预警,需采用大数据分析方法从多视角来深入剖析停电事件,尝试全景分析停电诱因及其对配电网影响程度的变化。因此,提出了一种简单易行的基于多源数据融合策略的配电网停电故障分析方法。方法首先融合停电池数据、配电网运行数据及气象数据来构建停电场景,然后采用成熟的主成分分析和逻辑回归来区分停电场景和其对应的非停电场景间的差异,进而发现配电网停电故障发生前的明显征兆。     

基于PSR模型的陕西省水资源承载力熵权法评价

为评价陕西省水资源承载力状况,基于PSR模型构建了水资源承载力综合评价指标体系,利用主成分分析法,借助SPSS20.0软件提取水资源承载力变化的主成分,引入熵权法计算三个主成分的权重,从而进行综合得分评价。结果表明,2009~2013年陕西省水资源承载力综合得分为0.142、0.171、0.206、0.231、0250,该分值逐年增加,表明水资源承载力呈现增长的态势,但增长速率逐年减少,说明水资源承载潜力下降,分析结果与现实情况相吻合。     

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.