【摘要】 短期负荷预测对于电力系统安全、稳定、经济地运行有重要意义。将粗糙集信息熵理论和统计学主成分分析方法用在负荷事例属性的约简上,分别针对负荷数据的重要性和相关性进行了有效处理。这样,不仅减少了事例重用过程的训练时间,还有效控制了次要负荷因素对重要因素的干扰;在事例修正过程中,针对非正常日提出一些有效的修正方案。最后,用河北省保定供电公司2000~2004年的负荷数据对该方案验证,结果表明,提出的预测方案是有效,可行的。更多还原

【Abstract】 Short-term load forecasting(STLF) plays a vital part in the operation of electric power system,and it relates the security,stability,and economic dispatch of the system.In this paper,rough sets information entropy and principal component analysis were applied to the attributes reduction of load cases,and respectively,the significance and relativity of load data were disposed.Thus,not only the training time in the process of retrieval was decreased,but also effective control was implemented aiming at petit factors to essential ones.In the process of revise,some impactful amendments are presented to improve prediction precision.Finally,this scheme was performed on the data 2000-2004 of Baoding Electric Power Company(BDEPC),and the test results showed that the proposed model was feasible and promising for load forecasting.更多还原