摘要
将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在此基础上,通过计算规则的信赖度和支持度形成不同层次上符合初定阈值的带粗糙集算子的网络规则集,能减少因噪音的影响而产生的多余规则,提高规则产生和实际分类的效率,使所产生的分类规则集大大缩小,提高在使用规则时检索规则的效率。在负荷预测时自上而下逐层搜索规则网直至找出与所给信息相匹配的规则。粗糙集算子反映了规则的重要程度,同时作为选择规则的标准。实际应用表明,该方法能有效去除噪音,提高默认规则的挖掘效率,从而提高负荷预测的精度,具有一定的实用性。
Here, the mining default rules based on rough set （MDRBR） is applied to power system short-term load forecasting. First, the conditional attributes such as temperature and humidity that affect load characteristics are discretized by rough set discretization algorithm based on Gini index, and the consideration is given to both conditional attributes and decision-making attributes. On this basis, through computing the confidence and support of rules the network rules set in different levels, which is accompanied with rough set operator and conforms to originally specified threshold, is generated, so the redundant rules brought about by the influence of noise can be reduced, so that the generated classification rules set can be evidently minified and the efficiency of retrieving rules can be improved while the rules are used. During the load forecasting the rules set is searched layer by layer from the top to the bottom until the rules that match with the information are found out. The rough set operator reflects the significance level of the nile, so it is used as the standard to choose rules. Case applications show that the presented method can effectively remove noise and improve the efficiency of mining default rules, therefore the accuracy of load forecasting can be improved.
出处
《电网技术》
EI
CSCD
北大核心
2006年第5期18-23,共6页
Power System Technology
基金
国家自然科学基金资助项目(50077007)。~~
