摘要
多目标遗传优化算法的一个优点就是可在一次迭代计算中寻找到问题的多个非劣最优解。该文应用多目标遗传算法和关联规则算法提出一个基于模糊规则的电力负荷模式分类系统。在此分类系统中采用多目标遗传优化算法从众多模糊分类规则中自动挑选出具有较好识别性能和可解释性的模糊规则,并利用模糊关联规则挖掘通过启发式规则选择改善遗传算法的搜索性能。经仿真试验表明此分类系统具有较好的分类性能,可为节假日负荷预测提供更为充分的历史数据,从而改善其负荷预测性能。
One advantage of multi-objective genetic optimization algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we proposed a fuzzy rule-based classifier for electrical load pattern classification by using multi-objective genetic algorithm and fuzzy association rule mining. Multi-objective genetic algorithm is used to automatically select the rules with better classification accuracy and interpretability, and the key concepts of fuzzy association rule mining are the bases of heuristic rule selection for improving the performance of genetic algorithm searching. Through computation experiments on a real power system, it is shown that the generated fuzzy rule-based classifier leads to high classification performance, and can supply more sufficient historical data for load forecasting of anomalous days, better performance of load forecasting is gained accordingly.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第10期29-34,共6页
Proceedings of the CSEE
关键词
电力系统
负荷预测
人工神经网络
模糊多目标遗传优化算法
仿真
Electric power engineering
Power system
Fuzzy rule-based classifier
Multi-objective genetic algorithm
Association rule mining
Load forecasting
