基于模糊聚类和粗糙集的电力系统短期负荷预测

电力系统短期负荷预测中存在大量的不确定因素直接影响到负荷预测结果的准确性,而粗糙集理论能有效地分析和处理各种不精确、不一致、不完整的信息,并从中发现隐含的知识,揭示潜在的规律。将模糊聚类分析和粗糙集理论结合在一起对电力系统短期负荷进行预测。首先通过模糊聚类分析,根据气温、相对湿度以及日类型等影响负荷的因素将负荷历史数据分成若干类,然后应用粗糙集理论分别建立相应的负荷预测模型。采用某电网提供的数据进行负荷预测,结果分析表明该方法有很高的预测精度,从而说明了基于模糊聚类和粗糙集理论的电力系统短期负荷预测方法的优越性。

Power system short-term load forecasting based on fuzzy clustering analysis and rough sets

In order to get more accuracy load forecasting result in electric power system which exits large uncertain information,rough set theory is proposed to deal with inaccuracy and uncertain information that affects the accuracy of load forecasting result in this paper.Implied knowledge and rules can also be discovery and excavated by using rough set.A short-term electrical load forecasting model based on fuzzy clustering and rough set is presented in this paper.At first,by means of fuzzy clustering the historical data is divided into several groups according to similarity daily characters,for example,temperature,humidity and day type etc.Then the corresponding load forecasting model based on rough set is built.The satisfactory accurate result can be gotten by applying this model in the practical electricity load forecasting,which shows the superiority of combining fuzzy clustering and rough set for short-time electrical load forecasting.