摘要
影响电力短期负荷预测精度的因素众多,为了找到负荷值与各种外在因素之间的关系,提出了一种基于粗糙集理论的混合属性约简算法,并对与预测日相似性数据进行快速约简,讨论了基于混合属性约简和BP神经网络相结合的预测模型。实验结果表明,这种方法提高了短期电力负荷预测精度。
There are many factors that influence the accuracy of short power load forecasting.In order to find the relationship between the load value and the outside factors,this paper presents a fast hybrid attribute reduction algorithm for data reduction based on rough set,and then discusses the forecasting model using hybrid attribute reduction and the BP artificial neural network.The experiment results show the model improves the forecasting accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第30期234-236,240,共4页
Computer Engineering and Applications
基金
江苏省省属高校自然科学资金项目(No.09KJD520004)
关键词
混合属性约简算法
粗糙集
BP神经网络
短期负荷预测
hybrid attribute reduction algorithm
rough set
BP artificial neural network
short power load forecasting
