摘要
电能作为常见的能源类型,易与其他类型的能源进行转换,被广泛用于日常生活以及社会发展等方面。随着电力系统的不断发展,用户的不断突破,短期内的负荷用电预测成为国家电网稳定运行不可缺少的一部分。本文提出一种基于量子免疫优化算法改进的BP神经网络算法短期负荷预测方法,旨在提高BP神经网络算法存在的收敛速度慢、初始值敏感等问题,经某电力公司提供数据,对电力短期负荷进行预测,结果证明了本文提出方法的有效性与快速性。
As a common type of energy,electric energy is easy to convert with other types of energy,and is widely used in daily life and social development.With the continuous development of the power system and the continuous breakthrough of users,the short-term load forecasting has become an indispensable part of the stable operation of the state grid.In this paper,a short-term load forecasting method based on improved BP neural network algorithm based on quantum immune optimization algorithm is proposed to improve the problems of slow convergence speed and sensitive initial value of BP neural network algorithm.The short-term load forecasting is carried out with data provided by a power company.The results show that the proposed method is effective and fast.
作者
符亚杰
冯国成
FU Yajie;FENG Guocheng(Wuhai Vocational And Technical College,Wuhai Inner Mongolia 016000,China;North United Power Corporation,Wuhai Inner Mongolia 016000,China)
出处
《电子器件》
CAS
北大核心
2021年第3期659-663,共5页
Chinese Journal of Electron Devices
基金
内蒙古自治区教育厅科研项目(NJZY17583)。
关键词
短期负荷预测
量子免疫优化算法
BP神经网络算法
short term load forecasting
quantum immune optimization algorithm
BP neural network algorithm
