基于BP神经网络的负载识别和C语言实现

对学生公寓用电负载的类型进行识别,识别出负载中计算机功率的值,进而判断检测到的总功率中所含的非计算机功率,以限制大功率负载的应用,对学生公寓的用电管理有重大意义。对所采集不同负载类型的电压、电流信号进行傅立叶交换,提取特征值,并加入总功率参数作为BP神经网络的训练样本,训练网络对负载中计算机功率进行识别,并用C语言编程实现。对石家庄铁道学院学生公寓电器类型的识别结果表明,神经网络方法用于识别电器类型具有可靠性和实用性。

Application of Neural Network in recognizing the electrical appliances

Recognizing the load in the apartment of student and the computer power in the sum-power have important value to the management of students' apartment. The value of sum-power and different frequency what come from the fast Fourier transform to the voltage and the electric current signals of all kinds of loads as the input of the BP network and the power of computer as the output of the BP network were taken. The network to recognize the power of computer in the sum-power was trained. Then we carry out the system by C language. The results of recognizing the load of students' apartment show that the neural network used in recognizing the electrical appliances has reliability and practicability.