Non-intrusive load identification method and system based on Alexnet neural network and color coding

The invention discloses a non-intrusive load identification method and system based on an Alexnet neural network and color coding in the technical field of power load identification. The method comprises the following steps: acquiring operation data of a load, and constructing a voltage-current track feature map of the load; performing preliminary identification on the operation data of the load based on an SVM clustering algorithm to obtain a preliminary load identification result; distinguishing the voltage-current track feature map and the preliminary load identification result by using RGB colors, and constructing a voltage-current track feature map with color distinguishing; and based on the trained Alexnet neural network, identifying the voltage-current track feature map with color distinguishing, and obtaining a load identification result. The method has the characteristics of high identification accuracy, small occupied calculation space and the like. 本发明公开了电力负荷识别技术领域的一种基于Alexnet神经网络和颜色编码的非侵入式负荷识别方法及系