Abstract: The classification method for power load data of new energy grid based on improved OTSU algorithm is studied to improve the classification accuracy of power load data. According to the idea of two-dimensional visualization of time series, GAF (Geographical Adaptive Fidelity) method is used to transform the current data of power load in new energy grid into two-dimensional image of power load in new energy grid. The intra class dispersion is introduced, and the improved OTSU algorithm is used to segment the foreground and background of the two-dimensional image according to the pixel gray value of the two-dimensional image and the one-dimensional inter class variance corresponding to the pixel neighborhood gray value. The two-dimensional foreground image of power load is taken as the input sample of convolution neural network. The convolution neural network extracts the features of the two-dimensional foreground image of power load through convolution layer. According to the extracted features, the classification results of power load data of new energy grid are output through three steps: nonlinear processing, pooling processing and full connection layer classification. The experimental results show that this method can accurately classify the power load data of new energy grid, and the classification accuracy is higher than 97%.

Xun Ma, Kai Liu, Anlei Liu, Xuchao Jia and Yong Wang, “Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131063

@article{Ma2022,
title = {Classification Method for Power Load Data of New Energy Grid based on Improved OTSU Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131063},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131063},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Xun Ma and Kai Liu and Anlei Liu and Xuchao Jia and Yong Wang}
}