Asset Benefit Evaluation Model for Power Grid Enterprises using CNN Model

With the development of smart grid technology, asset management of power grid enterprises is gradually moving towards data-driven and intelligent directions. This article proposes an asset benefit evaluation model for power grid enterprises based on Convolutional Neural Network (CNN). By processing and analyzing the asset data of power grid enterprises and utilizing the powerful feature extraction capability of CNN, the evaluation model can accurately predict the benefit level of power grid assets. One of the basic performance evaluation experiments shows that the Mean Squared Error (MSE) of the CNN model is 0.0105. Second, compared with traditional methods, the MSE of CNN is significantly lower than that of algorithms such as linear regression. In the long time series prediction experiment, the MSE of the CNN model is 0.0098. Finally, when the noise level increases from 0.1 to 0.5, the MSE of the CNN increases from 0.012 to 0.041. In the above experimental data conclusions, the CNN model has shown higher prediction accuracy and robustness in asset benefit evaluation of power grid enterprises, especially in low noise levels and long time series prediction, with significant advantages. This study paves the way for the adoption of deep learning in power grid asset management, promising substantial improvements in efficiency and accuracy for future applications.