综合智慧能源2026，Vol.48Issue(1)：1-12,12.DOI:10.3969/j.issn.2097-0706.2026.01.001
基于Res-MobileCom并行网络的光伏发电功率等级分类
Photovoltaic power level classification based on Res-MobileCom parallel network
摘要
Abstract
To enhance the accuracy of photovoltaic power level classification to meet industry requirements,a classification model based on a Res-MobileCom parallel network was proposed.In data preprocessing,denormalized bilinear interpolation was used to maximize the preservation of data features.Subsequently,through parallel training of simplified residual network(ResNet)and efficient convolutional neural networks for mobile vision(MobileNet),their outputs were jointly fed into the channel estimation-signal detection network(ComNet)for further data feature extraction,ultimately obtaining the classification results.The experimental results demonstrated that compared to common deep learning models,the Res-MobileCom model retained the feature extraction capability and lightweight nature of ResNet and MobileNet,exhibiting good balance and generalization ability.By using the denormalized bilinear interpolation method and the ComNet for further data feature extraction,the model accuracy improved by more than 10 percentage points,providing a novel approach and idea for improving the accuracy of photovoltaic power level classification models.Future work will focus on stability optimization,cross-task validation,and engineering deployment.关键词
光伏发电功率等级分类/深度学习/残差网络/残差网络/移动网络/ComNet/轻量化/平行网络Key words
photovoltaic power level classification/deep learning/residual network/mobile network/ComNet/lightweight/parallel network分类
能源科技引用本文复制引用
殷林飞,周扬钢..基于Res-MobileCom并行网络的光伏发电功率等级分类[J].综合智慧能源,2026,48(1):1-12,12.基金项目
国家自然科学基金项目(62463001)National Natural Science Foundation of China(62463001) （62463001）
