基于Res-MobileCom并行网络的光伏发电功率等级分类

doi:10.3969/j.issn.2097-0706.2026.01.001

摘要/Abstract

摘要：

为了提高光伏发电功率等级分类准确性以适应行业要求，提出了一种基于Res-MobileCom并行网络的分类模型。在数据处理中使用去归一化的双线性插值法最大限度保留数据特征，然后通过简化残差网络（ResNet）和用于移动视觉的高效卷积神经网络（MobileNet）结构并行训练后将其输出联合输入信道估计-信号检测网络（ComNet）中进一步提取数据特征，最终得到分类结果。试验结果表明：相比于常见的深度学习模型，Res-MobileCom模型保持了ResNet和MobileNet的特征提取能力和轻量性，模型具备较好的平衡性和泛化能力；采用去归一化双线性插值法和进一步提取数据特征的ComNet后，模型准确率提高了10百分点以上，为提高光伏发电功率等级分类模型的准确率提供了新的方法和思路。未来工作将围绕稳定性优化、跨任务验证及工程化部署展开。

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

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.

Key words: photovoltaic power level classification, deep learning, residual network, mobile network, ComNet, lightweight, parallel network

中图分类号:

TK51：TP393

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.

YIN Linfei, ZHOU Yanggang. Photovoltaic power level classification based on Res-MobileCom parallel network[J]. Integrated Intelligent Energy, 2026, 48(1): 1-12.

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Function模块消融试验结果

Function模块形式	测试集准确率
$1 0.1 + x$	51
$1 0.1 + x + R$	63

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参考文献 30

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功率等级	平均功率	功率等级	平均功率
f1	239.220	f6	3 750.000
f2	396.000	f7	2 000.000
f3	397.870	f8	500.000
f4	332.395	f9	6 000.000
f5	201.140

模型	Letterbox	双线性插值法	去归一化双线性插值法
InceptionV1	0.111 0	0.111 0	0.099 9
InceptionV2	0.111 1	0.111 0	0.113 7
Inception-ResNetV1	0.111 0	0.111 0	0.115 1
Inception-ResNetV2	0.111 0	0.111 0	0.113 7
ShuffleunitNet	0.111 1	0.111 0	0.119 3
SqueezeNet	0.111 0	0.111 0	0.099 9
DenseNet121	0.111 1	0.111 0	0.221 9
ResNet18	0.111 1	0.111 0	0.567 1
ResNet50	0.111 0	0.111 1	0.620 0
ResNet101	0.111 1	0.111 1	0.636 1
MobileNetV1	0.111 1	0.111 0	0.111 0
MobileNetV2	0.111 0	0.111 0	0.155 3
MobileNetV3	0.111 1	0.111 1	0.111 0

参数	数值
学习率	0.01
批次	32
输入通道数	1
输入数据尺寸	224×224
损失函数	交叉熵损失函数
优化器	随机梯度下降优化器

模型	准确率	精确率	召回率	F1分数
InceptionV1	0.099 9	0.010 0	0.099 9	0.018 1
InceptionV2	0.113 7	0.012 9	0.113 7	0.023 2
Inception-ResNetV1	0.115 1	0.125 2	0.115 1	0.032 4
Inception-ResNetV2	0.113 7	0.012 9	0.113 7	0.023 2
ShuffleunitNet	0.119 3	0.014 2	0.119 3	0.025 4
SqueezeNet	0.099 9	0.010 0	0.099 9	0.018 1
DenseNet121	0.221 9	0.062 7	0.221 9	0.096 7
ResNet18	0.567 1	0.564 9	0.567 1	0.549 2
ResNet50	0.620 0	0.616 7	0.620 0	0.597 6
ResNet101	0.636 1	0.634 2	0.636 1	0.599 6
MobileNetV1	0.111 0	0.012 3	0.111 0	0.022 2
MobileNetV2	0.155 3	0.085 4	0.155 3	0.107 5
MobileNetV3	0.111 0	0.012 3	0.111 0	0.022 2
Res-MobileComNet	0.726 8	0.734 4	0.726 8	0.721 3

模型	准确率
LightResNet	0.636 6
LightMobileNetV3large	0.632 5
Res-MobileCom	0.726 8