基于卷积神经网络与特征融合的天气识别方法

在太阳能热水器及太阳能电池等太阳能发电领域，下雨、下雪、阴天等气候因素将严重影响发电效果，而太阳能随动系统工作也必须消耗能量，所以迅速判断当前的天气状况，并设计自适应的开关随动系统极其重要。当天气状况为阴雨或者雪天时，系统应当关闭从而减少能耗。鉴于传统的天气识别方法效率低、准确度差、计算量大的问题，在公开的天气图像基础上创建了一个具有多种类别的天气分类集，并提供了一种基于卷积神经网络与特征融合的天气图像识别技术。通过采用传统方式获取图像的颜色、纹理、形状3种特征作为整个模型的底层特征，在原本的VGG16(visual geometry group-16)模型基础上进行了改进，从而提取图像的深层特征，最后将底层特征与深层特征融合起来在Softmax上进行输出，总识别率达到94%。

Weather recognition method based on convolutional neural network and feature fusion

In the field of solar power generation such as solar water heaters and solar cells, the climate factors such as rain, snow and cloudy days will seriously affect the power generation effect, and the work of solar servo system must also consume the energy. Therefore, it is extremely important to quickly judge the current weather conditions and design an adaptive on-off servo system. When the weather is rainy or snowy, the system should be shut down to reduce the energy consumption. In view of the problems of low efficiency, poor accuracy and large amount of calculation of traditional weather recognition methods, a weather classification set with multiple categories on the basis of public weather images was created, and a weather image recognition technology based on convolutional neural network and feature fusion was provided. By using the traditional way to obtain the color, texture and shape of the image as the bottom features of the whole model, it was improved on the basis of the original visual geometry group-16 (VGG16) model, so as to extract the deep features of the image. Finally, the bottom features and deep features were fused and output on Softmax, and the total recognition rate is 94%.