Raw photovoltaic panel image

What is sky images & photovoltaic power generation dataset?

To fill these gaps, we introduce SKIPP’D—a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains three years (2017–2019) of quality-controlled down-sampled sky images and PV power generation data that is ready-to-use for short-term solar forecasting using deep learning.

How to detect photovoltaic cells in aerial images?

Recognition of photovoltaic cells in aerial images with Convolutional Neural Networks (CNNs). Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet. Create a Python 3.8 virtual environment and run the following command:

What is a multi-resolution dataset for PV panel segmentation?

This study built a multi-resolution dataset for PV panel segmentation, including PV08 from Gaofen-2 and Beijing-2 satellite images with a spatial resolution of 0.8 m, PV03 from aerial images with a spatial resolution of 0.3 m, and PV01 from UAV images with a spatial resolution of 0.1 m.

How to extract PV panel information from a PvP dataset?

Wang et al. [ 17] trained their semantic segmentation model with the PVP dataset in the same year. Both studies demonstrated that accurate PV panels area can be extracted using red, green, and blue band images. Therefore, we used RGB band information to extract PV panel information.

How to extract PV panel area from crystalline silicon photovoltaic modules?

Both studies demonstrated that accurate PV panels area can be extracted using red, green, and blue band images. Therefore, we used RGB band information to extract PV panel information. The core part of crystalline silicon photovoltaic modules is the solar cell, which mostly appears in a deep blue color to enhance the absorption of sunlight [ 37 ].

How to evaluate PV panel extraction ability of PVI?

In order to evaluate the PV panel extraction ability of PVI more objectively and clearly, first, we calculated the PVI of all the images in the PVP dataset. Then, we transformed the PVI images into binary images using the Otsu [ 50] method. The evaluation metrics show that the mean values of IoU and F1 are 57.64% and 68.49%.