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Classifying ground-measured 1?minute temporal variability within hourly intervals for direct normal irradiances
摘要： Variability of solar surface irradiances in the 1 minute range is of interest especially for solar energy applications. Eight variability classes are defined for the 1 minute resolved direct normal irradiance (DNI) variability inside an hour. They combine high, medium, and low irradiance conditions with small, medium, and large scale variations from one minute to the next minute. A reference data base of 333 individual hours with ground-based 1 minute DNI observations was created by expert review from one year of observations at the BSRN station in Carpentras, France. Each variability class is represented by 16 to 63 members. Variability indices as previously published or newly suggested are used as classifiers to detect the class members automatically. Up to 77 % of all class members are identified correctly by this automatic scheme. The variability classification method allows the comparison of different project sites in a statistical and automatic manner to quantify short-term variability impacts on solar power production.
关键词： variability,ground-based observations,direct irradiance,global horizontal irradiance,automatic classification
更新于2025-09-23 15:21:01
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A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training
摘要： Mobile Laser Scanning (MLS) data of outdoor environment are typically characterised by occlusion, noise, clutter, large data size and high quantum of information which makes their classification a challenging problem. This paper presents three deep Convolutional Neural Network (CNN) architectures in three dimension (3D), namely single CNN (SCN), multi-faceted CNN (MFC) and MFC with reproduction (MFCR) for automatic classification of MLS data. The MFC uses multiple facets of an MLS sample as inputs to different SCNs, thus providing additional information during classification. The MFC, once trained, is used to reproduce additional samples with the help of existing samples. The reproduced samples are employed to further refine the MFC training parameters, thus giving a new method called MFCR. The three architectures are evaluated on an ensemble of 3D outdoor MLS data consisting of four classes, i.e. tree, pole, house and ground covered with low vegetation along with car samples from KITTI dataset. The total accuracy and kappa values of classifications reached up to (i) 86.0% and 81.3% for the SCN (ii) 94.3% and 92.4% for the MFC and (iii) 96.0% and 94.6% for the MFCR, respectively. The paper has demonstrated the use of multiple facets to significantly improve classification accuracy over the SCN. Finally, a unique approach has been developed for reproduction of samples which has shown potential to improve the accuracy of classification. Unlike previous works on the use of CNN for structured point cloud of indoor objects, this work shows the utility of different proposed CNN architectures for classification of varieties of outdoor objects, viz., tree, pole, house and ground which are captured as unstructured point cloud by MLS.
关键词： Sample reproduction,Mobile Laser Scanning (MLS),Automatic classification,Convolutional Neural Network (CNN)
更新于2025-09-19 17:15:36