Research On Cloud Tracking And Motion Prediction Based On Ground-based Cloud Image

Cloud motion is one of the main causes of PV power output fluctuation,and cloud tracking and motion trend prediction using ground-based cloud maps are very important for PV power output fluctuation forecasting.However,cloud motion changes randomly,which brings great challenges to cloud tracking and motion trend prediction research,and how to efficiently and accurately perform cloud detection,tracking and motion trend prediction has become an important research direction in the field of solar photovoltaic power generation.In this paper,we take the ground-based cloud map as the research object and propose a cloud tracking and cloud position prediction method.The main work is as follows.(1)Research on cloud cluster detection based on improved median filtering.Aiming at the problem that the blurred edges of cloud masses in some ground-based cloud maps make it difficult to detect cloud masses accurately,an image filtering-based cloud mass detection method is proposed,which improves the image NBR value by separating the channels of ground-based cloud maps,filtering the red and blue channel images before fusion,and finally segmenting the images with the threshold method.The detection accuracy of fuzzy edges is improved,which lays the foundation for the subsequent research of cloud cluster tracking and motion trend prediction.(2)Research on cloud cluster tracking by fusing particle filtering and optical flow.An optical flow tracking algorithm fusing particle filtering is proposed to obtain a priori information through particle filtering image processing,and then use the optical flow method to process video sequences,which effectively solves the problems of slow tracking rate and easy loss of tracking targets in existing cloud mass tracking algorithms.The accurate measurement of cloud motion vector is realized.(3)Research on cloud mass motion trend prediction based on deep learning.Aiming at the problems of large error and low reliability of existing cloud mass motion trend prediction,a motion trend prediction method integrating convolutional neural network and recurrent neural network is proposed to give correlation to continuous ground-based cloud maps in time dimension and predict future cloud mass position changes by learning the change pattern of cloud map features in time dimension to achieve cloud mass motion trend prediction.In this paper,a cloud tracking and motion trend prediction method is proposed to solve the problems of inaccurate cloud detection,low tracking rate,easy loss of target and inaccurate prediction in current research,and the feasibility of the proposed algorithm is verified through experiments.