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Journal ArticleDOI

Comparative analysis of methods for cloud segmentation in ground-based infrared images

01 Sep 2021-Renewable Energy (Pergamon)-Vol. 175, pp 1025-1040
TL;DR: In this paper, a comparison between discriminative and generative models for cloud segmentation is presented, where both unsupervised and supervised learning methods are evaluated using the j-statistic.
About: This article is published in Renewable Energy.The article was published on 2021-09-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Discriminative model & Unsupervised learning.
Citations
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Journal ArticleDOI
01 Jan 2022
TL;DR: In this article , the authors proposed a framework to classify the dark cloud patterns (DCP) for prediction of precipitation, which consists upon three steps to classify cloud images, first step tackles noise reduction operations, feature selection and preparation of datasets, second step construct the decision model by using convolutional neural network (CNN) and third step presents the performance visualization by using confusion matrix, precision, recall and accuracy measures.
Abstract: Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers to know about the weather conditions and to care their cash crops. This research proposes a framework to classify the dark cloud patterns (DCP) for prediction of precipitation. The framework consists upon three steps to classify the cloud images, first step tackles noise reduction operations, feature selection and preparation of datasets. Second step construct the decision model by using convolutional neural network (CNN) and third step presents the performance visualization by using confusion matrix, precision, recall and accuracy measures. This research contributes (1) real-world clouds datasets (2) method to prepare datasets (3) highest classification accuracy to predict estimated as 96.90%.
Proceedings ArticleDOI
04 Dec 2022
TL;DR: In this paper , the authors developed a novel model structure focusing on satellite-derived PV power forecasting, which can measure the effects of different cloud motion displacement by involving various input combinations.
Abstract: Renewable power generation is beneficial to handle the global energy shortage problem. However, the intermittent generation output is still the major drawback of renewable energy sources, such as solar energy. Forecasting technology can efficiently deal with these stochastic energy sources, and numerous studies have developed state-of-the-art forecasting models in order to increase the prediction accuracy. Among those, satellite-derived models perform well in short- term photovoltaic (PV) power forecasting, since they can capture the local cloud motion by using historical satellite images. Nevertheless, the explainability of these novel models decreases as the price of accuracy improvements, especially for those intelligent black-box forecast models. The limited geographical resolution of satellite measurements may also lead to the miss or over-forecast of a solar power ramp, causing higher economic loss and model distrust. It is necessary to understand how the models obtain the forecast results, whereas this problem still attracts little attention nowadays. As a result, this study develops a novel model structure focusing on satellite- derived PV power forecasting. The model can measure the effects of different cloud motion displacement by involving various input combinations. The results denote that the proposed method can improve the model explainability while retaining the accuracy level.
Posted Content
TL;DR: In this paper, the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images was investigated using an innovative infrared sky imager mounted on a solar tracker.
Abstract: Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.
Journal ArticleDOI
TL;DR: In this article , the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images was investigated using an innovative infrared sky imager mounted on a solar tracker.
Abstract: Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.
References
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Journal ArticleDOI
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

41,772 citations

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TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

17,017 citations

Journal ArticleDOI
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

15,696 citations

Book
23 Nov 2005
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Abstract: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

11,357 citations