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While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies. • An analysis of the correlation and interdependence of 9 different meteorological variables in two case studies. • A dimensionality reduction of the input meteorological variables is presented. • Several machine learning regression methods are evaluated for estimating PV output power. • The importance and ranking of the variables depend on the climate of the area of study. • A lower-dimension subspace of meteorological variables can result in a slightly similar estimation accuracy. [ABSTRACT FROM AUTHOR]

While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies. • An analysis of the correlation and interdependence of 9 different meteorological variables in two case studies. • A dimensionality reduction of the input meteorological variables is presented. • Several machine learning regression methods are evaluated for estimating PV output power. • The importance and ranking of the variables depend on the climate of the area of study. • A lower-dimension subspace of meteorological variables can result in a slightly similar estimation accuracy. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal, and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012–2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates. [ABSTRACT FROM AUTHOR]

Ground-based whole sky cameras are now-a-days extensively used for localized monitoring of the clouds. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we derive a model for estimating the solar irradiance using pictures taken by those imagers. Unlike pyranometers, these sky images contain information about the cloud coverage and can be used to derive cloud movement. An accurate estimation of the solar irradiance using solely those images is thus a first step towards short-term solar energy generation forecasting, as cloud movement can also be derived from them. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a root mean square error of 178 Watt/m² between estimated and measured solar irradiance, outperforming state-of-the-art methods using other weather instruments. Our method shows a significant improvement in estimating strong short-term variations. [ABSTRACT FROM AUTHOR]

Sky-cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg - an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multiexposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results. [ABSTRACT FROM AUTHOR]

Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRSeg - an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR images for cloud segmentation and achieves very good results. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel graph‐based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state‐of‐the‐art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state‐of‐the‐art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph‐based climate classification system. [ABSTRACT FROM AUTHOR]

Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate(e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today's climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration's daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations. • Interoperable knowledge graphs advances integrating and updating multi-source data. • OpenStreetMap data enhance the geographical features of the knowledge graph data. • Exploring the knowledge graph structures visually is necessary for non-experts. [ABSTRACT FROM AUTHOR]

Stereo matching refers to finding the correspondence of a point in the real world between two different storage mediums (e.g. , intensity images, depth images, three-dimensional points). There are existing stereo matching methods in the literature, but they exhibit two shortcomings. Firstly, during the feature region extraction of stereo matching, these methods require measuring the distance of regions, but measuring the texture distribution of the region is difficult and might lead to the failure of matching. Secondly, the templates used in these methods are rectangles with a fixed size, while most of the natural images exhibit rich information and are more suitable for flexible templates. In this paper, we propose an attentional multi-directional convolutional network (AMDCNet) for circumventing these issues. Our AMDCNet approach consists of three stages: extract the visual sensitivity factor, construct the multi-directional aggregation template and utilize left–right consistency detection to optimize. We evaluate our approach using standard images in the Middlebury test dataset, Scene Flow and KITTI 2015. Experimental results show that AMDCNet can reduce the mismatch rate, and also show significant improvement in accuracy compared with some classical method. In some scenarios, it surpasses some advanced methods based on deep learning. The model code, dataset, and results of the experiments in this paper are available at: https://github.com/WangHewei16/Attentional-Multi-Directional-Convolution-Network. • Proposed an attentional multi-directional convolutional network (AMDCNet) for stereo matching. • AMDCNet produces promising results in mismatching rate, disparity estimation, and matching precision experiments. • AMDCNet introduces a visual sensitivity factor in the cost calculation stage. • Constructs a multi-directional aggregation template by using visual sensitivity factor. [ABSTRACT FROM AUTHOR]