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Open accessJournal ArticleDOI: 10.1016/J.DIB.2021.106914

Girasol, a sky imaging and global solar irradiance dataset

04 Mar 2021-Data in Brief (Elsevier)-Vol. 35, pp 106914-106914
Abstract: The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

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Topics: Solar irradiance (60%), Pyranometer (58%), Photovoltaic system (55%) ... show more

7 results found

Open accessJournal ArticleDOI: 10.1016/J.APENERGY.2021.116656
15 Apr 2021-Applied Energy
Abstract: The energy available in a solar energy powered grid is uncertain due to the weather conditions at the time of generation. Forecasting global solar irradiance could address this problem by providing the power grid with the capability of scheduling the storage and dispatch of energy. The occlusion of the Sun by clouds is the main cause of instabilities in the generation of solar energy. This investigation proposes a method to visualize the wind velocity field in sequences of longwave infrared images of clouds when there are multiple wind velocity fields in an image. This method can be used to forecast the occlusion of the Sun by clouds, providing stability in the generation of solar energy. Unsupervised learning is implemented to infer the distribution of the clouds’ velocity vectors and heights in multiple wind velocity fields in an infrared image. A multi-output weighted support vector machine with flow constraints is used to extrapolate the wind velocity fields to the entire frame, visualizing the path of the clouds. The proposed method is capable of approximating the wind velocity field in a small air parcel using the velocity vectors and physical features of clouds extracted from infrared images. Assuming that the streamlines are pathlines, the visualization of the wind velocity field can be used for forecasting cloud occlusions of the Sun. This is of importance when considering ways of increasing the stability of solar energy generation.

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Topics: Wind speed (59%), Solar irradiance (59%), Solar energy (55%) ... show more

5 Citations

Journal ArticleDOI: 10.1016/J.APENERGY.2021.117014
Meenu Ajith1, Manel Martínez-Ramón1Institutions (1)
15 Jul 2021-Applied Energy
Abstract: Solar irradiance forecasting has been gaining paramount importance in recent years due to its impact on power grids. However, solar energy harvesting over shorter periods also brings new challenges due to its intermittent and uncertain attributes. Hence, accurate forecasting has become an indispensable aspect of the effective management of power system operations. The existing models focus on using only time-series data for solar radiation forecasting. But during cloudy time instances, it fails to quickly capture the nonlinear Spatio-temporal variations in the data for shorter periods. To bridge this gap, in this paper, a multi-modal fusion network is developed for studying solar irradiance micro forecasts by using both infrared images and past solar irradiance data. Here both spatial and temporal information is extracted parallelly and fused using a fully connected neural network. The solar forecasts of the proposed methods are evaluated against benchmark models in terms of Mean Absolute Percentage Error (MAPE) and other qualitative measures. The experimental results illustrate that the multi-modal fusion networks outperform the existing methods while predicting solar irradiance for cloudy days as well as mixed days (both cloudy and sunny days). Hence a transfer learning-based classifier with 99.23% accuracy is developed to categorize the cloudy days from sunny days. In the case of higher horizon forecasts, the proposed models show the optimum trade-off between performance and test time. Moreover, the Multiple Image Convolutional Long Short Term Memory Fusion Network (MICNN-L) shows a 46.42% improvement in MAPE whereas the Convolutional Long Short Term Memory Fusion Network (CNN-L) has a 42.02% increase when compared to the benchmark machine learning and deep learning models.

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4 Citations

Open accessJournal ArticleDOI: 10.1016/J.RENENE.2021.04.141
01 Sep 2021-Renewable Energy
Abstract: The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.

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Topics: Discriminative model (58%), Unsupervised learning (54%), Feature vector (53%) ... show more

4 Citations

Open accessPosted Content
Abstract: Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. The infrared cloud images have been preprocessed to remove debris, which increases the performance of the analyzed methods. The use of neighboring features of the pixels also leads to a performance improvement. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation.

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Topics: Kernel (statistics) (54%), Kernel method (52%), Cloud computing (51%) ... show more

Open accessPosted Content
11 Oct 2021-arXiv: Learning
Abstract: The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This investigation aims to decrease the additional cost by introducing probabilistic multi-task intra-hour solar forecasting (feasible in real time applications) to increase the penetration of photovoltaic systems in power grids. The direction of moving clouds is estimated in consecutive sequences of sky images by extracting features of cloud dynamics with the objective of forecasting the global solar irradiance that reaches photovoltaic systems. The sky images are acquired using a low-cost infrared sky imager mounted on a solar tracker. The solar forecasting algorithm is based on kernel learning methods, and uses the clear sky index as predictor and features extracted from clouds as feature vectors. The proposed solar forecasting algorithm achieved 16.45\% forecasting skill 8 minutes ahead with a resolution of 15 seconds. In contrast, previous work reached 15.4\% forecasting skill with the resolution of 1 minute. Therefore, this solar forecasting algorithm increases the performances with respect to the state-of-the-art, providing grid operators with the capability of managing the inherent uncertainties of power grids with a high penetration of photovoltaic systems.

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Topics: Photovoltaic system (60%), Solar irradiance (59%), Solar tracker (55%)


15 results found

Open accessJournal ArticleDOI: 10.1016/J.RENENE.2016.12.095
01 May 2017-Renewable Energy
Abstract: Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach.

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Topics: Probabilistic forecasting (65%), Solar irradiance (58%), Gradient boosting (57%) ... show more

688 Citations

Open accessBook
01 Jan 2003-
Abstract: From the Publisher: Rapidly evolving computer and communications technologies have achieved data transmission rates and data storage capacities high enough for digital video But video involves much more than just pushing bits! Achieving the best possible image quality, accurate color, and smooth motion requires understanding many aspects of image acquisition, coding, processing, and display that are outside the usual realm of computer graphics At the same time, video system designers are facing new demands to interface with film and computer system that require techniques outside conventional video engineering Charles Poynton's 1996 book A Technical Introduction to Digital Video became an industry favorite for its succinct, accurate, and accessible treatment of standard definition television (SDTV) In Digital Video and HDTV, Poynton augments that book with coverage of high definition television (HDTV) and compression systems With the help of hundreds of high quality technical illustrations, this book presents the following topics: Basic concepts of digitization, sampling, quantization, gamma, and filtering Principles of color science as applied to image capture and display Scanning and coding of SDTV and HDTV Video color coding: luma, chroma (4:2:2 component video, 4fSC composite video) Analog NTSC and PAL Studio systems and interfaces Compression technology, including M-JPEG and MPEG-2 Broadcast standards and consumer video equipment

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Topics: Video capture (71%), Video processing (68%), S-Video (68%) ... show more

507 Citations

Open accessJournal ArticleDOI: 10.1016/J.RSER.2013.06.042
Maimouna Diagne1, Mathieu David1, Philippe Lauret1, John Boland2  +1 moreInstitutions (2)
Abstract: Integration of solar energy into the electricity network is becoming essential because of its continually increasing growth in usage. An efficient use of the fluctuating energy output of photovoltaic (PV) systems requires reliable forecast information. In fact, this integration can offer a better quality of service if the solar irradiance variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs. The study starts with a presentation of statistical approaches and techniques based on cloud images. Next numerical weather prediction or NWP models are detailed before discussing hybrid models. Finally, we give indications for future solar irradiance forecasting approaches dedicated to the management of small-scale insular grids.

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Topics: Solar power forecasting (69%), Solar irradiance (63%), Photovoltaic system (60%) ... show more

481 Citations

Open accessJournal ArticleDOI: 10.1016/J.SOLENER.2017.11.023
Dazhi Yang1, Jan Kleissl2, Christian A. Gueymard, Hugo T.C. Pedro2  +1 moreInstitutions (2)
01 Jul 2018-Solar Energy
Abstract: Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

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Topics: Photovoltaic system (56%), Solar irradiance (52%)

235 Citations

Open accessJournal ArticleDOI: 10.1016/J.APENERGY.2018.08.109
01 Jan 2019-Applied Energy
Abstract: In this study, we model seven scenarios for the European power system in 2050 based on 100% renewable energy sources, assuming different levels of future demand and technology availability, and compare them with a scenario which includes low-carbon non-renewable technologies. We find that a 100% renewable European power system could operate with the same level of system adequacy as today when relying on European resources alone, even in the most challenging weather year observed in the period from 1979 to 2015. However, based on our scenario results, realising such a system by 2050 would require: (i) a 90% increase in generation capacity to at least 1.9 TW (compared with 1 TW installed today), (ii) reliable cross-border transmission capacity at least 140 GW higher than current levels (60 GW), (iii) the well-managed integration of heat pumps and electric vehicles into the power system to reduce demand peaks and biogas requirements, (iv) the implementation of energy efficiency measures to avoid even larger increases in required biomass demand, generation and transmission capacity, (v) wind deployment levels of 7.5 GW y−1 (currently 10.6 GW y−1) to be maintained, while solar photovoltaic deployment to increase to at least 15 GW y−1 (currently 10.5 GW y−1), (vi) large-scale mobilisation of Europe’s biomass resources, with power sector biomass consumption reaching at least 8.5 EJ in the most challenging year (compared with 1.9 EJ today), and (vii) increasing solid biomass and biogas capacity deployment to at least 4 GW y−1 and 6 GW y−1 respectively. We find that even when wind and solar photovoltaic capacity is installed in optimum locations, the total cost of a 100% renewable power system (∼530 €bn y−1) would be approximately 30% higher than a power system which includes other low-carbon technologies such as nuclear, or carbon capture and storage (∼410 €bn y−1). Furthermore, a 100% renewable system may not deliver the level of emission reductions necessary to achieve Europe’s climate goals by 2050, as negative emissions from biomass with carbon capture and storage may still be required to offset an increase in indirect emissions, or to realise more ambitious decarbonisation pathways.

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Topics: Renewable energy (61%), Photovoltaic system (54%), Electric power system (52%) ... show more

219 Citations

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