Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera

Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several sectors in which the study of cloudiness is of vital importance. In the renewable field, the increasing development of solar technology and the emerging trend for constructing and operating solar plants across the earth’s surface requires very precise control systems that provide optimal energy production management. Similarly, airports are hubs where cloud coverage is required to provide high-precision periodic observations that inform airport operators about the state of the atmosphere. This work presents an autonomous cloud detection system, in real time, based on the digital image processing of a low-cost sky camera. An algorithm was developed to identify the clouds in the whole image using the relationships established between the channels of the RGB and Hue, Saturation, Value (HSV) color spaces. The system’s overall success rate is approximately 94% for all types of sky conditions; this is a novel development which makes it possible to identify clouds from a ground perspective without the use of radiometric parameters

Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types

Determination of the Soiling Impact on Photovoltaic Modules at the Coastal Area of the Atacama Desert

With an elevation of 1000 m above sea level, once the coastal mountain range is crossed, the Atacama Desert receives the highest levels of solar radiation in the world. Global horizontal irradiations over 2500 kWh/(m2 year) and a cloudiness index below 3% were determined. However, this index rises to 45% in the coastal area, where the influence of the Pacific Ocean exists with a large presence of marine aerosols. It is on the coastal area that residential photovoltaic (PV) applications are concentrated. This work presents a study of the soiling impact on PV modules at the coastline of Atacama Desert. The current–voltage characteristics of two multicrystalline PV modules exposed to outdoor conditions were compared, while one of them was cleaned daily. Asymptotic behavior was observed in the accumulated surface dust density, over 6 months. This behavior was explained by the fact that as the glass became soiled, the probability of glass-to-particle interaction decreased in favor of a more likely particle-to-particle interaction. The surface dust density was at most 0.17 mg·cm−2 per month. Dust on the module led to current losses in the range of 19% after four months, which in turn produced a reduction of 13.5%rel in efficiency

Prognostic significance of FLT3-ITD length in AML patients treated with intensive regimens

FLT3-ITD mutations are detected in approximately 25% of newly diagnosed adult acute myeloid leukemia (AML) patients and confer an adverse prognosis. The FLT3-ITD allelic ratio has clear prognostic value. Nevertheless, there are numerous manuscripts with contradictory results regarding the prognostic relevance of the length and insertion site (IS) of the FLT3-ITD fragment. We aimed to assess the prognostic impact of these variables on the complete remission (CR) rates, overall survival (OS) and relapse-free survival (RFS) of AML patients with FLT3-ITDmutations. We studied the FLT3-ITD length of 362 adult AML patients included in the PETHEMA AML registry. We tried to validate the thresholds of ITD length previously published (i.e., 39 bp and 70 bp) in intensively treated AML patients (n = 161). We also analyzed the mutational profile of 118 FLT3-ITD AML patients with an NGS panel of 39 genes and correlated mutational status with the length and IS of ITD. The AUC of the ROC curve of the ITD length for OS prediction was 0.504, and no differences were found when applying any of the thresholds for OS, RFS or CR rate. Only four out of 106 patients had ITD IS in the TKD1 domain. Our results, alongside previous publications, confirm that FLT3-ITD length lacks prognostic value and clinical applicability. © 2021, The Author(s)

Nowcasting System Based on Sky Camera Images to Predict the Solar Flux on the Receiver of a Concentrated Solar Plant

As part of the research for techniques to control the final energy reaching the receivers of central solar power plants, this work combines two contrasting methods in a novel way as a first step towards integrating such systems in solar plants. To determine the effective power reaching the receiver, the direct normal irradiance was predicted at ground level using a total sky camera, TSI-880 model. Subsequently, these DNI values were used as the inputs for a heliostat model (Fiat-Lux) to trace the sunlight’s path according to the mirror features. The predicted valuex of flux, obtained from these simulations, differ of less than 20% from the real values. This represents a significant advance in integrating different technologies to quantify the losses produced in the path from the heliostats to the central receiver, which are normally caused by the presence of atmospheric attenuation factors

Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

Solar resource assessment is of paramount importance in the planning of solar energy applications. Solar resources are abundant and characterization is essential for the optimal design of a system. Solar energy is estimated, indirectly, by the processing of satellite images. Several analyses with satellite images have considered a single variable—cloudiness. Other variables, such as albedo, have been recognized as critical for estimating solar irradiance. In this work, a multivariate analysis was carried out, taking into account four variables: cloudy sky index, albedo, linke turbidity factor (TL2), and altitude in satellite image channels. To reduce the dimensionality of the database (satellite images), a principal component analysis (PCA) was done. To determine regions with a degree of homogeneity of solar irradiance, a cluster analysis with unsupervised learning was performed, and two clustering techniques were compared: k-means and Gaussian mixture models (GMMs). With respect to k-means, the GMM method obtained a smaller number of regions with a similar degree of homogeneity. The multivariate analysis was performed in Mexico, a country with an extended territory with multiple geographical conditions and great climatic complexity. The optimal number of regions was 17. These regions were compared for annual average values of daily irradiation data from ground stations using multiple linear regression. A comparison between the mean of each region and the ground station measurement showed a linear relationship with a R2 score of 0.87. The multiple linear regression showed that the regions were strongly related to solar irradiance. The optimal sites found are shown on a map of Mexico

No Evidence that CD33 rs12459419 Polymorphism Predicts Gemtuzumab Ozogamicin Response in Consolidation Treatment of Acute Myeloid Leukemia Patients: Experience of the PETHEMA Group.

Gemtuzumab ozogamicin (GO) is a conjugate of a monoclonal antibody and calicheamicin, which has been reapproved for the treatment of acute myeloid leukemia (AML). AML patients with the CD33 rs12459419 CC genotype might benefit from the addition of GO to intensive treatment in contrast to patients with CT/TT genotypes. Nevertheless, contradictory results have been reported. We sought to shed light on the prediction of GO response in AML patients with rs12459419 polymorphism who were treated with GO in the consolidation (n = 70) or reinduction (n = 20) phase. The frequency distribution of the rs12459419 polymorphism in the complete cohort of patients was 44.4% (n = 40), 50% (n = 45), and 5.6% (n = 5) for CC, CT, and TT genotypes, respectively. Regarding the patients treated with GO for consolidation, we performed a Kaplan-Meier analysis of overall survival and relapse-free survival according to the rs12459419 polymorphism (CC vs. CT/TT patients) and genetic risk using the European Leukemia Net (ELN) 2010 risk score. We also carried out a Cox regression analysis for the prediction of overall survival, with age and ELN 2010 as covariates. We found no statistical significance in the univariate or multivariate analysis. Additionally, we performed a global Kaplan-Meier analysis for the patients treated with GO for reinduction and did not find significant differences; however, our cohort was too small to draw any conclusion from this analysis. The use of GO in consolidation treatment is included in the approval of the compound; however, evidence regarding its efficacy in this setting is lacking. Rs12459419 polymorphism could help in the selection of patients who might benefit from GO. Regrettably, in our cohort, the rs12459419 polymorphism does not seem to be an adequate tool for the selection of patients who might benefit from the addition of GO in consolidation cycles