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

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE)

Field Quality Control of Spectral Solar Irradiance Measurements by Comparison with Broadband Measurements

Measurement of solar spectral irradiance is required in an increasingly wide variety of technical applications, such as atmospheric studies, health, and solar energy, among others. The solar spectral irradiance at ground level has a strong dependence on many atmospheric parameters. In addition, spectroradiometer optics and detectors have high sensitivity. Because of this, it is necessary to compare with a reference instrumentation or light source to verify the quality of measurements. A simple and realistic test for validating solar spectral irradiance measurements is presented in this study. This methodology is applicable for a specific spectral range inside the broadband range from 280 to 4000 nm under cloudless sky conditions. The method compares solar spectral irradiance measurements with both predictions of clear-sky solar spectral irradiance and measurements of broadband instruments such as pyrheliometers. For the spectral estimation, a free atmospheric transmittance simulation code with the air mass calculation as the mean parameter was used. The spectral direct normal irradiance (Gbl) measurements of two different spectroradiometers were tested at Plataforma Solar de Almería, Spain. The results are presented in this article. Although only Gbl measurements were considered in this study, the same methodology can be applied to the other solar irradiance componentsThis research was funded by the Chilean Corporación de Fomento de la Producción (CORFO), grant number 17BPE3-83761, and 7PTECES-75830 under the framework of the project “AtaMoS TeC,” by the ANID grant number ANID/FONDAP/15110019 (SERC-Chile) and ANID/ FONDECYT-INITIATION/11190289, funded by the Spanish Education and Competitivity Ministry and co-financed by the European Regional Development Fund grant number ENE2017-83790-C3- 1,2,3 and by the Spanish Ministry of Science and Innovation and co-financed by the European Regional Development Fund grant reference PID2020-118239RJ-I00 (MAPVSpain) Special thanks to Antonio Campos (PSA, Spain) for his support during this wor

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

Economic Effect of Dust Particles on Photovoltaic Plant Production

The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation is extreme. In this work, the effect of dust soiling is examined on the electricity generation of an experimental photovoltaic pilot plant, installed at the Solar Energy Research Center (CIESOL) at the University of Almería. An average reduction of 5% of the power of a photovoltaic plant due to dust contamination has been obtained, this data being used to simulate the economic effect in plants of 9 kWp and 1 and 50 MWp. The economic losses have been calculated, and are capable of being higher than 150,000 €/year in industrial plants of 50 MWp. A cleaning strategy has also been presented, which represents a substantial economic outlay over the years of plant operation

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

The modular network structure of the mutational landscape of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways

Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications

The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor