scholarly journals A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4951
Author(s):  
Thomas Carrière ◽  
Rodrigo Amaro e Silva ◽  
Fuqiang Zhuang ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Prediction Interval ◽  
Solar Forecasting ◽  
Forecast Horizon ◽  
Motion Vectors ◽  
Forecast Uncertainty ◽  
Average Width ◽  
Clear Sky ◽  
Cloud Motion ◽  
Proposed Model ◽  
The One

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

scholarly journals Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2264 ◽  
Author(s):  
Stéphanie Monjoly ◽  
Maina André ◽  
Rudy Calif ◽  
Ted Soubdhan
Keyword(s):  
Time Scales ◽  
Predictive Performance ◽  
Forecast Model ◽  
Decomposition Methods ◽  
Solar Forecasting ◽  
Forecast Horizon ◽  
Clear Sky ◽  
Modeling Process ◽  
Resource Variability ◽  
Solar Resource

The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.

A Relativistic Newtonian Mechanics Predicts with Precision the Results of Recent Neutrino-Velocity Experiments

2014 ◽  
Vol 6 (1) ◽  
pp. 1032-1035 ◽  
Author(s):  
Ramzi Suleiman
Keyword(s):  
Null Hypothesis ◽  
Experimental Studies ◽  
Theoretical Models ◽  
Newtonian Mechanics ◽  
Speed Of Light ◽  
Symmetry Principle ◽  
Proposed Model ◽  
Significant Difference ◽  
The One ◽  
Complete Collapse

The research on quasi-luminal neutrinos has sparked several experimental studies for testing the "speed of light limit" hypothesis. Until today, the overall evidence favors the "null" hypothesis, stating that there is no significant difference between the observed velocities of light and neutrinos. Despite numerous theoretical models proposed to explain the neutrinos behavior, no attempt has been undertaken to predict the experimentally produced results. This paper presents a simple novel extension of Newton's mechanics to the domain of relativistic velocities. For a typical neutrino-velocity experiment, the proposed model is utilized to derive a general expression for . Comparison of the model's prediction with results of six neutrino-velocity experiments, conducted by five collaborations, reveals that the model predicts all the reported results with striking accuracy. Because in the proposed model, the direction of the neutrino flight matters, the model's impressive success in accounting for all the tested data, indicates a complete collapse of the Lorentz symmetry principle in situation involving quasi-luminal particles, moving in two opposite directions. This conclusion is support by previous findings, showing that an identical Sagnac effect to the one documented for radial motion, occurs also in linear motion.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Impact Assessment of Urethral Meatus Morphology and Penile Biometry in Transurethral Prostate and Bladder Surgery

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Rodrigo Ribeiro Vieiralves ◽  
Paulo Henrique Pereira Conte ◽  
Eduardo Medina Felici ◽  
Nádia Cristina Pinheiro Rodrigues ◽  
Tomás Accioly de souza ◽  
...  
Keyword(s):  
Average Length ◽  
Intervention Group ◽  
Urethral Meatus ◽  
New Classification ◽  
Average Width ◽  
Front Position ◽  
Determining Factors ◽  
Group A ◽  
The One ◽  
Group B

Objective. To analyze the penile and urethral meatus biometry and its correlation with meatoplasty during endoscopic resections. We also propose a new classification for urethral meatus morphology.Materials and Methods. We prospectively studied 105 patients who underwent prostate and bladder transurethral resections. We performed standardized measurement of penile and urethral meatus biometry followed by penile photo in the front position. The need to perform meatoplasty or dilatation during resectoscope introduction was registered. Data were analyzed comparing the correlation between two groups: without intervention (Group A) and with intervention (Group B).Results. We observed in Group A and Group B, respectively, the average length of urethral meatus of 1.07 cm versus 0.75 cm (p<0.001) and average width of urethral meatus of 0.59 cm versus 0.38 cm (p<0.001). Considering the morphology of the urethral meatus, we propose a new classification, in the following groups: (a) typical; (b) slit; (c) point-like; (d) horseshoe; and (e) megameatus. The point-like meatus was the one that most needed intervention, followed by the slit and the typical meatus (p<0.001).Conclusions. Point-like and slit-shaped urethral meatus, as well as reduced length and width of the urethral meatus, are the determining factors.

Modeling Capacity of Through Movement at Signalized Intersection Affected by Short Left-Turn Bay under Different Signal Settings

2021 ◽  
pp. 036119812110064
Author(s):  
Zihang Wei ◽  
Yunlong Zhang ◽  
Xiaoyu Guo ◽  
Xin Zhang
Keyword(s):  
Cycle Length ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Essential Factor ◽  
Highway Capacity ◽  
Left Turn ◽  
Highway Capacity Manual ◽  
Proposed Model ◽  
Vehicle Demand ◽  
The One

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.

scholarly journals An Alternative Promotion Time Cure Model with Overdispersed Number of Competing Causes: An Application to Melanoma Data

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1815
Author(s):  
Diego I. Gallardo ◽  
Mário de Castro ◽  
Héctor W. Gómez
Keyword(s):  
Estimation Method ◽  
Selection Criterion ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Cure Rate ◽  
Nested Models ◽  
Proposed Model ◽  
The Em Algorithm ◽  
Competing Causes ◽  
The One

A cure rate model under the competing risks setup is proposed. For the number of competing causes related to the occurrence of the event of interest, we posit the one-parameter Bell distribution, which accommodates overdispersed counts. The model is parameterized in the cure rate, which is linked to covariates. Parameter estimation is based on the maximum likelihood method. Estimates are computed via the EM algorithm. In order to compare different models, a selection criterion for non-nested models is implemented. Results from simulation studies indicate that the estimation method and the model selection criterion have a good performance. A dataset on melanoma is analyzed using the proposed model as well as some models from the literature.

scholarly journals A novel approach for the extraction of cloud motion vectors using airglow imager measurements

2015 ◽  
Vol 8 (9) ◽  
pp. 3893-3901 ◽  
Author(s):  
S. Satheesh Kumar ◽  
T. Narayana Rao ◽  
A. Taori
Keyword(s):  
Meridional Wind ◽  
Noise Removal ◽  
Search Window ◽  
Motion Vectors ◽  
Good Correspondence ◽  
Cloud Motion ◽  
Cloud Height ◽  
Novel Approach ◽  
Wind Velocities ◽  
Cloud Tracking

Abstract. The paper explores the possibility of implementing an advanced photogrammetric technique, generally employed for satellite measurements, on airglow imager, a ground-based remote sensing instrument primarily used for upper atmospheric studies, measurements of clouds for the extraction of cloud motion vectors (CMVs). The major steps involved in the algorithm remain the same, including image processing for better visualization of target elements and noise removal, identification of target cloud, setting a proper search window for target cloud tracking, estimation of cloud height, and employing 2-D cross-correlation to estimate the CMVs. Nevertheless, the implementation strategy at each step differs from that of satellite, mainly to suit airglow imager measurements. For instance, climatology of horizontal winds at the measured site has been used to fix the search window for target cloud tracking. The cloud height is estimated very accurately, as required by the algorithm, using simultaneous collocated lidar measurements. High-resolution, both in space and time (4 min), cloud imageries are employed to minimize the errors in retrieved CMVs. The derived winds are evaluated against MST radar-derived winds by considering it as a reference. A very good correspondence is seen between these two wind measurements, both showing similar wind variation. The agreement is also found to be good in both the zonal and meridional wind velocities with RMSEs < 2.4 m s−1. Finally, the strengths and limitations of the algorithm are discussed, with possible solutions, wherever required.

scholarly journals Adaptive model to support business process reengineering

2021 ◽  
Vol 7 ◽  
pp. e505
Author(s):  
Noha Ahmed Bayomy ◽  
Ayman E. Khedr ◽  
Laila A. Abd-Elmegid
Keyword(s):  
Business Process ◽  
Success Factors ◽  
Business Processes ◽  
Business Environment ◽  
Process Time ◽  
Critical Success ◽  
Proposed Model ◽  
Before And After ◽  
Time Cycle ◽  
The One

The one constant in the world is change. The changing dynamics of business environment enforces the organizations to re-design or reengineer their business processes. The main objective of such reengineering processes is to provide services or produce products with the possible lowest cost, shortest time, and best quality. Accordingly, Business Process Re-engineering (BPR) provides a roadmap of how to efficiently achieve the operational goals in terms of enhanced flexibility and productivity, reduced cost, and improved quality of service or product. In this article, we propose an efficient model for BPR. The model specifies where the breakdowns occur in BPR implementation, justifies why such breakdowns occur, and proposes techniques to prevent their occurrence again. The proposed model has been built based on two main sections. The first section focuses on integrating Critical Success Factors (CSFs) and the performance of business processes during the reengineering processes. Additionally, it implements the association rule mining technique to investigate the relationship between CSFs and different business processes. The second section aims to measure the performance of business processes (intended success of BPR) by process time, cycle time, quality and cost before and after reengineering processes. A case study of the Egyptian Tax Authority (ETA) is used to test the efficiency of the proposed model.

Modeling Hydroplaning and Effects of Pavement Microtexture

2005 ◽  
Vol 1905 (1) ◽  
pp. 166-176 ◽  
Author(s):  
G. P. Ong ◽  
T. F. Fwa ◽  
J. Guo
Keyword(s):  
Three Dimensional ◽  
Flow Simulation ◽  
Experimental Results ◽  
Tire Pressure ◽  
Finite Volume Model ◽  
Proposed Model ◽  
Loss Of Contact ◽  
The One ◽  
Theoretical Considerations ◽  
Good Agreement

Hydroplaning on wet pavement occurs when a vehicle reaches a critical speed and causes a loss of contact between its tires and the pavement surface. This paper presents the development of a three-dimensional finite volume model that simulates the hydroplaning phenomenon. The theoretical considerations of the flow simulation model are described. The simulation results are in good agreement with the experimental results in the literature and with those obtained by the well-known hydroplaning equation of the National Aeronautics and Space Administration (NASA). The tire pressure–hydroplaning speed relationship predicted by the model is found to match well the one obtained with the NASA hydroplaning equation. Analyses of the results of the present study indicate that pavement microtexture in the 0.2- to 0.5-mm range can delay hydroplaning (i.e., raise the speed at which hydroplaning occurs). The paper also shows that the NASA hydroplaning equation provides a conservative estimate of the hydroplaning speed. The analyses in the present study indicate that when the microtexture of the pavement is considered, the hydroplaning speed predicted by the proposed model deviates from the speed predicted by the smooth surface relationship represented by the NASA hydroplaning equation. The discrepancies in hydroplaning speed are about 1% for a 0.1-mm microtexture depth and 22% for a 0.5-mm microtexture depth. The validity of the proposed model was verified by a check of the computed friction coefficient against the experimental results reported in the literature for pavement surfaces with known microtexture depths.

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