Supervised classification of solar features using prior information

Visscher, Ruben De; Delouille, Véronique; Dupont, Pierre; Deledalle, Charles-Alban

doi:10.1051/swsc/2015033

Cited by 6 publications

(9 citation statements)

References 42 publications

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“…A Naïve Bayes Classifier is machine learning technique based on Bayes probabilistic theory that represents the relationship between a prior probability and posterior probability using conditional probabilities [30][31][32][33][34]; it deals with decision problems mathematically under uncertainty and creates a simple and efficient model in the field of document taxonomy and disease prediction [35,36]. This method makes classification rules based on historical data and applies new values to the class that is arranged according to predefined rules.…”

Section: Probabilistic Forecasting For Solar Power Using a Naï Ve Baymentioning

confidence: 99%

“…This method makes classification rules based on historical data and applies new values to the class that is arranged according to predefined rules. The general formula for a Naïve Bayes Classifier is as shown in Equation (5) [30][31][32][33][34]: We minimize the error variance in the ordinary kriging method using the Lagrange function.…”

Section: Probabilistic Forecasting For Solar Power Using a Naï Ve Baymentioning

confidence: 99%

“…The probability of the attribute value located in the denominator serves as a normalized constant and does not affect the probability results, so we have omitted it for convenience of calculation. When we assign a new attribute value to a class based on predefined classification criteria, all classes will have a post probability, and the class among them with the maximum probability is finally selected as shown in Equation 7 [30][31][32][33][34].…”

Section: Probabilistic Forecasting For Solar Power Using a Naïve Bayementioning

confidence: 99%

“…The prior probability is represented as shown in Equation (8) below. The larger the amount of data, the more different the class, and the prior probability can be adjusted depending on the class range setting [30][31][32][33][34].…”

Section: Pamentioning

confidence: 99%

“…In contrast, each This assumption supports efficient algorithms that are simple to compute for test cases and to estimate from training data. The independency assumption means that the conditional probability is represented by the chain rule as shown in Equation 6 [30][31][32][33][34].…”

Section: Pamentioning

confidence: 99%

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Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

Nam

Hur

2018

Energies

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Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when interconnected. Accurate and reliable forecasting helps to maintain the system safely given large-scale solar power resources; this paper therefore proposes a probabilistic forecasting approach to solar resources using the R statistics program, applying a hybrid model that considers spatio-temporal peculiarities. Information on how the weather varies at sites of interest is often unavailable, so we use a spatial modeling procedure called kriging to estimate precise data at the solar power plants. The kriging method implements interpolation with geographical property data. In this paper, we perform day-ahead forecasts of solar power based on the probability in one-hour intervals by using a Naïve Bayes Classifier model, which is a classification algorithm. We augment forecasting by taking into account the overall data distribution and applying the Gaussian probability distribution. To validate the proposed hybrid forecasting model, we perform a comparison of the proposed model with a persistence model using the normalized mean absolute error (NMAE). Furthermore, we use empirical data from South Korea’s meteorological towers (MET) to interpolate weather variables at points of interest.

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Section: Probabilistic Forecasting For Solar Power Using a Naï Ve Baymentioning

confidence: 99%

Section: Probabilistic Forecasting For Solar Power Using a Naï Ve Baymentioning

confidence: 99%

Section: Probabilistic Forecasting For Solar Power Using a Naïve Bayementioning

confidence: 99%

Section: Pamentioning

confidence: 99%

Section: Pamentioning

confidence: 99%

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Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

Nam

Hur

2018

Energies

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Verification of high‐speed solar wind stream forecasts using operational solar wind models

et al. 2016

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High‐speed solar wind streams emanating from coronal holes are frequently impinging on the Earth's magnetosphere causing recurrent, medium‐level geomagnetic storm activity. Modeling high‐speed solar wind streams is thus an essential element of successful space weather forecasting. Here we evaluate high‐speed stream forecasts made by the empirical solar wind forecast (ESWF) and the semiempirical Wang‐Sheeley‐Arge (WSA) model based on the in situ plasma measurements from the Advanced Composition Explorer (ACE) spacecraft for the years 2011 to 2014. While the ESWF makes use of an empirical relation between the coronal hole area observed in Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images and solar wind properties at the near‐Earth environment, the WSA model establishes a link between properties of the open magnetic field lines extending from the photosphere to the corona and the background solar wind conditions. We found that both solar wind models are capable of predicting the large‐scale features of the observed solar wind speed (root‐mean‐square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high‐speed solar wind streams (threat score, TS ≈ 0.37). The predicted high‐speed streams show typical uncertainties in the arrival time of about 1 day and uncertainties in the speed of about 100 km/s. General advantages and disadvantages of the investigated solar wind models are diagnosed and outlined.

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Real-time solar image classification: Assessing spectral, pixel-based approaches

Hughes

Hsu

Seaton

et al. 2019

J. Space Weather Space Clim.

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In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using extreme ultraviolet and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012), a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

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Supervised classification of solar features using prior information

Cited by 6 publications

References 42 publications

Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models

Verification of high‐speed solar wind stream forecasts using operational solar wind models

Real-time solar image classification: Assessing spectral, pixel-based approaches

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