Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images

Alonso-Montesinos, Joaquín; Batlles, F.J.; Portillo, Carlos

doi:10.1016/j.enconman.2015.09.001

Cited by 106 publications

(56 citation statements)

References 32 publications

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“…This means that, for a classification problem with an imbalance in distributed samples such as that in our study, the best value of parameter K mainly depends on the quantity of class with the smallest sample scale. Thus, for the PV plant considered in this paper, the suggested value of K with a particular sample scale can be: (N A , N B , N C , N D ), (19) where N A , N B , N C and N D refer to the quantities of data in each GWC.…”

Section: The Optimal Value Of the Nearest Neighbors For Knnmentioning

confidence: 99%

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Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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Section: The Optimal Value Of the Nearest Neighbors For Knnmentioning

confidence: 99%

“…The former methods are mainly used for intra-hour forecasting, while the latter features forecast horizons of tens of minutes to up to 6 h [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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“…To quantify the models' validity, the statistical estimation analysis was obtained for the three cases: RMSE-the root mean square error (5), in which the results are shown as a value between 0 and 1 (0 being 0 attenuation and 1 being 100% attenuation), and where N is the total number of estimations; nRMSE-the normalized mean square error (6), measured in percent (%); MBE-the mean deviation (7), with the same unit as the RMSE; and nMBE-given by (8), expressed in (%) and the dimensionless correlation coefficient Equation (2). Table 1 displays the digitized data (DD) combinations of M1, carried out randomly, that had the highest r value from which the best combination was chosen; this was based on the behavior of the digitized channels in performing the linear regression training with 2400 data points, and then validating the model for M2 with the remaining 800 data points.…”

Section: Resultsmentioning

confidence: 99%

“…There are records of its use at the University of California Merced's solar observatory station (2012), at CIESOL, University of Almeria, Spain (2013), at the University of California, San Diego (2014), at UNAM in Mexico (2015) and at the University of Singapore (2019), amongst others. Using this technology with models that convert the digitized information into irradiance indices, it is possible to estimate the direct solar irradiance for different sky conditions, as they occur, by modeling a camera calibration system (a process where an image's pixel intensity is related to the amount of solar radiation present at that moment) [5,24,25]. In addition, remote sensing techniques (satellite images and sky cameras) have been combined with radiometric data for sky classification processing [26].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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. and overcast skies over the short-term (from 1 to 180 min) using a sky camera, where the average nRMSE values obtained were 24. 36%, 20.9% and 19.17%, respectively [5]. G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].An analysis of energy forecasting in solar-tower plants combining a short-term solar irradiation forecasting scheme with a solar-tower plant model, the System Advisor Model (SAM), was used to simulate the behavior of the Gemasolar and Crescent Dunes plants. The findings showed that the best results appeared for the 90-min horizon, where the annual forecasting energy yield for Gemasolar was 97.34 GWh year while for Crescent Dunes it was 392.57 GWh year [8]. Similarly, cloud abundance forecasting has been studied for timescales of between (1-180 min), resulting in short-term forecasting (of less than one hour) and medium-term forecasting (up to 3 h), which was proven to have an 80% success rate-indeed, it was so successful that an application (portal) tool was developed that helps to increase power plant production [9,10].Recent studies have presented a method for the probabilistic forecasting of solar irradiance based on the joint Probability Distribution Function (PDF) of irradiance predicted using the Numerical Weather Prediction (NWP) and the irradiance observed; these are based on models of meteorological processes such as atmospheric dynamics, cloud formation and radiative transfer processes [11]. H. Yang and B. Kurtz estimated direct solar irradiance over the short and medium term for different sky conditions ...

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“…In addition to this, a cloud identification system was developed where the sky camera images were processed according to a sky classification [17], thus solving the pixel saturation problem in the solar area [18]. These studies were concluded with a solar radiation estimation using the pixel information from the sky image [19] applying the cloud motion vectors to obtain solar irradiance forecasting over the short term [20]. Moreover, satellite images have also been used to study cloud cover and cloud classification.…”

Section: Introductionmentioning

confidence: 99%

The application of Bayesian network classifiers to cloud classification in satellite images

et al. 2016

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a b s t r a c tThe need to reduce the impact of traditional electricity generation necessitates an increase in the optimization of alternative systems that produce less environmental contamination. Renewables play a key role, with solar energy considered one of the most important energy supply sources. Solar power plants have to be perfectly designed to optimize electricity generation, and their placement must be as suitable as possible for the meteorological conditions. Clouds are the most mitigating factor in solar energy production and their study is decisive in locating the plant. Apart from the importance of studying clouds before building the solar plants, cloud detection is equally decisive in adapting plant operation to cloud types during solar power plant operation.This adaptation benefits plant performance and allows electricity management to be integrated into the electricity grid. Nonetheless, the majority of cloud studies determine atmospheric parameters, which are sometimes not available. In this work, we have developed an automatic, fully-exportable cloud classification model, where Bayesian network classifiers were applied to satellite images so as to determine the presence of clouds, classifying the sky as cloudless or with high, medium and low cloud presence. There was an average success probability of 90% for all sky conditions.

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Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images

Cited by 106 publications

References 32 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

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

The application of Bayesian network classifiers to cloud classification in satellite images

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