Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging

Martins, Bruno Juncklaus; Cerentini, Allan; Neto, Sylvio Luiz Mantelli; Chaves, Thiago Zimmermann Loureiro; Branco, Nicolas Moreira; Wangenheim, Aldo von; Rüther, Ricardo; Arrais, Juliana Marian

doi:10.1016/j.seja.2022.100019

Cited by 16 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract cloud cover parameters from the sky images, the widely employed Red-Blue Ratio (RBR) cloud detection algorithm is used [34,35]. This method classifies the image pixels into cloud and clear sky regions, creating a binary image based on the values of the red and blue channels.…”

Section: All-sky Image Segmentationmentioning

confidence: 99%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

View full text Add to dashboard Cite

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

show abstract

Section: All-sky Image Segmentationmentioning

confidence: 99%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…The hybrid method based on a self-organizing map (SOM theory) and grey wolf optimization (GWO) based general regression neural network (GRNN) has been proposed by the authors to predict the solar power output within a 24-hour time range (14) . In the review of nowcasting approaches for solar energy production, the authors have found that Artificial Neural Networks and Convolutional Neural Networks are the most commonly used techniques with the root mean squared error as the predominant error metric for model validation (15) . But, Neural Networks require a high volume of data and are computationally expensive in nature.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting of Weather Parameters Impacting Solar PV Output Using Grey System Model

Uma¹,

Sridhar²,

Sumathi³

2023

IJST

View full text Add to dashboard Cite

Objectives: To nowcast the weather parameters having a direct impact on the power output of the solar PV installations, with high prediction accuracy and a limited quantity of past data. Methods: In this study, the GM (1,1) model with Fourier series of error residuals has been proposed and used for forecasting the weather parameters namely Ambient temperature, Solar Photo Voltaic Module temperature, and Solar Irradiation. Real-time data has been used for showing the suitability of the proposed model to nowcast the weather parameters. The existing models like Autoregression and Double Exponential Smoothing are applied to the same data to prove the superiority of the GM (1,1) model with Fourier series of error residuals. Findings: It is found that the GM (1,1) model with Fourier series of error residuals is an apt model for nowcasting the weather parameters. The accuracy of the predicted result of this model on the real-time data ascertains the appropriateness of this model for nowcasting. The precision of the prediction accuracy of GM (1,1) with the Fourier series of error residuals model is verified by comparing it with other time series prediction models such as Autoregression and Double Exponential Smoothing algorithms. Novelty: Using GM (1,1) with the Fourier series of error residuals model for nowcasting the weather data is novel when compared with the existing algorithms because for nowcasting the weather parameters with accuracy, many of the existing algorithms require a huge volume of past data and involve complex computation. On the other hand, GM (1,1) with the Fourier series of error residuals model requires only a limited measure of past data and involves simple computation. Moreover, the accuracy of prediction is significantly higher than the other models.

show abstract

“…In this context, an accurate forecasting of solar irradiation is necessary. Solar irradiation forecasting is performed by several methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14] that use numerical weather prediction (NWP), satellite imaging, ground-based systems, which are characterized by temporal-spatial scales. NWP has an advantage that solar irradiation can be forecasted at any place up to more than one week.…”

Section: Introductionmentioning

confidence: 99%

“…However, the accurate estimation of solar irradiation from the space is still a challenging issue. Ground-based systems forecast fluctuations by cloud shadows [3,4,[7][8][9][10][11]15,16] in solar irradiation for short duration of ∼30 min with the accurate solar irradiation measurement. The ground-based systems are divided into two types: the systems with pyranometer sensor networks [5,6] and the systems with all sky imagers (ASIs).…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of cloud layers and solar irradiations for all seasons in Toyohashi city, Japan

Bando,

Ito,

Wakisaka

et al. 2023

Renew. Energy Environ. Sustain.

View full text Add to dashboard Cite

Several solar irradiation forecasting methods are being developed toward economically efficient solar power generation. Among these, the method that utilizes all sky imagers to estimate the direction of cloud movement above certain observation points is able to forecast fluctuations in solar irradiation with localized temporal resolution (∼30 min) through observation of shadows cast by clouds. The one-layered cloud is usually assumed in forecasting the direction of cloud movement. However, in reality, multilayered clouds may move in different directions in the sky. While such multilayered clouds may have effect on forecast accuracy, the extent to which multilayered clouds affect the solar irradiation and the generated solar power has never been reported. Therefore, the purposes of this study are (1) to give the dataset to know the effect of multilayered clouds and (2) to decide whether the multilayered clouds have to be considered in forecasting of solar irradiation. We analyzed the number of cloud layers and their effect on the solar irradiation, the generated solar power, and the clearness index through statistical analysis for all seasons in Toyohashi city, Japan. The dataset used to analysis includes data with 4381 h. The analytical results show that multilayered clouds were observed for ∼40% of the daytime duration even in winter. In addition, the results show that multilayered clouds were observed even when the solar irradiation, the generated solar power, and the clearness index were high, which suggests that the effect of multilayered clouds must be considered. In addition, it will be shown that the maximum number of cloud layers to be considered is 3 from statistical analysis.

show abstract

Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging

Cited by 16 publications

References 57 publications

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Nowcasting of Weather Parameters Impacting Solar PV Output Using Grey System Model

Statistical analysis of cloud layers and solar irradiations for all seasons in Toyohashi city, Japan

Contact Info

Product

Resources

About