Global Solar Radiation Prediction Methodology using Artificial Neural Networks for Photovoltaic Power Generation Systems

Kamadinata, Jane Oktavia; Ken, Tan Lit; Suwa, Tohru

doi:10.5220/0006248700150022

Cited by 6 publications

(7 citation statements)

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“…These are appreciated as solar water heating, agricultural studies, wood drying, photovoltaic, which designs some solar energy applications such as thermal load, evaluating potential power levels, solar radiation measurements, atmospheric energy balance studies and meteorological forecasts [15]. This energy could be converted into electricity which is recognized as another beneficial type of energy, using photovoltaic power generation systems to combat global warming [8].…”

Section: Related Workmentioning

confidence: 99%

“…This energy can be smoothly converted into electricity, another more usable form of energy, by using photovoltaic power generation systems to combat global warming. When photovoltaic power generation systems are connected to the electrical grid, predicting global solar radiation becomes significant to stabilize the entire network [8]. There are a wide range of algorithms and methods utilizing to predict the solar radiation.…”

Section: Introductionmentioning

confidence: 99%

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Global solar radiation prediction model with random forest algorithm

Kör

2021

THERM SCI

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Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These Predictive Models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global solar radiation prediction model with random forest algorithm

Kör

2021

THERM SCI

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“…Other variants of cloud detection on sky/cloud images captured by ground-based cameras are based on cloud segmentation [16], which is learning-based and not suitable for rapidly changing weather conditions. Some research involved artificial neural networks that depend on the datasets for a given area [17,18]. These approaches are only suitable for specific areas and often cannot be applied to all locations.…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Forecast Based on Cloud Movement Prediction

Radovan¹,

Šunde

Kučak

et al. 2021

Energies

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Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar unshadowing curve to the mean value of the irradiance predicted by the hydrometeorological institute for that hourly interval. Image processing for cloud detection and localization is performed using a computer vision library and the Java programming language. The algorithm developed in this work leads to improved accuracy and resolution of irradiance prediction for the next hour interval. The predicted irradiance curve can be used as a predicted reference for solar energy production in energy storage system optimization.

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“…Different methodologies utilizing two ANN have already been employed. Kamadinata et al [13] developed and compared two different ANN to first forecast cloud movement direction where the output of this ANN is utilized as input for the second ANN for predicting the GHI. The results of this study show a reduction of the computational effort capturing the trend of the GHI very well.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images

Crisosto

2019

International Journal of Photoenergy

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We present here a new method to predict cloud concentration five minutes in advance from all-sky images using the Artificial Neural Networks (ANN). An autoregressive neural network with backpropagation (Ar-BP) was created and trained with four years of all-sky images as inputs. The pictures were taken with a hemispheric sky imager fixed on the roof at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Firstly, a statistical method is presented to obtain key information of the pictures. Secondly, a new image-processing algorithm is suggested to optimize the cloud detection process starting with the Haze Index. Finally, the cloud concentration five minutes in advance at the IMUK is forecasted using machine learning methods. A persistence model forecast to provide a reference for comparison was generated. The results are quantified in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The new algorithm reduced both the RMSE and the MAE of the prediction by approximately 30% compared to the reference persistence model under diverse cloud conditions. The new algorithm could be used as a tool for the stable maintenance of the network for the transmission system operators, i.e., the primary control reserve (within 30 seconds) and the secondary control reserve (within 5 minutes).

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Global Solar Radiation Prediction Methodology using Artificial Neural Networks for Photovoltaic Power Generation Systems

Cited by 6 publications

References 0 publications

Global solar radiation prediction model with random forest algorithm

Global solar radiation prediction model with random forest algorithm

Solar Irradiance Forecast Based on Cloud Movement Prediction

Autoregressive Neural Network for Cloud Concentration Forecast from Hemispheric Sky Images

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