Estimation of solar radiation using modern methods

Karaman, Ömer Ali; Ağır, Tuba Tanyıldızı; Arsel, İsmail

doi:10.1016/j.aej.2020.12.048

Cited by 41 publications

(18 citation statements)

References 38 publications

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“…This is attributed to the limitations of the availability of some parameters considered for calculations. In this context, ANNs have been promoted as a modern and accurate approach with the sensor nodes to be benefited from [23].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optimal Neural Network for Predicting Solar Energy in Sensor Units Based on a Cascaded Input/Structure Direct Optimization

et al. 2022

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The sensor units are considered one of the significant technologies that use solar energy as an assistant power source to the batteries. Despite their advantages over the other forms of renewables, solar energy has an intermittent nature which negatively affects the operation of these units. Reaching an effective operation ensuring sustainable units requires a prior prediction of the harvested solar energy. Artificial neural networks (ANNs) appeared recently as a promising prediction approach with those units. This is attributed to the high accuracy compared to the conventional stochastic and statistical ones. Till now, the optimal neural network that fits with sensor units has not been precisely determined. This paper is aimed at finding the optimal neural network that would be applied with solar-supplied sensor units. This is performed by applying a cascaded input/structure direct optimization. The optimization process handles the aspects of accuracy, computational efforts, and complexity. It mainly identifies the type and number of parameters that would be utilized as inputs in the first stage. Then, it optimizes the structure by addressing the number of hidden layers and hidden neurons. The corresponding analysis has been implemented for premeasured real data over five-year time period. The results showed that the optimal neural network can be achieved by using three input parameters which are the air temperature (AT), the relative humidity (RH), and the zenith angle ( θ z ). For the structure, it has been concluded that the proposed optimal ANN should have two hidden layers with ten neurons in each of them. Lastly, the proposed optimal ANN was verified against the associated prediction error which is minimized to less than 2%.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optimal Neural Network for Predicting Solar Energy in Sensor Units Based on a Cascaded Input/Structure Direct Optimization

et al. 2022

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“…Analysis of the results obtained in a real problem of solar radiation estimation at Toledo, Spain, has been carried out. In [53] the authors compare standard ELMs vs ANNs for solar radiation estimation. Networks are trained with data acquired from Karaman province (Turkey) in the period 2010-2018.…”

Section: Solar Energy 321 Elms In Solar Radiation Prediction Problemsmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

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Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic-

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“…The sum of these two radiations is also called global solar radiation. Solar radiation is measured using devices such as a pyranometer, pyrheliometer, and solarmeter [ 17 ]. However, it is not practically possible to place these measurement devices in all regions due to their huge cost, measurement difficulties, and calibration problems.…”

Section: Introductionmentioning

confidence: 99%