A CRO-species optimization scheme for robust global solar radiation statistical downscaling

Salcedo‐Sanz, Sancho; Jiménez-Fernández, Silvia; Aybar-Ruíz, Adrián; Casanova-Mateo, C.; Sanz-Justo, J.; García‐Herrera, Ricardo

doi:10.1016/j.renene.2017.03.079

Cited by 33 publications

(9 citation statements)

References 52 publications

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“…Salcedo-Sanz et al (2017) [94] studied the prediction of global solar radiation at a given point incorporating a multilayer perceptron trained with extreme learning machines. A coral reefs optimization algorithm with species was used to reduce the number of significant predictive variables.…”

Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Results in data from the Radiometric Observatory of Toledo (Spain), shows the good performance of this approach. [68] discusses a similar application of feature selection in a problem of solar radiation estimation. In this case a hybrid approach mixing a CRO algorithm for selecting the features and an ELM for obtaining the solar radiation prediction is proposed.…”

Section: Hybrid Elms Techniques 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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“…For example, Salcedo-Sanz et al [39], examined the usage of a species-optimizing coral reef algorithm to gain a decreased collection of important features to forecast GHI. Also, Yadav et al implemented a set of features to some specific input predictors and observed the parameters of latitude and longitude are having the slightest impact on the forecasting of solar radiation [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost

Alresheedi¹,

Abdullah²

2020

IJACSA

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More solar-based electricity generation stations have been established markedly in recent years as new and an important source of renewable energy. That is to ensure a more efficient, reliable integration of solar power to overcome several challenges such as, the future forecasting, the costly equipment in the metrological stations. One of the effective prediction methods is Artificial Neural Networks (ANN) and the Boruta algorithm for optimal attributes selection, to train the proposed prediction model to obtain high accurate prediction performance at a lower cost. The precise goal of this research is to predict the Global Horizontal Irradiance (GHI) by building the ANN model. Also, reducing the total number of GHI prediction attributes/features consequently reducing the cost of devices and equipment required to predict this important factor. The dataset applied in this research is real data, collected from 2015-2018 by solar and meteorological stations in KSA. It provided by King Abdullah City for Atomic and Renewable Energy (KA CARE). The findings emphasize the achievement of accurate predictions of solar radiation with a minimum cost, which is considered to be highly important in KSA and all other countries that have a similar environment.

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A CRO-species optimization scheme for robust global solar radiation statistical downscaling

Cited by 33 publications

References 52 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

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

Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost

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