A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

Nazhad, Seyed Hossein Hosseini; Lotfinejad, Mohammad Mehdi; Danesh, Malihe; Amin, Rooh Ul; Shamshirband, Shahaboddin

doi:10.1080/01431161.2017.1368098

Cited by 27 publications

(1 citation statement)

References 43 publications

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“…Results in time series from different parts of the World have shown the good performance of this approach. In [70] a comparison among different ELMs for predicting daily horizontal diffuse solar radiation in a region of southern Iran is carried out. The work discusses hybrid methods based on ELMs, such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM).…”

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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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

View full text Add to dashboard Cite

show abstract

Comprehensive review of solar radiation modeling based on artificial intelligence and optimization techniques: future concerns and considerations

Attar

Sattari

Prasad

et al. 2022

Clean Techn Environ Policy

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Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

Abdel-Fattah

Mokhtar

Abdo

2020

Environ Sci Pollut Res

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A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

Cited by 27 publications

References 43 publications

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

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

Comprehensive review of solar radiation modeling based on artificial intelligence and optimization techniques: future concerns and considerations

Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

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