Agnostic deep neural network approach to the estimation of hydrogen production for solar-powered systems

Mert, İlker

doi:10.1016/j.ijhydene.2020.11.161

Cited by 33 publications

(5 citation statements)

References 40 publications

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“…These tools have the potential to significantly improve the design and operation of production equipment, as well as predict and prevent equipment failures [135]. Figure 26 presents several AI technologies that have been used in these systems [136,137]. Response surface methodology (RSM) and artificial neural networks (ANNs) have demonstrated their superior predictive accuracy in modeling complex nonlinear bioprocesses, making them more efficient tools for optimizing the design and operation of production equipment.…”

Section: Recent Progress In Artificial Intelligence and Additive Manu...mentioning

confidence: 99%

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

Adeli,

Nachtane,

Faik

et al. 2023

Applied Sciences

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As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them widely. The goal of this review paper is to give a complete overview of the latest technology for the manufacture and storage of hydrogen and ammonia. This paper deals with hydrogen and ammonia synthesis and storage. It examines the most recent technological breakthroughs in areas such as electrolysis, reforming, C-ZEROS, HYSATA, DAE, sulfide, and SRBW, as well as novel storage techniques, such as solid-state storage, plasma kinetics, and POWERPASTE. This article examines the history of ammonia production and discusses some of the newer and more sustainable techniques for producing ammonia, such as electrochemical and biological approaches. This study also looks at how artificial intelligence (AI) and additive manufacturing (AM) could be used to revolutionize the way green hydrogen and ammonia are produced, with an emphasis on recent breakthroughs in AI-assisted catalyst design and 3D-printed reactors, as well as considering major investments in the shift to green energy, such as Moroccan government programs, and how they may affect future hydrogen and ammonia production.

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Section: Recent Progress In Artificial Intelligence and Additive Manu...mentioning

confidence: 99%

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

Adeli,

Nachtane,

Faik

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…To validate the proposed deep learning hybrid stacked LSTM sequence-to-sequence autoencoder (i.e., SAELSTM) model, we adopted popular Machine Learning models: (i) Deep Neural Networks (DNN) as extensions of artificial neural network ( [43,[71][72][73][74][75][76][77]), (ii) Gradient Boosting Regressor (GBM) as an ensemble-based Machine Learning model [78][79][80][81], (iii) Random Forest Regression (RFR) as an ensemble-based Machine Learning model that uses an ensemble of Decision Trees to predicts outcomes [82][83][84][85][86][87][88][89][90], (iv) Extremely Randomized Trees Regression model (ETR) that uses bagging [91], and (v) the Adaptive Boosting Regression (ADBR) that aims to adaptively solve complex problems [10,[92][93][94][95][96].…”

Section: Benchmark Modelsmentioning

confidence: 99%

Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results

et al. 2022

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We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.

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“…The prediction of RES's resources such as wind velocity, solar radiation and ambient temperature was carried out in the literature [8][9][10][11]. Zhang et al [8] used weather forecast and ANN model to predict solar radiation, ambient temperature and wind velocity.…”

Section: Characteristics Prediction Of Renewable Resourcesmentioning

confidence: 99%

“…Zhang et al [8] used weather forecast and ANN model to predict solar radiation, ambient temperature and wind velocity. Mert [9] took the observed and forecasted solar radiation values as the core dataset to train the ANN and agnostic deep learning (DL) models to predict a hydrogen production system supported by PV technology. It showed that the DL model was well-matched with the observed data and its value of the coefficient of determination was 96.26%.…”

Section: Characteristics Prediction Of Renewable Resourcesmentioning

confidence: 99%

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Yan

Agbossou

et al. 2022

J. Phys.: Conf. Ser.

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In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik’s weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.

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Agnostic deep neural network approach to the estimation of hydrogen production for solar-powered systems

Cited by 33 publications

References 40 publications

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results

Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

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