Computational intelligence approach for modeling hydrogen production: a review
Sina Ardabili,Bahman Najafi,Shahaboddin Shamshirband,Behrouz Minaei Bidgoli,Ravinesh C. Deo,Kwok Wing Chau +5 more
TLDR
A clean energy source with a relatively low pollution footprint, hydrogen does not exist in nature as a separate element but only in compound forms as mentioned in this paper, and hydrogen is produced in the USA.Abstract:
Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced throug...read more
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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Amir Mosavi,Amir Mosavi,Amir Mosavi,Mohsen Salimi,Sina Ardabili,Timon Rabczuk,Shahaboddin Shamshirband,Annamária R. Várkonyi-Kóczy +7 more
TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.
Journal ArticleDOI
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources
TL;DR: Different types of Deep Learning algorithms applied in the field of solar and wind energy resources are discussed and their performance through a novel taxonomy is evaluated and a comprehensive state-of-the-art of the literature is presented leading to an assessment and performance evaluation.
Journal ArticleDOI
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
TL;DR: It is argued that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities and facilitate and fosters environmental governance.
Journal ArticleDOI
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou,Yingui Qiu,Shuangli Zhu,Danial Jahed Armaghani,Chuanqi Li,Hoang Nguyen,Saffet Yagiz +6 more
TL;DR: Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models, confirming that this hybrid S VM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
Journal ArticleDOI
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Sheraz Aslam,Sheraz Aslam,Herodotos Herodotou,Syed Muhammad Mohsin,Nadeem Javaid,Nouman Ashraf,Shahzad Aslam +6 more
TL;DR: A comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting, and discusses the datasets used to train and test the differentDL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Sustainable Hydrogen Production
TL;DR: Identifying and building a sustainable energy system are perhaps two of the most critical issues that today's society must address.
Book
Nature-Inspired Metaheuristic Algorithms
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.