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All Journal JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Le, Thanh Tuan
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability Le, Thanh Tuan; Priya, Jayabal Chandra; Le, Huu Cuong; Le, Nguyen Viet Linh; Duong, Minh Thai; Cao, Dao Nam
International Journal of Renewable Energy Development Vol 13, No 2 (2024): March 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60119

Abstract

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.  
Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms Le, Thanh Tuan; Paramasivam, Prabhu; Adril, Elvis; Nguyen, Van Quy; Le, Minh Xuan; Duong, Minh Thai; Le, Huu Cuong; Nguyen, Anh Quan
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60387

Abstract

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects
Nanotechnology-based biodiesel: A comprehensive review on production, and utilization in diesel engine as a substitute of diesel fuel Le, Thanh Tuan; Tran, Minh Ho; Nguyen, Quang Chien; Le, Huu Cuong; Nguyen, Van Quy; Cao, Dao Nam; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60126

Abstract

As a sustainable replacement for fossil fuels, biodiesel is a game-changer in the energy sector. There is no strategy to minimize biodiesel's significance as a sustainable, clean fuel source in light of the increasing climate change and environmental sustainability concerns. Nevertheless, conventional biodiesel production methods often run into problems like inadequate conversion efficiency and inappropriate fuel properties, which impede their broad adoption. The revolutionary potential of nanotechnology to circumvent these limitations and revolutionize biodiesel consumption and production is explored in this review paper. There are new possibilities for improving biodiesel output and engine efficiency, thanks to nanotechnology, which can alter matter at the atomic and molecular levels. Using nano-catalysts, nano-emulsification processes, and nano-encapsulation procedures, researchers have made significant advances in improving biodiesel qualities such as stability, combustion efficiency, and viscosity. Through a comprehensive analysis of current literature and research data, this article elucidates the crucial role of nanotechnology in advancing biodiesel technology. By shedding light on the most current advancements, challenges, and potential future outcomes in nano-based biodiesel manufacturing and consumption, this review hopes to add to the growing corpus of knowledge in the field and inspire additional innovation. In conclusion, there is great hope for a sustainable energy future, increased economic growth, and reduced environmental impacts through the application of nanotechnology.  
Artificial intelligence applications in solar energy Le, Thanh Tuan; Le, Thi Thai; Le, Huu Cuong; Dong, Van Huong; Paramasivam, Prabhu; Chung, Nghia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2686

Abstract

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.
Co-Authors Cao, Dao Nam Chung, Nghia Dong, Van Huong Duong, Minh Thai Elvis Adril Le, Huu Cuong Le, Minh Xuan Le, Nguyen Viet Linh Le, Thi Thai Nguyen, Anh Quan Nguyen, Quang Chien Nguyen, Van Quy Paramasivam, Prabhu Priya, Jayabal Chandra Tran, Minh Ho