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All Journal JOIV : International Journal on Informatics Visualization Journal of Emerging Science and Engineering
Dong, Van Huong
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Computer Science & IT Engineering Environmental Science

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Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; Le, Huu Cuong; Nguyen, Duc Chuan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2637

Abstract

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
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.
Optimized conversion of waste vegetable oil to biofuel with Meta heuristic methods and design of experiments Dong, Van Huong; Sharma, Prabhakar
Journal of Emerging Science and Engineering Vol. 1 No. 1 (2023)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2023.4

Abstract

Biodiesel generated from waste cooking oil (WCO) shows enormous potential for accomplishing SDGs and embracing circular economy principles. This strategy coincides with SDGs 7 and 12, which promote clean energy along with ethical consumerism, by converting waste cooking oil into biofuel. It reduces dependency on fossil fuels, reduces emissions, and promotes sustainable energy sources. Furthermore, using WCO biodiesel adheres to the circular economy concept, reducing waste and pollution while conserving resources (SDGs 12, 14, and 15). To optimize this process, a hybrid technique comprising RSM, ANOVA, and particle swarm optimization is being explored. Researchers achieved 90% biodiesel production employing this technology, encouraging both eco-friendly energy and resource-efficient practices. The optimized parameters produced remarkable results: 82.98% biodiesel generation with a reaction time of 101 minutes, 2% catalyst, and a methanol-to-oil ratio of 20%, demonstrating the potential of this integrated strategy.
Co-Authors Chung, Nghia Le, Huu Cuong Le, Thanh Tuan Le, Thi Thai Nguyen, Duc Chuan Nguyen, Tien Han Paramasivam, Prabhu Sharma, Prabhakar