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All Journal International Journal of Renewable Energy Development
Minh Thai Vu
Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh City
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Machine learning in solar energy systems: Methods, applications, and future directions Hoang Dat Do; Raghav Kumar Thakur; Xuan Manh Dinh; Do Duong Lam Le; Minh Thai Vu; Van Quy Nguyen; Ngoc Doanh Le
International Journal of Renewable Energy Development Vol 15, No 4 (2026): July 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62760

Abstract

In the present era, the ever-growing need for energy and the greenhouse gas emissions from fossil fuel burning have become a real challenge. Solar energy is an attractive option among various options available in renewable energy domain.  Solar energy systems are rapidly expanding, and that growth brings real challenges as they need to face challenges such as unpredictable output, constant changes, and complex operations. To handle these challenges and for smoother operation, Machine Learning (ML) can be useful as it can handle a large amount of data and keep everything running smoothly. In this review, a comprehensive overview of applying ML to solar energy is presented. The review will explore the working of existing ML techniques, covering both conventional as well as modern approaches. The key application areas are identified, ranging from forecasting and optimization to fault detection and energy management in integrated grids. It also discusses some important barriers like data inconsistency, the black-box nature of conventional ML models, and the difficulty in scaling up to real-world settings. On the brighter side, the review points to some exciting new directions like explainable AI, physics-informed learning, and real-time analytics. It is observed that it is a rapidly evolving field with marked shifting toward ML tools that are more flexible, explainable, and can be tuned into the bigger system. Overall, this review provides a combined and forward-looking perspective, offering actionable insights for the development of robust, scalable, and practically deployable ML solutions in solar energy systems.
Assessing energy policy effectiveness in Vietnam using multi-criteria decision making Thi Thuc Anh Duong; Viet Khai Le; Ngoc Doanh Le; Minh Thai Vu; Tien Dat Nguyen
International Journal of Renewable Energy Development Vol 15, No 3 (2026): May 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62318

Abstract

Vietnam's energy transition is strong, and therefore, Vietnam needs a policy framework appropriate to the current context to promote this sustainable transition. This study assesses energy policy alternatives in Vietnam in order to identify the best policy strategies for sustainable energy transition by using a hybrid Multi-Criteria Decision Making (MCDM) approach. The research basically applies a DEMATEL-VIKOR framework to discuss five policy alternatives: Renewable Energy Promotion (RP), Energy Efficiency and Demand-Side Management (EE&DSM), Grid Modernization (GM), Fossil Fuel Transition (FFT), and Institutional, Regulatory and Market Reform (IR). Seven criteria, from Economic Efficiency (EE) to Policy Consistency (PC), were used for evaluation. The analysis by DEMATEL points to the Institutional and Regulatory Effectiveness (IE) as being the most crucial causal driver with the highest prominence score of 3.84 and a net causality value of 0.53. These results give direct information to the VIKOR analysis, where IR (A5) is the best compromise solution with a perfect Q-index value of 0.00 and the lowest individual regret (R-index) value of 0.08. In comparison, Fossil Fuel Transition (FFT) ranked the worst with a Q-index of 1.00. Sensitivity analysis to prove the robustness of IR as the dominant policy for all decision-making parameters (v). The results have illustrated that the energy policy of Vietnam should place more emphasis on institutional strengthening and grid modernization (Q = 0.22) than stand-alone technological deployment to ensure a stable, efficient, and equitable energy transition.
Co-Authors Do Duong Lam Le Hoang Dat Do Ngoc Doanh Le Ngoc Doanh Le Raghav Kumar Thakur Thi Thuc Anh Duong Tien Dat Nguyen Van Quy Nguyen Viet Khai Le Xuan Manh Dinh