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Duong, Minh Thai
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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
Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine Le, Khac Binh; Duong, Minh Thai; Cao, Dao Nam; Le, Van Vang
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60724

Abstract

In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R² of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R² of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R² of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R² of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R² of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.
Co-Authors Cao, Dao Nam Elvis Adril Le, Huu Cuong Le, Khac Binh Le, Minh Xuan Le, Nguyen Viet Linh Le, Thanh Tuan Le, Van Vang Nguyen, Anh Quan Nguyen, Van Quy Paramasivam, Prabhu Priya, Jayabal Chandra