Unlocking renewable energy potential - Harnessing machine learning and intelligent algorithms

Aug 3, 2026

This review article examines the transformative potential of machine learning (ML) and intelligent algorithms in enabling renewable energy across solar, wind, biofuel, and biomass domains. It addresses key challenges including data variability, system inefficiencies, and predictive maintenance through ML integration. ML supports more accurate solar irradiance prediction and improved photovoltaic performance, while enhancing wind speed forecasting and turbine efficiency. For biofuel and biomass, ML optimizes feedstock selection, process parameters, yield forecasts, and real-time thermal conversion and operational management, while highlighting remaining issues in data quality, interpretability, compute demands, and system integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages31

Reading Time1 h 18 min

AccessFree to download

Introduction

Energy demand and fossil fuel impacts

Role of renewable energy and SDG-7 targets

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Contents list available at CBIORE journal website
International Journal of Renewable Energy Development
Journal homepage: [https://ijred.cbiore.id](https://ijred.cbiore.id) | | --- | --- | Review Article Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms Thanh Tuan Lea, Prabhu Paramasivamb,*, Elvis Adrilc, Van Quy Nguyend, Minh Xuan Lee,*, Minh Thai Duongf, Huu Cuong Led, Anh Quan Nguyend,* aInstitute of Engineering, HUTECH University, Ho Chi Minh City, VietNam bDepartment of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, TamilNadu-602105, India cMechanical Engineering Department, Politeknik Negeri Padang, West Sumatera, Indonesia dInstitute of Maritime, Ho Chi Minh city University of Transport, Ho Chi Minh City, VietNam eFaculty of Automotive Engineering, DongA University, Danang, VietNam fInstitute of Mechanical Engineering, Ho Chi Minh city University of Transport, Ho Chi Minh City, VietNam 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 realtime 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. Keywords: Machine learning; Artificial Intelligence; Renewable energy; Waste-to-energy path; Sustainable energy @ The author(s) . Published by CBIORE. This is an open access article under the CC BY-SA license ([http://creativecommons.org/licenses/by-sa/4.0/](http://creativecommons.org/licenses/by-sa/4.0/)). Received: 12th Feb 2024; Revised: 27th May 2024; Accepted: 7th June 2024; Available online: 25th June 2024 1. Introduction The economy has been growing quickly, and the need for energy is also rising quickly to meet people's daily demands and activities (Hoang et al., 2022d; Yoro et al. , 2021), in which the immediate result of the growing energy demand has been a notable rise in the quantity of electricity generated (IEA, 2022a; IRENA, 2013) . Due to the ever-increasing energy demand for human activities, the fossil fuel has been thoroughly exploited and used (Nguyen et al. , 2021b; Zou et al. , 2016) . As a result, the use of these fossil fuels contribute to numerous other serious environmental issues, which the emissions of greenhouse gases produced by these fossil fuels may be partly blamed for both the phenomena of climate change and global warming (Bakır et al. , 2022; Martins et al. , 2019; V. G. Nguyen et al. , 2023a; Nguyen et al. , 2021a) . Meanwhile, the cornerstones of a sustainable future are the development of technology that uses renewable energy sources and the execution of a policy that seeks to reach "zero carbo

FAQs

Which renewable energy areas does the review focus on for machine learning applications?

The review covers solar, wind, biofuel, and biomass energy domains, explaining how ML models can support each area’s performance and management.

How does machine learning improve performance in solar energy systems?

Machine learning improves solar irradiance prediction accuracy and helps maximize photovoltaic system performance.

What challenges remain despite the advantages of ML in renewable energy?

Key remaining issues include data quality, model interpretability, computing requirements, and difficulties integrating ML solutions with existing systems.

Aug 3, 2026

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