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Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions

Allal, Zaid, Noura, Hassan N., Salman, Ola, Chahine, Khaled·2024·Journal of Environmental Management

DOI 10.1016/j.jenvman.2024.120392

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Abstract

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems.

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Authors: Allal, Z., Noura, H. N., Salman, O., & Chahine, K.
Affiliations: Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies (FR)
American University of Beirut (LB)
American University of the Middle East (KW)
Corresponding author: Chahine, Khaled
ORCID: Allal, Zaid
Noura, Hassan N.
Salman, Ola
Chahine, Khaled
Venue: Journal of Environmental Management
Publisher: Elsevier BV
Published: 2024-03
Volume: 354
Pages: 120392
ISSN: 0301-4797 (ISSN-L)
DOI: 10.1016/j.jenvman.2024.120392
OpenAlex: W4392091061
Cited by: 130
References: 174
Citations by year: 2026: 48 · 2025: 62 · 2024: 20
FWCI: 13.45
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Journal of Environmental Management (publishedVersion)
PubMed (publishedVersion)
Funders: Agence Nationale de la Recherche (ANR-17-EURE-0002)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
Keywords: renewable energy systems, machine learning, renewable energy forecasting, Paris Agreement, sustainable energy, renewable energy technologies
MeSH terms: Machine Learning, Algorithms, Paris, Global Warming, Renewable Energy

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Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions

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