Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023)

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear n...

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Bibliographic Details
Published in:Energy strategy reviews 2024-05, Vol.53, p.101409, Article 101409
Main Authors: Hosseini, Eghbal, Al-Ghaili, Abbas M., Kadir, Dler Hussein, Gunasekaran, Saraswathy Shamini, Ahmed, Ali Najah, Jamil, Norziana, Deveci, Muhammet, Razali, Rina Azlin
Format: Article
Language:English
Subjects:
Deep learning
Energy applications
Meta-heuristics
Renewable energy
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Summary:The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include: 1. Overview of recent advancements in MHs, DL, and integration. 2. Coverage of trends from 2018 to 2023. 3. Introduction of Alpha metric for performance evaluation. 4. Innovative framework harmonizing MHs with DL for energy problems. •Overview of recent advancements in Meta-Heuristics, Deep Learning in energy applications.•Covers 2018-2023 trends, providing insights into recent innovations in energy technologies.•Introduces Alpha metric for evaluating energy tech adoption and performance globally.•Proposes a framework integrating Meta-Heuristics and Deep Learning for energy solutions.
ISSN:2211-467X
DOI:10.1016/j.esr.2024.101409