Key pathways for efficient solar thermal desalination

Huang, Jiahang; Zheng, Hongfei; Kong, Hui

doi:10.1016/j.enconman.2023.117806

Cited by 22 publications

(1 citation statement)

References 231 publications

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“…AI is leading the charge to transform sustainable energy management by providing solutions that use computational intelligence to solve difficult problems [31]. AI is, at its core, a collection of many technologies and approaches designed to allow robots to simulate human intellect [32].…”

Section: Ai In Sustainable Energy Contextmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

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Section: Ai In Sustainable Energy Contextmentioning

confidence: 99%