Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.
翻译:在对可持续能源和人工智能解决方案的辩论不断增多的同时,世界目前正在讨论人工智能的伦理道德及其对社会和环境可能产生的负面影响。在这些论点中,提出了可持续的AI,目的是推进可持续性之路,如可持续能源。在本文中,我们提出了一个新的背景主题模型,将LDA、BERT和集群结合起来。然后,我们将这些计算分析与相关科学出版物的内容分析结合起来,以确定能源方面可持续AI科学研究的主要学术专题、分主题和跨主题。我们的研究确定了八个主要议题,包括可持续建筑、基于AI的城市水管理DSS、气候人工智能、农业4、AI与IoT的趋同、AI对可再生能源技术的评估、智能校园和工程教育以及AI的优化。我们随后根据观察到的理论差距建议了14个潜在的未来研究领域。从理论上讲,这一分析有助于现有的关于可持续AI和可持续能源的文献,实际上,它打算作为能源工程师和科学家、AI科学家和社会科学家在AI和能源研究中扩大可持续性的知识。