Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis

•This paper analyzes research on sustainable artificial intelligence for sustainable energy.•We offer a novel contextual topic modeling combining LDA, BERT and clustering.•We complemented the findings with a cluster-based content analysis.•This paper identified eight dominant topics within the field.•We recommended 14 potential future research strands based on the observed gaps. 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, 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. In addition to its theoretical contribution to scientific research on sustainable artificial intelligence in energy management, the research utilizes a novel topic modeling method in exploring scientific texts and identifying challenges and possible solutions. A variety of solutions was incorporated, including huggingface tool or elbow method, to address these challenges.