Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis

TL;DR

This paper introduces a novel combination of computational models and content analysis to identify key research themes in sustainable AI for energy, providing insights and future directions for interdisciplinary research.

Contribution

It presents a new contextual topic modeling approach integrating LDA, BERT, and clustering to analyze scientific literature on sustainable AI in energy.

Findings

Identified eight main research topics in sustainable AI for energy.

Proposed 14 future research directions based on identified gaps.

Contributed to literature on sustainable AI and energy convergence.

Abstract

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.