Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Abstract

Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

A Recommendation System for Prosumers Based on Large Language Models

“…There are several innovative technologies and platforms in the energy sector designed to support prosumers, such as (a) HEMSs like Nest (Google Nest), well-known for its smart thermostats which learn schedules and preferences to optimize heating and cooling for energy efficiency [ 19 ], and Tesla Energy, which offers solar panels, solar roof and battery systems, integrating with a mobile app for energy monitoring and management [ 20 ]; (b) DR and energy optimization services, like OhmConnect, which rewards users for saving energy during peak hours, integrating with smart home devices to automate energy savings, and AutoGrid, which uses big data analytics and AI to offer DR, distributed energy resource management and energy storage optimization; (c) P2P energy trading platforms, like LO3 Energy (Exergy), a blockchain platform enabling LEM for P2P energy trading, and Power Ledger, which utilizes blockchain technology to facilitate energy trading, allowing consumers to buy and sell renewable energy directly [ 21 ]; (d) predictive analytics and AI for energy management, like Bidgely, which utilizes AI and machine learning (ML) to disaggregate energy data from smart meters, providing personalized energy insights and recommendations [ 22 , 23 ], and Tibber, a digital electricity supplier that uses AI to optimize electricity consumption for its customers, offering dynamic pricing based on real-time market conditions; (e) platforms integrating LLMs, which are a cutting-edge area of development and not yet sufficiently investigated. They have not been applied to prosumers’ energy systems and thus we identified a research gap.…”