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The integration of artificial intelligence (AI) into adaptive and autonomous systems announces a new era for Industry 5.0. The concept emphasizes human-centric, sustainable, and intelligent industrial processes. This study presents a comprehensive framework that combines supervised machine learning and generative modelling to predict and synthesize load types in industrial energy consumption data. A TinyML achieved a prediction accuracy of 95% in categorizing load types, such as light load, medium load, and maximum load, when compared to another ensemble model that achieved 94%, a GRU model at 82.8%, an LSTM with attention at 90.3%, and a traditional LSTM at 85.6%. Furthermore, a CTGAN-based tabular data generator was integrated to simulate realistic energy consumption patterns and facilitate advanced 'what if' analysis without the constraints of original data availability. The key features influencing load prediction included NSM, usage hours, and energy consumption. In comparison to the traditional deep learning model, the proposed work is lightweight because it utilizes a tiny machine learning model for load classification and employs a generative adversarial network to effectively define the CO2 emission rate and power consumption process within the industry, all while operating in a resource-constrained environment without requiring additional resources. This work highlights the crucial role of AI-driven autonomous systems in enhancing operational flexibility, promoting sustainability, and facilitating real-time human-AI collaboration in Industry 5.0 environments.
Muthusamy et al. (Mon,) studied this question.