Energy-Social Manufacturing for Social Computing

Download PDF
Open Webpage

Pengfei Zhao, Shuangqi Li, Yanjia Wang, Paul Jen-Hwa Hu, Chenye Wu, Zhidong Cao, Faith Xue Fei

Published: 2024, Last Modified: 07 Jan 2026IEEE Trans. Comput. Soc. Syst. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This article explores social manufacturing (SM) within cyber–physical–social systems (CPSSs), leveraging artificial intelligence (AI) to revolutionize energy prosumer networks. We introduce a blockchain-enabled operation and management mechanism for energy systems, incorporating energy aggregators for efficient transaction audits and employing consortium blockchain and proof-of-work for enhanced security. Guided by social governance principles and utilizing the soft actor–critic (SAC) approach for handling renewable generation and load demand uncertainties, our method offers a resilient and cost-effective solution. Simulated case studies reveal a 16.7% reduction in audit costs and a 2.4% increase in peer-to-peer transactions, highlighting improved network synergy. Our approach also reduces redundant trading by 6.5% and cuts operational costs by up to 6%, demonstrating the effectiveness of blockchain in improving cost-efficiency and enhancing social governance and security in energy manufacturing systems. The findings of this study contribute a novel vista to the ongoing discourse in SM, illustrating the formidable potential of advanced information and AI technologies in amplifying the operational acumen of contemporary manufacturing ecosystems.