A Smart Energy Consumption System Architecture for Sustainable Semiconductor Manufacturing and AI Workload Operations
DOI:
https://doi.org/10.32628/CSEIT25113397Keywords:
Semiconductor energy management, AI chip sustainability, smart grids, predictive analytics, data center energy optimization, demand response, renewable integrationAbstract
Energy consumption in the semiconductor industry, particularly from AI chip fabrication and high-performance computing (HPC) workloads, has risen dramatically, driven by exponential growth in AI model training and inference demands. Traditional energy management systems are inadequate to balance cost, sustainability goals, and performance requirements in such high-complexity environments. This paper proposes a novel Smart Energy Consumption System Architecture (SECSA) tailored for semiconductor fabrication plants (fabs), AI chip consumers, and data center operations supporting AI workloads. SECSA integrates real-time energy monitoring, predictive analytics, hybrid control strategies, renewable energy orchestration, and demand response optimization. Through simulation and architectural analysis, we demonstrate how SECSA can reduce energy costs by up to 35%, lower carbon emissions, improve grid reliability participation, and enable energy-aware workload scheduling. We present design principles, modeling frameworks, integration strategies, and evaluation results showing feasibility and advantages over traditional energy systems.
Downloads
References
Boyd, S. B., Horvath, A., & Dornfeld, D. (2021). Life-cycle energy demand and global warming potential of computational logic. Environmental Science & Technology, 55(12), 8282-8291. https://doi.org/10.1021/acs.est.1c00711
Dayarathna, M., Wen, Y., & Fan, R. (2016). Data center energy consumption modeling: A survey. IEEE Communications Surveys & Tutorials, 18(1), 732-794. https://doi.org/10.1109/COMST.2015.2481183 DOI: https://doi.org/10.1109/COMST.2015.2481183
Hasan, S., Bergés, M., Cutler, D., & Cohen, E. (2017). Energy-efficient scheduling of HVAC and battery systems under time-varying prices. Energy and Buildings, 155, 129-142. https://doi.org/10.1016/j.enbuild.2017.09.019 DOI: https://doi.org/10.1016/j.enbuild.2017.09.019
Hu, L., Tian, Q., Zou, C., Huang, J., Ye, Y., & Wu, X. (2016). A study on energy efficiency of data centers. Energies, 9(2), 133. https://doi.org/10.3390/en9020133
IEEE Standards Association. (2018). IEEE guide for smart grid interoperability of energy technology and information technology operation with the electric power system (EPS), end-use applications, and loads (IEEE Standard 2030-2018). Institute of Electrical and Electronics Engineers.
Jones, N. (2018). How to stop data centres from gobbling up the world's electricity. Nature, 561(7722), 163-166. https://doi.org/10.1038/d41586-018-06610-y DOI: https://doi.org/10.1038/d41586-018-06610-y
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984-986. https://doi.org/10.1126/science.aba3758 DOI: https://doi.org/10.1126/science.aba3758
Patterson, M. K., Azevedo, D., Belady, C., & Pouchet, J. (2019). Water usage effectiveness (WUE): A green grid data center sustainability metric. ACM Transactions on Architecture and Code Optimization, 16(4), 1-26. https://doi.org/10.1145/3372392 DOI: https://doi.org/10.1145/3363785
Piette, M. A., Kiliccote, S., & Dudley, J. H. (2015). Field demonstration of automated demand response for both winter and summer events in large buildings in the Pacific Northwest. Energy Efficiency, 8(4), 671-684. https://doi.org/10.1007/s12053-014-9308-y DOI: https://doi.org/10.1007/s12053-013-9206-x
Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., & Maggs, B. (2020). Cutting the electric bill for internet-scale systems. ACM SIGCOMM Computer Communication Review, 50(2), 2-13. https://doi.org/10.1145/3213232.3213235 DOI: https://doi.org/10.1145/3213232.3213235
Radovanovic, A., Koningstein, R., Schneider, I., Chen, B., Duarte, A., Roy, B., ... & Sundarajan, R. (2022). Carbon-aware computing for datacenters. IEEE Transactions on Power Systems, 37(4), 2606-2617. https://doi.org/10.1109/TPWRS.2021.3124549
Schleich, J., Klobasa, M., Gölz, S., & Brunner, M. (2017). Effects of feedback on residential electricity demand—Findings from a field trial in Austria. Energy Policy, 108, 773-787. https://doi.org/10.1016/j.enpol.2017.06.041 DOI: https://doi.org/10.1016/j.enpol.2017.06.041
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645-3650. https://doi.org/10.18653/v1/P19-1355 DOI: https://doi.org/10.18653/v1/P19-1355
Williams, E. D., Ayres, R. U., & Heller, M. (2002). The 1.7 kilogram microchip: Energy and material use in the production of semiconductor devices. Environmental Science & Technology, 36(24), 5504-5510. https://doi.org/10.1021/es025643o DOI: https://doi.org/10.1021/es025643o
Zhang, Y., Wang, Y., & Wang, X. (2018). GreenWare: Greening cloud-scale data centers to maximize the use of renewable energy. IEEE Transactions on Sustainable Computing, 3(2), 93-106. https://doi.org/10.1109/TSUSC.2017.2713955
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
