Deep Learning-Driven Industrial Energy Optimization: A Nationwide Framework for Reducing Operational Costs and Improving Manufacturing Profitability

  • Authors

    • Trang Huynh Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-V9I3P107

    Published 09-10-2026

  • Deep Learning, Industrial Energy Optimization, Smart Manufacturing, Energy Efficiency, Operational Costs, Manufacturing Profitability, Industry 4.0

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Manufacturing industries face growing pressure to reduce energy consumption while maintaining productivity, controlling operating costs, and improving profitability. This study develops a nationwide deep learning-driven framework for industrial energy optimization that integrates real-time manufacturing data, predictive analytics, and multi-objective decision support. The framework combines production, machine, environmental, maintenance, and energy-price variables to estimate industrial energy demand using artificial neural networks, convolutional neural networks, long short-term memory networks, gated recurrent units, and hybrid deep learning models. Model performance is evaluated using mean absolute error, root mean square error, mean absolute percentage error, and the coefficient of determination. Predicted energy demand is subsequently linked to an optimization layer that seeks to minimize energy consumption, peak demand, and operational costs while preserving production targets and improving manufacturing profitability. The study also evaluates sector-level and nationwide adoption scenarios to examine potential scalability across energy-intensive and discrete manufacturing industries. The proposed framework is intended to support smarter production scheduling, demand-response decisions, process control, and investment planning. By connecting deep learning-based energy prediction with operational and economic optimization, the study provides a unified approach for transforming industrial energy management from reactive monitoring into predictive and prescriptive decision-making, with implications for manufacturing competitiveness, national energy efficiency, and industrial policy.

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