Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality
Abstract Achieving carbon peak and carbon neutrality requires industries to enhance energy efficiency and optimize resource utilization. Traditional energy management methods rely on rule-based or static optimization approaches, which struggle to adapt to dynamic production environments and fluctuat...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00901-9 |
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| author | Hui Bai Yiyi Chen Hua Bai Meiling Liu Yu Fan |
| author_facet | Hui Bai Yiyi Chen Hua Bai Meiling Liu Yu Fan |
| author_sort | Hui Bai |
| collection | DOAJ |
| description | Abstract Achieving carbon peak and carbon neutrality requires industries to enhance energy efficiency and optimize resource utilization. Traditional energy management methods rely on rule-based or static optimization approaches, which struggle to adapt to dynamic production environments and fluctuating energy demands. These limitations lead to inefficient energy use, increased operational costs, and challenges in meeting sustainability goals. This research introduces Deep Learning-based Green Optimization for Enterprise Production (DeepGreen-Opt), a deep learning-driven framework designed to analyze energy consumption patterns, predict demand, and optimize resource allocation in real time. The DeepGreen-Opt framework integrates Long Short-Term Memory (LSTM) for accurate energy consumption forecasting and Adaptive Hybrid Particle Swarm Optimization (AHPSO) for dynamic energy optimization. A fuzzy logic-based decision system is incorporated to enhance adaptability under uncertain conditions, enabling real-time adjustments to fluctuating energy demands. The DeepGreen-Opt framework was specifically validated across multiple industrial sectors, including automotive manufacturing, steel production facilities, and chemical processing plants, where intelligent energy management demonstrates significant operational improvements. By implementing DeepGreen-Opt, enterprises can achieve cost-effective production while aligning with sustainability objectives. The framework ensures energy-efficient operations, reducing resource waste and improving production efficiency. Experimental validation on industrial datasets demonstrates a 15% increase in energy efficiency and a 12% improvement in overall production performance compared to existing approaches. This research highlights the potential of DeepGreen-Opt in industrial energy management, providing a foundation for future advancements in intelligent and sustainable production processes. |
| format | Article |
| id | doaj-art-b4bf01269f404a488a61a836a41f8dab |
| institution | DOAJ |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-b4bf01269f404a488a61a836a41f8dab2025-08-20T03:04:07ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-07-0118112510.1007/s44196-025-00901-9Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon NeutralityHui Bai0Yiyi Chen1Hua Bai2Meiling Liu3Yu Fan4College of Economics and Trade, Hunan Biological and Electromechanical PolytechnicCollege of Economics and Trade, Hunan Biological and Electromechanical PolytechnicCollege of Public Administration and Law, Hunan Agricultural UniversityCollege of Management, Changsha Medical UniversityChangsha Datong Xingsha Primary SchoolAbstract Achieving carbon peak and carbon neutrality requires industries to enhance energy efficiency and optimize resource utilization. Traditional energy management methods rely on rule-based or static optimization approaches, which struggle to adapt to dynamic production environments and fluctuating energy demands. These limitations lead to inefficient energy use, increased operational costs, and challenges in meeting sustainability goals. This research introduces Deep Learning-based Green Optimization for Enterprise Production (DeepGreen-Opt), a deep learning-driven framework designed to analyze energy consumption patterns, predict demand, and optimize resource allocation in real time. The DeepGreen-Opt framework integrates Long Short-Term Memory (LSTM) for accurate energy consumption forecasting and Adaptive Hybrid Particle Swarm Optimization (AHPSO) for dynamic energy optimization. A fuzzy logic-based decision system is incorporated to enhance adaptability under uncertain conditions, enabling real-time adjustments to fluctuating energy demands. The DeepGreen-Opt framework was specifically validated across multiple industrial sectors, including automotive manufacturing, steel production facilities, and chemical processing plants, where intelligent energy management demonstrates significant operational improvements. By implementing DeepGreen-Opt, enterprises can achieve cost-effective production while aligning with sustainability objectives. The framework ensures energy-efficient operations, reducing resource waste and improving production efficiency. Experimental validation on industrial datasets demonstrates a 15% increase in energy efficiency and a 12% improvement in overall production performance compared to existing approaches. This research highlights the potential of DeepGreen-Opt in industrial energy management, providing a foundation for future advancements in intelligent and sustainable production processes.https://doi.org/10.1007/s44196-025-00901-9Energy optimizationDeep learningCarbon peakCarbon neutralityEnterprise productionSmart manufacturing |
| spellingShingle | Hui Bai Yiyi Chen Hua Bai Meiling Liu Yu Fan Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality International Journal of Computational Intelligence Systems Energy optimization Deep learning Carbon peak Carbon neutrality Enterprise production Smart manufacturing |
| title | Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality |
| title_full | Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality |
| title_fullStr | Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality |
| title_full_unstemmed | Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality |
| title_short | Energy Optimization and Efficiency Improvement Model for Enterprise Production Process Based on Deep Learning Under the Background of Carbon Peak and Carbon Neutrality |
| title_sort | energy optimization and efficiency improvement model for enterprise production process based on deep learning under the background of carbon peak and carbon neutrality |
| topic | Energy optimization Deep learning Carbon peak Carbon neutrality Enterprise production Smart manufacturing |
| url | https://doi.org/10.1007/s44196-025-00901-9 |
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