COGEniX:用于智能区的认知能源预测和碳意识调度的工业信息学框架
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia. asalmazroa@pnu.edu.sa.
Scientific reports
|November 27, 2025
概括
通过整合预测,强化学习控制和可解释性来降低成本和碳排放,COGEniX优化了工业能源管理. 这种统一的框架提高了高效,可持续的运营的适应性和透明度.
科学领域:
- 能源信息学 能源信息学
- 能源系统中的人工智能
- 工业脱碳技术 工业脱碳技术
背景情况:
- 工业能源管理面临着成本上升,碳密度和整合可再生能源等挑战.
- 传统的方法,如启发式规则和黑盒模型,缺乏适应性,透明度和部署性.
- 低效率,置消费和错过的脱碳机会在工厂中仍然存在.
研究的目的:
- 引入COGEniX,一种统一的认知能量信息学和执行框架.
- 解决工业能源管理现有方法的局限性.
- 为了实现适应性,碳意识和可审计的决策,实现利性和可持续性.
主要方法:
- COGEniX集成了校准的多地平线预测,基于强化学习 (RL) 的调度,可解释性和边缘部署.
- 基于补丁的变压器产生预测;软行为者-关键调度器优化成本和碳目标.
- SHAP 解释和 ONNX/TorchScript 导出确保可解释性和边缘可行的部署.
主要成果:
- 在Kaggle数据集的预测中,COGEniX实现了44.0%较低的MAE和39.3%较低的RMSE.
- 证明了15.3%的能源节约和18.1%的二氧化碳减少,具有边缘可行的延迟.
- 对CityLearn工业区的评估显示,运营成本降低了27%,二氧化碳排放量减少了31%.
结论:
- 通过统一预测,控制,可解释性和部署,COGEniX促进了工业能源管理.
- 该框架提供了适应性,碳意识和可审计的决策能力.
- 它有效地平衡了运营效率,成本降低和可持续性目标.
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