结合强大的模型预测控制和深度学习的潜力,可以提高能源系统的控制性能和适应性
Xiaowen Lv1, Ali Basem2, Mohammadtaher Hasani3
1College of Information Science and Technology, ZheJiang ShuRen University, Hangzhou, 310015, China.
Scientific reports
|April 1, 2025
概括
这项研究将强大的模型预测控制 (RMPC) 与先进能源系统的深度学习相结合. 综合方法提高了控制精度,并降低了热电联产 (CHP) 等应用中的能源消耗.
科学领域:
- 能源系统工程 能源系统工程
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 传统的能源系统面临着动态条件和操作员和度的挑战.
- 优化热电联产 (CHP),电转和电转气甲系统需要先进的控制策略.
- 将预测控制与自适应学习相结合,为提高绩效提供了一个有希望的途径.
研究的目的:
- 调查能源系统中强大的模型预测控制 (RMPC) 和深度学习的协同集成.
- 提高复杂能源应用的性能,适应性和运营效率.
- 与传统方法相比,量化控制精度和能源消耗方面的改进.
主要方法:
- 开发一个综合框架,结合RMPC和深度学习算法.
- 对拟议系统的基于模拟的分析,应用于热电联产,电力转化和电力转化气体甲系统.
- 对传统的控制策略进行比较评估,以评估绩效的提高.
主要成果:
- 集成的RMPC-Deep Learning系统在控制准确度上显示了8.02%的改进.
- 实现了12.14%的能源消耗显著减少.
- 该系统有效地应对了操作员和等挑战,在动态条件下证明了其稳健性.
结论:
- 整合RMPC和深度学习为优化能源管理提供了强大且可适应的解决方案.
- 这种混合方法在复杂的能源应用中显著提高了控制精度和运营效率.
- 这些发现强调了将先进的控制理论与人工智能融合为未来能源系统的变革潜力.
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