优化分布式能源资源在绿色建筑中的运行环境环境
Safdar Ali1, Khizar Hayat1, Ibrar Hussain1,2
1Department of Software Engineering, The University of Lahore, Main Campus, Lahore 54590, Pakistan.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种电源管理和控制 (PMC) 系统,可以优化能源使用和乘客舒适度. PMC系统有效地降低了能源消耗,同时保持或提高了乘客舒适度指数 (OCI).
科学领域:
- 能源管理系统 能源管理系统
- 人工智能在建筑自动化中的应用
- 可持续能源解决方案 可持续能源解决方案
背景情况:
- 有效的能源管理对于改善生活方式至关重要,但它面临着有限,昂贵的资源所带来的挑战.
- 现有的能源管理解决方案往往侧重于减少能源或乘客舒适度,而不是两者兼而有之.
- 能源管理的多目标性质要求平衡降低消耗与保持乘客舒适度.
研究的目的:
- 为绿色环境提出一种新的能源控制系统,即电力管理和控制 (PMC).
- 为了应对降低能源消耗,同时保持乘客舒适的多目标挑战.
- 开发一个混合优化框架,将遗传算法 (GA) 和粒子群优化 (PSO) 结合起来.
主要方法:
- 实施一套利用混合能源优化,能源预测和多重预处理的电源管理和控制 (PMC) 系统.
- 基因算法 (GA) 和粒子群优化 (PSO) 的融合,以提高能源管理.
- 基于模拟的验证控制室内执行器 (风扇,灯,交流,炉) 以评估性能.
主要成果:
- 与ABCKB,GAP,SOHP和PSO框架相比,PMC框架实现了优越的乘客舒适度指数 (OCI).
- PMC证明了与AEO框架相比较的OCI,但能源消耗显著降低.
- PMC的理想OCI为1,超过FA-GA模型的OCI为0.98,并且比ABCKB,GAP,PSO和AEO消耗更少的能量.
结论:
- 拟议的PMC框架有效地减少了能源利用,并提高了乘客舒适度指数 (OCI).
- PMC为绿色环境中的多目标能源管理提供了强大的解决方案.
- 通过模拟验证系统的性能,证明其控制室内环境的能力.
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