智能模糊 Petri 基于网络的温度控制框架,用于减少建筑物的能源消耗
Wael Deabes1, Kheir Eddine Bouazza2, Wasl Algthami1
1Department of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究介绍了沙特阿拉伯空调系统的模糊培养网控制器,显著减少了能源消耗. 与传统方法相比,智能系统能耗降低了94%.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 沙特阿拉伯日益增长的能源需求需要提高能源效率.
- 传统的温度控制系统在与非线性,时间变化的动态作斗争.
- 空调系统是主要的能源消费者,需要优化控制.
研究的目的:
- 为空调系统开发一个智能节能框架.
- 为了优化能源利用和保持室内舒适,使用先进的控制.
- 在动态环境中解决传统控制器的局限性.
主要方法:
- 提出了一个模糊的培养网 (FPN) 控制器,集成了模糊的逻辑 (FL) 和培养网 (PN).
- 实施了用于温度设置授权的用户识别.
- 使用FL和PN监控设置并将其传输到PID控制器.
- 集成了FPN控制器与可转换频率空调压缩机.
主要成果:
- 与单独使用PN相比,FPN控制器的能耗降低了94%.
- 仅使用PN控制器,就能减少25%的能源消耗.
- 固定频压缩机增加了40%的能源消耗.
结论:
- 将FL与PN集成,可以显著提高交流系统的能源效率.
- FPN控制器为优化能源使用和舒适度提供了一个有前途的解决方案.
- 拟议的智能框架显示了可观的节能潜力.
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