相关实验视频
Updated: Jan 13, 2026

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Design and Optimization Strategies of a High-Performance Vented Box
Published on: June 9, 2023
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一个改进的非洲优化算法,用于占用驱动的智能建筑中的能源舒适管理
Ghulam Fizza1,2, Kushsairy Kadir1, Haidawati Nasir3
1Department of Electrical and Electronic Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), 53100, Selangor, Malaysia.
Scientific reports
|January 8, 2026
概括
这项研究引入了一种新的算法,MTV-AVOA,通过考虑占用率来优化智能建筑的舒适性和能源使用. 它显著提高了室内环境质量 (IEQ),并比其他方法减少了能源消耗.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 智能建筑旨在优化室内环境质量 (IEQ) 和能源效率.
- 现有的优化方法往往忽略了占用动态,导致低于最佳舒适度和浪费能量.
研究的目的:
- 开发和评估一个新的优化算法,基于多试验矢量的非洲优化算法 (MTV-AVOA),用于智能建筑.
- 通过结合占用动态来共同优化温度,湿度,照明和空气质量.
主要方法:
- 提出了MTV-AVOA与一个占用驱动的约束处理机制.
- 用528小时的智能办公室数据集对比MTV-AVOA与四个最先进的算法.
主要成果:
- 在MTV-AVOA的平均舒适度指数中,占用时间为0.8026,非占用时间为0.750.
- 该算法比未优化的情况 (0.665) 显著改进,并减少了能源消耗 (占用: 638.10 kWh,非占用: 528.20 kWh).
结论:
- 通过将占用数据整合到IEQ优化中,MTV-AVOA有效地平衡了乘客舒适度和能源效率.
- 拟议的算法有助于开发更可持续的智能建筑.
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