使用人工智能估计半干旱和干旱沙漠气候的住宅建筑的能源消耗
Hossam Wefki1, Rana Khallaf2, Ahmed M Ebid3
1Civil Engineering Department, Port Said University, Port Fouad, 42526, Egypt.
Scientific reports
|June 13, 2024
概括
这项研究使用人工智能开发了精确的建筑能源估计模型. 进化多项式回归 (EPR) 证明最有效,在预测能源消耗时达到2%的错误率.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 能源效率 能源效率 能源效率
背景情况:
- 准确的建筑能源估计对于可持续的设计和运行至关重要.
- 现有的模型可能缺乏精度或无法捕捉复杂的相互作用.
- 人工智能为提高预测能力提供了有希望的途径.
研究的目的:
- 开发和评估先进的预测模型,以准确地进行建筑能源估计.
- 为了比较基因编程 (GP),人工神经网络 (ANN) 和进化多项式回归 (EPR) 的性能.
- 确定影响建筑能耗的关键因素.
主要方法:
- 利用遗传编程 (GP),人工神经网络 (ANN) 和进化多项式回归 (EPR) 进行模型开发.
- 整合了16个能源效率措施,包括建筑尺寸,方向,外特性和气候控制设置.
- 使用根平均平方 (RMS),R平方 (R2) 和平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 进化多项式回归 (EPR) 模型表现出卓越的准确性,错误率为2%.
- 在测试的技术中,EPR被确定为最实用和最准确的模型.
- 建筑的大小,太阳能加热玻璃系数 (SHGC) 和目标室内温度显著影响了能源消耗 (87%的影响).
结论:
- 建筑能源估计 (EPR) 是用于建筑能源估计的高度准确和实用的方法.
- 建筑的大小,SHGC和恒温器设置是能源使用的主要驱动因素.
- 开发的模型为优化建筑能效提供了有价值的工具.
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