整合软计算方法,以优化住宅建筑的热能性能
PloS one
|September 8, 2023
概括
这项研究引入了使用人工神经网络和五个优化器预测建筑热负荷的新型混合算法. 贝卢加优化算法在估计年度热能需求方面表现出最高的准确性.
科学领域:
- 建筑物的能源性能分析.
- 在工程领域的计算智能.
- 可持续的建筑设计设计
背景情况:
- 准确预测建筑热负荷对于优化建筑设计和能源效率至关重要.
- 虽然使用机器学习模型,但热负荷估计的先进技术需要进一步研究.
研究的目的:
- 引入和评估用于估计建筑热负荷的新型混合算法.
- 用五种不同的优化算法集成的人工神经网络的性能进行比较.
主要方法:
- 开发了混合预测模型,将人工神经网络与阿基米德优化算法 (AOA),白优化 (BWO),取证调查 (FBI),蛇优化器 (SO) 和短暂搜索算法 (TSO) 结合起来.
- 使用平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE) 和确定系数 (R2) 评估模型准确性.
- 根据预测准确度对集成算法的性能进行了排名.
主要成果:
- 所有混合模型都实现了高精度,相对误差低于5% (MAPE) 和相关性高于92% (R2).
- 贝卢加优化 (BWO) 算法在预测年度热能需求方面表现出最高的准确性.
- 阿基米德优化算法 (AOA) 和蛇优化器 (SO) 显示了第二好的性能,其次是基于法医的调查 (FBI) 和暂时搜索算法 (TSO).
结论:
- 拟议的混合算法,特别是那些将人工神经网络与BWO等先进优化器集成的算法,对于可靠的建筑热负荷估计是有效的.
- 这些发现为选择合适的人工智能技术提供了洞察力,用于复杂建筑物的能源性能分析.
- 该研究强调了新型优化算法的潜力,提高了建筑能源模拟的准确性.
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