基于机器学习预测的光伏空气加热系统的生命周期评估和经济分析的比较研究
Songjie Xu1, Xinru Zhou2, Jiajie Ma3
1School of Accounting and Finance, Yantai Institute of Technology, Yantai, 264003, Shandong, China. xusongjie@yitsd.edu.cn.
光伏/热空气加热系统 (PV/T-AHS) 显示出比平板空气加热系统 (FP-AHS) 和电气空气加热系统 (E-AHS) 更好的环境性能. 这项研究使用机器学习和生命周期评估进行太阳能空气加热器分析.
科学领域:
- 可再生能源系统可再生能源系统
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 建筑加热系统对环境产生重大影响.
- 太阳能空气加热提供了一个可持续的替代方案.
- 环境和经济的比较评估对于系统的选择至关重要.
研究的目的:
- 评估和比较三个建筑空气加热系统的环境影响:光伏/热气加热系统 (PV/T-AHS),平板加热系统 (FP-AHS) 和电气加热系统 (E-AHS).
- 整合机器学习预测与生命周期评估 (LCA) 进行全面分析.
- 评估PV/T-AHS的经济可行性.
主要方法:
- 建筑加热的PV/T-AHS,FP-AHS和E-AHS的建造和测试.
- 开发一个直接合框架,将机器学习 (ML) 预测输出与LCA模型集成在一起.
- 使用卷积神经网络 (CNN) 预测系统运行时性能数据,实现0.99.2的R2.
- 在经济分析的同时,对系统寿命和区域进行敏感性分析.
主要成果:
- 该CNN模型准确预测了系统性能 (R2 = 0.99).
- 与FP-AHS (15.9314 mPt/年) 和E-AHS (14.6408 mPt/年) 相比,PV/T-AHS的环境总影响最低 (7.826 mPt/年).
- 随着所有系统的系统寿命的延长,环境影响减少;PV/T-AHS的回报期为1.56年.
结论:
- 在建筑加热应用中,PV/T-AHS在环境方面优于FP-AHS和E-AHS.
- 集成的ML-LCA框架为评估太阳能空气加热系统提供了一个强大的方法.
- PV/T-AHS提供了一个可行的和可持续的选择,经济回报期很短.
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