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节能太阳能吸收器的性能优化用于现代工业环境中的热能采集,使用太阳能深度学习模型

Ammar Armghan¹, Jaganathan Logeshwaran², S Raja³

¹Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.

|February 26, 2024

概括

这项研究引入了一个智能深度学习模型,以优化太阳能吸收器,以便在工业环境中高效地收集热能. 该模型准确地预测了能源输出,增强了来自太阳辐射的可再生能源发电.

科学领域:

可再生能源工程可再生能源工程
人工智能在能源中的作用
材料科学用于太阳能应用.

背景情况:

热能采集对于可再生能源发电至关重要,太阳能吸收器发挥着关键作用.
现代工业环境需要高效的能源解决方案,推动太阳能热技术的创新.
准确预测采集的热能对于优化系统设计和投资至关重要.

研究的目的:

建议对节能太阳能吸收器进行智能性能优化,以采集热能.
开发和评估太阳能深度学习模型 (SDLM),用于预测热能产量.
通过结合绝缘和方向等因素来提高能源采集预测的准确性.

主要方法:

收集有关环境因素 (温度,湿度,太阳辐射等) 的传感器数据. 随着时间的推移.
训练了一种机器学习算法 (SDLM) 来预测热能采集潜力.
将绝缘和定向参数纳入模型,以提高预测准确度.

主要成果:

拟议的SDLM在预测吸收率和性能指标方面取得了很高的准确性.
关键绩效指标包括假阳性吸收率,假发现率和关键成功指数.
该模型在训练和测试计算范围中都表现出强的性能.

结论:

开发的太阳能深度学习模型提供了优化太阳能吸收器用于工业热能采集的智能方法.
精确的能源收益率预测使可再生能源应用中的更好的投资决策和系统效率成为可能.
这种人工智能驱动的方法提高了太阳能热能为工业运营提供动力的可行性.

关键词:

深度学习是一种深度学习.节能采集节能采集节能采集可再生能源是可再生的能源.太阳能吸收器是一种吸收器.太阳辐射太阳辐射热制热器是一种热制热器.

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