Armghan Ammar, Logeshwaran Jaganathan, Raja S, Aliqab Khaled, Alsharari Meshari, Patel Shobhit K
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, 641202, India.
Heliyon. 2024 Feb 15;10(4):e26371. doi: 10.1016/j.heliyon.2024.e26371. eCollection 2024 Feb 29.
Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model. In this model, data is collected from multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and solar radiation. This data is then used to train a machine learning algorithm to make predictions on how much thermal energy can be harvested from a particular panel or system. In a computational range, the proposed solar deep learning model (SDLM) reached 83.22 % of testing and 91.72 % of training results of false positive absorption rate, 69.88 % of testing and 81.48 % of training results of false absorption discovery rate, 81.40 % of testing and 72.08 % of training results of false absorption omission rate, 75.04 % of testing and 73.19 % of training results of absorbance prevalence threshold, and 90.81 % of testing and 78.09 % of training results of critical success index. The model also incorporates components such as insulation and orientation to further improve its accuracy in predicting the amount of thermal energy that can be harvested. Solar absorbers are used in industrial environments to absorb the sun's radiation and turn it into thermal energy. This thermal energy can then be used to power things such as heating and cooling systems, air compressors, and even some types of manufacturing operations. By using a solar deep learning model, businesses can accurately predict how much thermal energy can be harvested from a particular solar absorber before making an investment in a system.
近年来，由于热能收集具有从太阳能产生可再生能源的潜力，其受到的关注日益增加。该过程的关键组件之一是太阳能吸收器，它负责将太阳辐射转化为热能。本文针对现代工业环境，提出了一种基于太阳能深度学习模型的高效太阳能吸收器智能性能优化方法。在该模型中，随着时间的推移，从多个传感器收集数据，这些传感器测量各种环境因素，如温度、湿度、风速、大气压力和太阳辐射。然后，利用这些数据训练机器学习算法，以预测从特定面板或系统中可以收集到多少热能。在计算范围内，所提出的太阳能深度学习模型（SDLM）的误正吸收率测试结果达到83.22%，训练结果达到91.72%；误吸收发现率测试结果达到69.88%，训练结果达到81.48%；误吸收遗漏率测试结果达到81.40%，训练结果达到72.08%；吸光度患病率阈值测试结果达到75.04%，训练结果达到73.19%；关键成功指数测试结果达到90.81%，训练结果达到78.09%。该模型还纳入了隔热和方向等组件，以进一步提高其预测可收集热能数量的准确性。太阳能吸收器用于工业环境中吸收太阳辐射并将其转化为热能。然后，这种热能可用于为加热和冷却系统、空气压缩机甚至某些类型的制造操作等设备提供动力。通过使用太阳能深度学习模型，企业在投资一个系统之前，可以准确预测从特定太阳能吸收器中可以收集到多少热能。
