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Special Issue:
SPECIAL TOPIC — Smart design of materials and design of smart materials
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| SPECIAL TOPIC—Smart design of materials and design of smart materials |
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Forecasting solar still performance from conventional weather data variation by machine learning method |
| Wenjie Gao(高文杰)1, Leshan Shen(沈乐山)2,3, Senshan Sun(孙森山)1, Guilong Peng(彭桂龙)1, Zhen Shen(申震)2,3, Yunpeng Wang(王云鹏)1, AbdAllah Wagih Kandeal6, Zhouyang Luo(骆周扬)2,3, A. E. Kabeel7,8, Jianqun Zhang(张坚群)4,†, Hua Bao(鲍华)5,‡, and Nuo Yang(杨诺)1,§ |
1 State Key Laboratory of Coal Combustion, and School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;
2 Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 31121, China;
3 Zhejiang Energy Group R&D Institute, Co., Ltd., Hangzhou 311121, China;
4 Zhejiang Zheneng Yueqing Electric Power Generation Co., Ltd., Yueqing 325609, China;
5 University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China;
6 Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt;
7 Mechanical Power Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt;
8 Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt |
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Abstract Solar stills are considered an effective method to solve the scarcity of drinkable water. However, it is still missing a way to forecast its production. Herein, it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data. The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm. The required data to train the model are obtained from daily measurements lasting 9 months. To validate the accuracy model, the determination coefficients of two types of solar stills are calculated as 0.935 and 0.929, respectively, which are much higher than the value of both multiple linear regression (0.767) and the traditional models (0.829 and 0.847). Moreover, by applying the model, we predicted the freshwater production of four cities in China. The predicted production is approved to be reliable by a high value of correlation (0.868) between the predicted production and the solar insolation. With the help of the forecasting model, it would greatly promote the global application of solar stills.
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Received: 16 July 2022
Revised: 24 September 2022
Accepted manuscript online: 10 October 2022
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PACS:
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88.40.-j
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(Solar energy)
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92.60.Vb
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(Radiative processes, solar radiation)
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| Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2018YFE0127800), the Science, Technology & Innovation Funding Authority (STIFA), Egypt grant (Grant No. 40517), China Postdoctoral Science Foundation (Grant No. 2020M682411), and the Fundamental Research Funds for the Central Universities (Grant No. 2019kfyRCPY045). |
Corresponding Authors:
Jianqun Zhang, Hua Bao, Nuo Yang
E-mail: zhangjianq888@163.com;hua.bao@sjtu.edu.cn;nuo@hust.edu.cn
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Cite this article:
Wenjie Gao(高文杰), Leshan Shen(沈乐山), Senshan Sun(孙森山), Guilong Peng(彭桂龙), Zhen Shen(申震), Yunpeng Wang(王云鹏), AbdAllah Wagih Kandeal, Zhouyang Luo(骆周扬), A. E. Kabeel, Jianqun Zhang(张坚群), Hua Bao(鲍华), and Nuo Yang(杨诺) Forecasting solar still performance from conventional weather data variation by machine learning method 2023 Chin. Phys. B 32 048801
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