Construction of Solar Panel Laying System based on Genetic Algorithm

  • Chun-yu Xu
  • Jun-hua Lin
  • Qin-ming Lin
  • Yuan-biao Zhang
Keywords: Genetic Algorithm, Multi-Objective Optimization, Laying System


Solar power generation is an important energy resource in most countries. It plays an important role in meeting energy demand, improving energy structure and reducing environmental pollution. The main carrier of solar power generation is solar panels, but the utilization efficiency of most existing solar cells is low, which causes serious waste of solar energy. In response to this phenomenon, we propose a Solar Panel Laying System(SPLS) based on genetic algorithm(GA) to construct solar panels, which solves four problems: the determination of the number of battery components, the layout of the panels, the selection of the inverter and the connection of the inverter. In the SPLS ,we introduce an improved genetic algorithm and multi-objective optimization solution. Under the double premise that the total amount of solar photovoltaic power generation is as large as possible and the cost per unit of power generation is as small as possible, the quantitative solution of the laying system is realized.


[1] Shah, A.V., Sculati-Mellaud, F., Berenyi, Z.J., Ghahfarokhi, O.M., & Kumar,R. (2011). Diagonostics of thin-film silicon solar cells and solar panels/modules with variable intensity measurements (VIM). Solar Energy Materials & Solar Cells, 92, 398- 403.
[2] Wei Zhao & Wang Yijing. (2015). Optimal laying of solar photovoltaic panels based on genetic algorithms. Electronic Design Engineering, 23(02), 54-56.
[3] Xu Qing Zhen & Xiao Cheng Lin. (2006). Research and application of genetic algorithm. Modern Computer, (05), 19-22.
[4] Wang Zhimei & Chen Chuanren. (2006). Development of genetic algorithm theory and its application. Inner Mongolia Petrochemical Industry, (09), 44-45.
[5] Shui Yong. (2014). Research and application of genetic algorithm. Software, 35(03), 107-110.
[6] Dunwei Gong, Guangsong Guo, Li Lu, & Hongmei Ma. (2008). Adaptive interactive genetic algorithms with individual interval fitness. Progress in Natural Science, 18(3), 359-365.
[7] Zhou Yong & Hu Zhonggong. (2018). Application of improved fast genetic algorithm in function optimization. Modern electronic technology, 41(17), 153-157.
[8] Liu Haoran, Zhao Cuixiang, Li Xuan, Wang Yanxia, & Guo Changjiang. (2016). A study of neural network optimization algorithm based on improved genetic algorithm. Journal of Instruments and Instruments, 37(07), 1573-1580.
[9] Rhodes, J.D., Upshaw, C.R., Cole, W.J., Holcomb, C.L., & Webber, M.E. (2014). A multi objective assessment of the effect of solar PV array orientation and tilt on energy production and system economics. Solar Energy, 108, 28-40.
[10] Wen Suping. (2017). Design of practical solar cabin in Datong. Journal of Jinzhong University, 34(03), 78-84.
[11] Luo Zhangtao. (2017). Mathematical model of solar energy hut design. Shandong Industrial Technology, 12, 72-73.
[12] Wu Mengtao & Zheng Xianju. (2017). Study on the laying scheme of photovoltaic cells for solar cabins in architectural design. Modern Electronic Technology, 40(05), 178-182.
[13] Zhou Keyuan & Zhou Houfu. (2013). Mathematical model of solar energy cabin design. Journal of Taiyuan Normal University (Natural Science Edition), 12(03), 104-108.
[14] Gao Huayue, Hu Lu, Feng Shanshan, & He Yingyu. (2013). Design of Solar Cabin. Journal of Hangzhou Normal University (Natural Science Edition), 12(05), 422-428.
[15] Gao Li, Hu Dandan, Zou Qian, & Zhang Fei. (2013). Research on the design and installation of solar energy cabins. Journal of Yunnan Normal University (Natural Science Edition), 33(05), 29-33.
How to Cite
Chun-yu Xu, Jun-hua Lin, Qin-ming Lin, & Yuan-biao Zhang. (2018). Construction of Solar Panel Laying System based on Genetic Algorithm. International Journal of Engineering and Management Research, 8(6), 69-80. Retrieved from