光伏组件温度分布及其功率预测模型研究

冯钰贻; 曾弋凡; 丁慕琛; 顾欣宇; 王子轩; 李英峰; 李美成

doi:10.19912/j.0254-0096.tynxb.2024-0172

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 383-390. DOI: 10.19912/j.0254-0096.tynxb.2024-0172

光伏组件温度分布及其功率预测模型研究

冯钰贻, 曾弋凡, 丁慕琛, 顾欣宇, 王子轩, 李英峰, 李美成

作者信息 +

RESEARCH ON TEMPERATURE DISTRIBUTION AND POWER PREDICTION MODEL OF PHOTOVOLTAIC MODULES

Feng Yuyi, Zeng Yifan, Ding Muchen, Gu Xinyu, Wang Zixuan, Li Yingfeng, Li Meicheng

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文章历史 +

摘要

为解决不断发展的大规模光伏发电接入电网后带来的调度问题,该文基于流体力学与热力学理论,提出一种用于预测光伏组件温度分布及其功率的模型。与传统的基于大数据与神经网络预测组件表面平均温度的方法相比,该模型一方面不依赖于历史测量数据,缺少相关数据也不妨碍其高精度的预测;另一方面可预测光伏组件表面的温度分布,并据此预测功率,具有更高的预测准确度。

Abstract

In order to solve the scheduling problem caused by the continuous development of large-scale photovoltaic power generation connected to the grid, this paper presents a model for predicting the temperature distribution and power output of photovoltaic modules based on the theory of fluid mechanics and thermodynamics. Compared with traditional methods based on big data and neural network, the model does not rely on historical measurement data, and the lack of relevant data does not hinder its high-precision predictions. Additionally, the temperature distribution on the surface of the photovoltaic module can be predicted, and the power output can be predicted accordingly, with higher prediction accuracy.

导出引用

冯钰贻, 曾弋凡, 丁慕琛, 顾欣宇, 王子轩, 李英峰, 李美成. 光伏组件温度分布及其功率预测模型研究[J]. 太阳能学报. 2025, 46(5): 383-390 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0172

Feng Yuyi, Zeng Yifan, Ding Muchen, Gu Xinyu, Wang Zixuan, Li Yingfeng, Li Meicheng. RESEARCH ON TEMPERATURE DISTRIBUTION AND POWER PREDICTION MODEL OF PHOTOVOLTAIC MODULES[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 383-390 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0172

中图分类号： TM615

参考文献

[1] LI C F, SOLANGI Y A, ALI S.Evaluating the factors of green finance to achieve carbon peak and carbon neutrality targets in China: a Delphi and fuzzy AHP approach[J]. Sustainability, 2023, 15(3): 2721.
[2] SHI H, HE X B.The legal guarantee for achieving carbon peak and neutrality goals in China[J]. International journal of environmental research and public health, 2023, 20(3): 2555.
[3] YE G Q, XU X L, CHEN Y J, et al.Towards green economics and society: exploring the efficiency of new energy generation[J]. Mathematical problems in engineering, 2021, 2021: 9950687.
[4] LYU C X.Analysis of China’s new energy industry development from the perspective of low-carbon economy[J]. SHS web of conferences, 2022, 144: 02020.
[5] QU C Y, GUAN J, WANG T.Development direction and focus of new energy industry in Jilin Province[J]. IOP conference series: earth and environmental science, 2021, 836(1): 012025.
[6] LUO Z M, HE J Q, HU S Y.Driving force model to evaluate China’s photovoltaic industry: historical and future trends[J]. Journal of cleaner production, 2021, 311: 127637.
[7] LI G Q, XUAN Q D, PEI G, et al.Effect of non-uniform illumination and temperature distribution on concentrating solar cell - a review[J]. Energy, 2018, 144: 1119-1136.
[8] 贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.
JIA L Y, YUN S N, ZHAO Z N, et al.Recent progress of short-term forecasting of photovoltaic generation based on artificial neural networks[J]. Acta energiae solaris sinica, 2022, 43(12): 88-97.
[9] GOVERDE H, GOOSSENS D, GOVAERTS J, et al.Spatial and temporal analysis of wind effects on PV module temperature and performance[J]. Sustainable energy technologies and assessments, 2015, 11: 36-41.
[10] GÖKMEN N, HU W H, HOU P, et al. Investigation of wind speed cooling effect on PV panels in windy locations[J]. Renewable energy, 2016, 90: 283-290.
[11] KAPLANI E, KAPLANIS S.Thermal modelling and experimental assessment of the dependence of PV module temperature on wind velocity and direction, module orientation and inclination[J]. Solar energy, 2014, 107: 443-460.
[12] ARMSTRONG S, HURLEY W G.A thermal model for photovoltaic panels under varying atmospheric conditions[J]. Applied thermal engineering, 2010, 30(11/12): 1488-1495.
[13] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[14] 薛阳, 燕宇铖, 贾巍, 等. 基于改进灰狼算法优化长短期记忆网络的光伏功率预测[J]. 太阳能学报, 2023, 44(7): 207-213.
XUE Y, YAN Y C, JIA W, et al.Photovoltaic power prediction model based on IGWO-LSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 207-213.
[15] ASSOA Y B, GUIOT T, Gaillard L, et al.A validated model to predict the thermal and electrical performance of residential rooftop BIPV systems[C]//27th European photovoltaic solar energy conference(EU PVSEC). Frankfurt, Germany, 2012.
[16] GUEYMARD C A.Direct and indirect uncertainties in the prediction of tilted irradiance for solar engineering applications[J]. Solar energy, 2009, 83(3): 432-444.
[17] MA T, YANG H X, LU L.Development of a model to simulate the performance characteristics of crystalline silicon photovoltaic modules/strings/arrays[J]. Solar energy, 2014, 100: 31-41.
[18] CHANDER S, PUROHIT A, SHARMA A, et al.A study on photovoltaic parameters of mono-crystalline silicon solar cell with cell temperature[J]. Energy reports, 2015, 1: 104-109.
[19] LI W, SHI Y, CHEN K F, et al.A comprehensive photonic approach for solar cell cooling[J]. ACS photonics, 2017, 4(4): 774-782.
[20] MATTEI M, NOTTON G, CRISTOFARI C, et al.Calculation of the polycrystalline PV module temperature using a simple method of energy balance[J]. Renewable energy, 2006, 31(4): 553-567.
[21] CHURCHILL S W.A comprehensive correlating equation for laminar, assisting, forced and free convection[J]. AIChE journal, 1977, 23(1): 10-16.
[22] SARTORI E.Convection coefficient equations for forced air flow over flat surfaces[J]. Solar energy, 2006, 80(9): 1063-1071.
[23] MUZATHIK A M, LANKA S.Photovoltaic modules operating temperature estimation using a simple correlation[J]. International Journal of Energy Engineering, 2014, 4(4): 151-158.