2024年7月10日，Elsevier 旗下top期刊《Energy Conversion and Management》在线发表了云南师范大学能源与环境科学学院李明教授最新研究成果《Solar irradiance prediction with variable time lengths and multi-parameters in full climate conditions based on photovoltaic greenhouse》。云南师范大学能源与环境科学学院李明教授为通讯作者。合作单位为西南联合研究生院和埃及开罗大学农学院

https://www.sciencedirect.com/science/article/abs/pii/S019689042400699X

Abstract

Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse.

扩展阅读：

云师大能环学院李明教授课题组在国际知名储能期刊《Energy》发表最新研究成果

云师大能环学院王云峰教授等在国际知名期刊《Energy》上发表最新研究成果

云师大李明、李忠光、高伟和吴鲜思维教授入选全球前2%顶尖科学家榜单

云师大能环学院王云峰教授等在国际知名新能源期刊《Renewable Energy》上发表最新研

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李明教授课题组在Energy Conversion and Management上发表第6篇研究论文

云南师范大学基础研究之中流——李明教授

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