2024年11月29日，Elsevier 旗下top期刊《Renewable Energy》在线发表了云南师范大学能源与环境科学学院李明教授课题组李国良的最新研究成果《Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series》。云南师范大学能源与环境科学学院李国良高级实验师为通讯作者。

Accurate and reliable ultra-short-term prediction of solar irradiance in photovoltaic (PV) greenhouses at low-latitude plateau is essential to precisely control electricity consumption of greenhouse equipment and ensure high quality crop yields. However, the irradiance in the low-latitude plateau has problems such as poor data quality, limited short-term prediction accuracy, and insufficient ability to capture nonlinear characteristics. Therefore, in order to achieve efficient utilization of photovoltaic resources, this study proposed a new hybrid integrated model TTAO-CNN-BiGRU-Attention framework to predict ultra-short-term photovoltaic greenhouse irradiance in the region. Monthly and seasonal characteristics of irradiance in low-latitude plateau areas were analyzed by statistical methods. The performance of the proposed model was verified using 9 different models for 5 different data volumes and 4 different seasons. Comprehensive analysis results show that Total radiant instantaneous(TRI) demonstrates a seasonal trend, generally low in spring, high in summer and autumn, relatively stable in autumn and winter. The monthly trend initially increases and then decreases, reaching the highest value of the year in September. The scheme proposed in this paper makes full use of the advantages of CNN, BiGRU, Attention and TTAO, greatly improving the comprehensive prediction ability of the model. In predicting different data amounts, 1 year prediction performance was the best, with RMSE, MAE, MAPE and R2 reaching 70.61 W/m2, 31 W/m2, 9.3% and 95.84%, respectively. With regard to different seasons, autumn prediction performance was the best, with RMSE, MAE, MAPE and R2 reaching 66.27 W/m2, 31.02 W/m2, 8.37% and 95.87%, respectively. The TRI prediction curve of the proposed model was closer to the actual value than other comparison models. The study found that the TTAO-CNN-BiGRU-Attention model is more accurate and stable than many traditional models in predicting ultra-short-term TRI in low-latitude plateau photovoltaic greenhouses, which can provide a reference for the comprehensive performance of PV greenhouse irradiance prediction models and precise regulation of energy supply in the future.

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