Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series

IF 9 1区 工程技术 Q1 ENERGY & FUELS
Yinlong Zhu , Guoliang Li , Yonglei Jiang , Ming Li , Yunfeng Wang , Ying Zhang , Yali Liu , Muchi Yao
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引用次数: 0

Abstract

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.

Abstract Image

基于超短期时间序列的高原低纬度地区光伏温室辐照度预测
准确、可靠的低纬度高原光伏大棚太阳辐照度超短期预测是精确控制大棚设备用电量、保证作物高产的关键。然而,低纬度高原地区辐照度存在数据质量差、短期预测精度有限、捕捉非线性特征能力不足等问题。因此,为了实现光伏资源的高效利用,本研究提出了一种新的混合集成模型TTAO-CNN-BiGRU-Attention框架来预测该地区超短期光伏温室辐照度。采用统计方法分析了低纬度高原地区辐照度的月特征和季节特征。利用5个不同数据量和4个不同季节的9个不同模型验证了所提出模型的性能。综合分析结果表明,总辐射瞬时值(TRI)呈季节变化趋势,春季总体较低,夏秋季较高,秋冬季相对稳定。月趋势先上升后下降,在9月达到全年最高值。本文提出的方案充分利用了CNN、BiGRU、Attention和TTAO的优势,大大提高了模型的综合预测能力。在预测不同数据量时,1年预测效果最好,RMSE、MAE、MAPE和R2分别达到70.61 W/m2、31 W/m2、9.3%和95.84%。不同季节,秋季预测效果最好,RMSE、MAE、MAPE和R2分别达到66.27 W/m2、31.02 W/m2、8.37%和95.87%。该模型的TRI预测曲线比其他比较模型更接近实际值。研究发现,TTAO-CNN-BiGRU-Attention模型在预测低纬度高原光伏温室超短期TRI方面比许多传统模型更加准确和稳定,可为未来光伏温室辐照度预测模型的综合性能和能源供应的精准调控提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Renewable Energy
Renewable Energy 工程技术-能源与燃料
CiteScore
18.40
自引率
9.20%
发文量
1955
审稿时长
6.6 months
期刊介绍: Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices. As an international, multidisciplinary journal in renewable energy engineering and research, we strive to be a premier peer-reviewed platform and a trusted source of original research and reviews in the field of renewable energy. Join us in our endeavor to drive innovation and progress in sustainable energy solutions.
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