Physical model and long short-term memory-based combined prediction of photovoltaic power generation

IF 1.3 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Power Electronics Pub Date : 2024-05-25 DOI:10.1007/s43236-024-00782-9
Yaoyu Wu, Jing Liu, Suhuan Li, Mingyue Jin
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Abstract

Solar energy is clean and pollution free. However, the evident intermittency and volatility of illumination make power systems uncertain. Therefore, establishing a photovoltaic prediction model to enhance prediction precision is conducive to lessening the uncertainty of photovoltaic (PV) power generation and to ensuring the safe and stable operation of power grid scheduling. The radiation from the sun to the Earth has a certain regularity, which can be estimated under ideal weather conditions. However, the radiation is affected by climate, cloud cover, and other reasons. Therefore, this paper puts forward a PV prediction model combining a physical model and a neural network that can modify solar radiation in complex weather through the neural network to enhance the accuracy of PV power prediction. First, a solar radiation model (SRM) is established by using the solar radiation mechanism to estimate the sum radiation value on the horizontal plane. Then the slope radiation value received by the PV panel is calculated by the slope irradiance conversion method. Second, the main factors that greatly influence PV power are screened out by the Pearson method. The calculated slope radiation and the main influencing factors are taken as inputs. The long short-term memory network (LSTM) is selected to set up the SRM-LSTM PV power prediction method. The significance of the suggested method is verified by the true data from Alice Springs, Australia. The results show that when compared with the backpropagation (BP) prediction method, the MAE and RMSE were reduced by 22.18% and 33.89% under complex weather conditions, respectively. When compared with the LSTM prediction method, the MAE and RMSE were reduced by 15.99% and 21.73%, respectively. These results demonstrate high accuracy.

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基于物理模型和长短期记忆的光伏发电综合预测
太阳能清洁无污染。然而,光照明显的间歇性和波动性使得电力系统具有不确定性。因此,建立光伏预测模型,提高预测精度,有利于减少光伏发电的不确定性,确保电网调度安全稳定运行。太阳对地球的辐射具有一定的规律性,可以在理想的天气条件下进行估算。然而,辐射会受到气候、云层等原因的影响。因此,本文提出了一种结合物理模型和神经网络的光伏预测模型,通过神经网络修正复杂天气下的太阳辐射,提高光伏发电预测的准确性。首先,利用太阳辐射机理建立太阳辐射模型(SRM),估算水平面的总辐射值。然后通过斜率辐照度转换方法计算光伏板接收到的斜率辐射值。其次,利用 Pearson 方法筛选出对光伏发电影响较大的主要因素。将计算出的斜坡辐射值和主要影响因素作为输入。选择长短期记忆网络(LSTM)来建立 SRM-LSTM 光伏功率预测方法。澳大利亚爱丽斯泉的真实数据验证了所建议方法的重要性。结果表明,在复杂天气条件下,与反向传播(BP)预测方法相比,MAE 和 RMSE 分别降低了 22.18% 和 33.89%。与 LSTM 预测方法相比,MAE 和 RMSE 分别降低了 15.99% 和 21.73%。这些结果表明预测结果具有很高的准确性。
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
Journal of Power Electronics
Journal of Power Electronics 工程技术-工程:电子与电气
CiteScore
2.30
自引率
21.40%
发文量
195
审稿时长
3.6 months
期刊介绍: The scope of Journal of Power Electronics includes all issues in the field of Power Electronics. Included are techniques for power converters, adjustable speed drives, renewable energy, power quality and utility applications, analysis, modeling and control, power devices and components, power electronics education, and other application.
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