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首页> 外文期刊>Energy Conversion & Management >Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting
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Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting

机译：基于生成对抗网络和卷积神经网络的天气分类模型，用于日前短期光伏发电预测

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摘要

Accurate solar photovoltaic power forecasting can help mitigate the potential risk caused by the uncertainty of photovoltaic out power in systems with high penetration levels of solar photovoltaic generation. Weather classification based photovoltaic power forecasting modeling is an effective method to enhance its forecasting precision because photovoltaic output power strongly depends on the specific weather statuses in a given time period. However, the most intractable problems in weather classification models are the insufficiency of training dataset (especially for the extreme weather types) and the selection of applied classifiers. Given the above considerations, a generative adversarial networks and convolutional neural networks-based weather classification model is proposed in this paper. First, 33 meteorological weather types are reclassified into 10 weather types by putting several single weather types together to constitute a new weather type. Then a data-driven generative model named generative adversarial networks is employed to augment the training dataset for each weather types. Finally, the convolutional neural networks-based weather classification model was trained by the augmented dataset that consists of both original and generated solar irradiance data. In the case study, we evaluated the quality of generative adversarial networks-generated data, compared the performance of convolutional neural networks classification models with traditional machine learning classification models such as support vector machine, multilayer perceptron, and k-nearest neighbors algorithm, investigated the precision improvement of different classification models achieved by generative adversarial networks, and applied the weather classification models in solar irradiance forecasting. The simulation results illustrate that generative adversarial networks can generate new samples with high quality that capture the intrinsic features of the original data, but not to simply memorize the training data. Furthermore, convolutional neural networks classification models show better classification performance than traditional machine learning models. And the performance of all these classification models is indeed improved to the different extent via the generative adversarial networks-based data augment. In addition, weather classification model plays a significant role in determining the most suitable and precise day-ahead photovoltaic power forecasting model with high efficiency.
机译：准确的太阳能光伏发电预测可以帮助减轻由于太阳能光伏发电普及率高的系统中光伏输出功率的不确定性引起的潜在风险。基于天气分类的光伏功率预测模型是提高其预测精度的有效方法，因为光伏输出功率在很大程度上取决于给定时间段内的特定天气状况。但是，天气分类模型中最棘手的问题是训练数据集不足（尤其是针对极端天气类型）和应用分类器的选择。基于以上考虑，提出了一种基于生成对抗网络和卷积神经网络的天气分类模型。首先，通过将几种单一的天气类型放在一起构成一个新的天气类型，将33种气象天气类型重新分类为10种天气类型。然后，使用一个名为生成对抗网络的数据驱动生成模型来扩充每种天气类型的训练数据集。最后，基于卷积神经网络的天气分类模型由增强数据集训练，该数据集包括原始和生成的太阳辐照度数据。在案例研究中，我们评估了生成对抗网络生成数据的质量，将卷积神经网络分类模型与传统机器学习分类模型（例如支持向量机，多层感知器和k近邻算法）的性能进行了比较，调查了通过生成对抗网络实现不同分类模型的精度改进，并将天气分类模型应用于太阳辐照度预报。仿真结果表明，生成对抗网络可以生成高质量的新样本，以捕获原始数据的内在特征，而不是简单地记住训练数据。此外，卷积神经网络分类模型显示出比传统机器学习模型更好的分类性能。通过基于生成对抗网络的数据扩充，所有这些分类模型的性能确实得到了不同程度的提高。此外，天气分类模型在确定最合适，最精确的高效日前光伏发电预测模型中起着重要作用。

著录项

  • 来源
    《Energy Conversion & Management》 |2019年第2期|443-462|共20页
  • 作者

    Wang Fei; Zhang Zhanyao; Liu Chun; Yu Yili; Pang Songling; Duic Neven; Shafie-Khah Miadreza; Catalao Joao P. S.;

  • 作者单位

    North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Baoding 071003, Peoples R China|North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China|North China Elect Power Univ, Hebei Key Lab Distributed Energy Storage & Microg, Baoding 071003, Peoples R China;

    North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China;

    China Elect Power Res Inst, State Key Lab Operat & Control Renewable Energy &, Beijing 100192, Peoples R China;

    North China Elect Power Univ, Dept Elect Engn, Baoding 071003, Peoples R China;

    Hainan Power Grid Co Ltd, Elect Power Res Inst, Haikou 570311, Hainan, Peoples R China;

    Univ Zagreb, Fac Mech Engn & Navala Architecture, Ivana Lucica 5, Zagreb 10000, Croatia;

    INESC TEC, P-4200465 Porto, Portugal;

    INESC TEC, P-4200465 Porto, Portugal|Univ Porto, Fac Engn, P-4200465 Porto, Portugal|Univ Beira Interior, C MAST, P-6201001 Covilha, Portugal;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Photovoltaic power forecasting; Weather classification; Generative adversarial networks; Convolutional neural networks;

    机译：光伏发电预测;天气分类;对抗网络;卷积神经网络;

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