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首页> 外文期刊>Energy Conversion & Management >A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework
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A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework

机译：基于LSTM-RNN模型的前方PV功率预测方法及部分日常模式预测框架下的时间相关修改

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摘要

Photovoltaic (PV) power generation is an effective means to realize solar energy utilization. Due to the natural characteristics of random fluctuations in solar energy, the applications of PV power such as grid-connected PV power plant, distributed PVs, and building integrated PVs will introduce new characteristics to the generation and load side of the power grid. Therefore, accurate day-ahead PV power forecasting is of great significance for enabling grid manager to achieve PV power output data in advance and mitigate the influence of random fluctuations. To tackle the deficiencies of conventional artificial intelligence (AI) modeling methods such as overfitting problem and insufficient generalization ability to complex nonlinear modeling, a day-ahead PV power forecasting model assembled by fusing deep learning modeling and time correlation principles under a partial daily pattern prediction (PDPP) framework is proposed. First, an independent day-ahead PV power forecasting model based on long-short-term memory recurrent neural network (LSTM-RNN) is established. Second, a modification method is proposed to update the forecasting results of LSTM-RNN model based on time correlation principles regarding different patterns of PV power in the forecasting day. Third, a partial daily pattern prediction (PDPP) framework is proposed to provide accurate daily pattern prediction information of particular days, which is used to guide the modification parameters. Simulation results show that the proposed forecasting method with time correlation modification (TCM) is more accurate than the individual LSTM-RNN model, and the performance of the forecasting model can be further improved for those days with accurate daily pattern predictions under the proposed PDPP framework.
机译：光伏（PV）发电是实现太阳能利用的有效手段。由于太阳能随机波动的自然特性，PV电源的应用，如网格连接的光伏电站，分布式PV和建筑集成PVS将对电网的产生和负载侧引入新的特性。因此，准确的一天前的PV功率预测对于使电网管理器能够提前实现PV电源输出数据并减轻随机波动的影响。解决传统人工智能（AI）建模方法的缺陷，例如复杂非线性建模的过度问题和概括能力，通过融合深度学习建模和时间相关原理，在部分日常模式预测下组装的一天前方PV功率预测模型（PDPP）框架是提出的。首先，建立了基于长短期内存经常性神经网络（LSTM-RNN）的独立日前的PV功率预测模型。其次，提出了一种修改方法，以基于预测日中的PV电力模式的时间相关原理更新LSTM-RNN模型的预测结果。第三，提出了部分日常模式预测（PDPP）框架以提供特定日期的准确每日模式预测信息，用于引导修改参数。仿真结果表明，随着时间相关性修改（TCM）的提出的预测方法比单独的LSTM-RNN模型更准确，并且在提出的PDPP框架下准确的日常模式预测，可以进一步提高预测模型的性能。

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  • 来源
    《Energy Conversion & Management》 |2020年第5期|112766.1-112766.14|共14页
  • 作者

    Wang Fei; Xuan Zhiming; Zhen Zhao; Li Kangping; Wang Tieqiang; Shi Min;

  • 作者单位

    North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China|North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 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;

    North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China|Tsinghua Univ State Key Lab Power Syst Dept Elect Engn Beijing 100084 Peoples R China;

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

    State Grid Hebei Elect Power Co Ltd Dispatch & Control Ctr Shijiazhuang 050021 Hebei Peoples R China;

    State Grid Hebei Elect Power Co Ltd Dispatch & Control Ctr Shijiazhuang 050021 Hebei Peoples R China;

  • 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; LSTM-RNN; Time correlation modification; Daily pattern prediction;

    机译：光伏力量;LSTM-RNN;时间相关性修改;每日模式预测;

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