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    Suzhou Electric Appliance Research Institute
    期刊號: CN32-1800/TM| ISSN1007-3175

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    基于交叉熵理論的光伏發電功率組合預測方法

    來源:電工電氣發布時間:2022-04-20 14:20 瀏覽次數:414

    基于交叉熵理論的光伏發電功率組合預測方法

    陳麗霞
    (國網福建省電力有限公司福州供電公司,福建 福州 350007)
     
        摘 要:光電功率預測對電網的安全穩定運行以及調度等方面具有重要意義。針對單一預測方法精度較低的問題,提出了一種基于交叉熵理論的光伏發電功率組合預測方法,以單一預測方法和最小化組合預測方法的“差值”為依據,動態地改變不同預測方法的權重,提高組合預測的精度。以某光伏電場為算例進行分析,結果表明,該模型針對不同的天氣,具有較強的預測適應性,可以提高預測精度,減少預測誤差的出現。
        關鍵詞:光伏發電;功率預測;交叉熵;權重;組合預測
        中圖分類號:TM615     文獻標識碼:A     文章編號:1007-3175(2022)04-0017-04
     
    A Combination Forecasting Method of Photovoltaic Power Generation
    Based on Cross-Entropy Theory
     
    CHEN Li-xia
    (Fuzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd, Fuzhou 350007, China)
     
        Abstract: Photoelectric power prediction has significant meaning to the safe and stable operation and dispatching of power grids. This paper proposed a combined prediction method of photovoltaic power generation based on cross-entropy theory to solve the problem of the low accuracy of the single prediction method. This combined prediction method is based on the“gap”between the single forecast method and the minimized combined forecast method. It changed the weight of different forecasting methods dynamically and improved the accuracy of combined forecasting. This paper took a photovoltaic field as an example to analyze. The result shows that the model has strong prediction adaptability, and it could improve prediction accuracy and reduce prediction errors.
        Key words: photovoltaic power generation; power prediction; cross-entropy; weight; combined prediction
     
    參考文獻
    [1] 李旭. 基于典型日出力特性分析的光伏電站功率預測研究[D]. 北京:華北電力大學,2016.
    [2] BOYLE Godfrey.Renewable electricity and thegrid[M].London :Earthscan Publications Ltd,2007.
    [3] 龔鶯飛,魯宗相,喬穎,王強. 光伏功率預測技術[J]. 電力系統自動化,2016,40(4) :140-151.
    [4] 朱永強,田軍. 最小二乘支持向量機在光伏功率預測中的應用[J]. 電網技術,2011,35(7) :54-59.
    [5] FERNANDEZ-JIMENEZ L A, TERREROS-OLARTE S,ZORZANO-SANTAMARIA P J, MENDOZA-VILLENA M,GARCIA-GARRIDO E.Probabilistic photovoltaic power forecasting model based on deterministic forecasts[C]//E3S Web of Conferences,2020:152.
    [6] 丁明,徐寧舟. 基于馬爾可夫鏈的光伏發電系統輸出功率短期預測方法[J] . 電網技術,2011,35(1) :152-157.
    [7] 楊錫運,劉歡,張彬,陳嵩. 基于熵權法的光伏輸出功率組合預測模型[J] . 太陽能學報,2014,35(5) :744-749.
    [8] 楊錫運,任杰,肖運啟. 基于粗糙集的光伏輸出功率組合預測模型[J] . 中國電力,2016,49(12) :133-138.
    [9] 陳嵩. 組合預測技術及其在功率預測中的應用[D].北京:華北電力大學,2015.
    [10] WANG Yue, GUO Chuangxin, WU Q H.A cross-entropy-based three-stage sequential importance sampling for composite power system short-term reliability evaluation[J].IEEE Transactions on Power Systems,2013,28(4) :4254-4263.
    [11] 王越,郭創新,文云峰,李樹青,袁翔. 一種三段式序貫交叉熵重采樣方法及其在電力系統短期可靠性評估中的應用[J] . 中國電機工程學報,2013,33(28) :94-100.
    [12] 周軍妮,楊潤玲,王燕妮,江莉. 一種結合交叉熵和投影特征的圖像匹配算法[J] . 小型微型計算機系統,2013,34(2) :405-408.
    [13] 陳寧,沙倩,湯奕,朱凌志. 基于交叉熵理論的風電功率組合預測方法[J] . 中國電機工程學報,2012,32(4) :29-34.
    [14] 高陽,張碧玲,毛京麗,劉勇. 基于機器學習的自適應光伏超短期出力預測模型[J] . 電網技術,2015,39(2) :307-311.

     

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