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

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兼顧供電量組分特性的最優(yōu)GM(1,N )季度電量預測方法

來源:電工電氣發(fā)布時間:2018-01-22 12:22 瀏覽次數(shù):791
兼顧供電量組分特性的最優(yōu)GM(1,N )季度電量預測方法
 
李京平1,陳丹伶2,曾繁華1,王鑫2,方嵩1
(1 廣東電網(wǎng)有限責任公司中山供電局,廣東 中山 528400;2 廣州市奔流電力科技有限公司,廣東 廣州 510640)
 
    摘 要:提出考慮供電量組分多層級劃分及外部因素影響,利用關(guān)聯(lián)度尋優(yōu)方法構(gòu)造最優(yōu)GM(1,N )電量預測模型。根據(jù)供電地區(qū)的行業(yè)用電分類,對總供電量的組分進行分層級劃分和重要性分析;計算各重要組分及外部影響因素與供電量的關(guān)聯(lián)度,并依據(jù)關(guān)聯(lián)度大小對各影響因素進行排序,再通過建立不同N下的GM(1,N ) 模型,根據(jù)預測精度確定最優(yōu)GM(1,N ) 模型。采用該模型對廣東電網(wǎng)中山供電局的供電量數(shù)據(jù)進行預測分析,證明了該模型的預測結(jié)果具有較高的準確性。
    關(guān)鍵詞:季度電量預測;GM(1,N ) 模型;行業(yè)用電分類;外部影響因素
    中圖分類號:TM715     文獻標識碼:A     文章編號:1007-3175(2018)01-0027-05
 
Optimal GM (1, N) Quarter Electric Quantity Forecasting Method Considering Characteristics of Power Supply Components
 
LI Jing-ping1, CHEN Dan-ling2, ZENG Fan-hua1, WANG Xin2, FANG Song1
(1 Zhongshan Power Supply Bureau, Guangdong Power Grid Co., Ltd, Zhongshan 528400, China;
2 Guangzhou Power Electrical Engineering Technology Co., Ltd, Guangzhou 510640, China)
 
    Abstract: This paper proposed to use the correlation optimization method to construct the optimal GM (1, N) electric quantity prediction model considering the power supply components multilevel division and external influencing factors. According to the industry power utilization classification of power supply area, this paper carried out the power supply components multilevel division, analyzed the importance of the power supply components and calculated the correlation between each important component, together with external influencing factors and the power supply components. Each influencing factor was sorted based on the correlation and the GM (1, N) model of different N was established to determine the optimal one according to the prediction accuracy. The actual power supply data of Zhongshan power supply bureau of Guangdong power grid verifies the high accuracy of this model’s forecasting algorithm.
    Key words: quarter power supply forecasting; GM (1, N) model; industry power utilization classification; external influencing factor
 
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