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

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基于相似日和CNN-LSTM的短期負(fù)荷預(yù)測

來源：電工電氣發(fā)布時間：2022-08-29 14:29 瀏覽次數(shù)：358

基于相似日和CNN-LSTM的短期負(fù)荷預(yù)測

童占北1，鐘建偉1，李禎維2，吳建軍2，李家俊2
(1 湖北民族大學(xué) 智能科學(xué)與工程學(xué)院，湖北 恩施 445000；
2 國網(wǎng)湖北省電力有限公司恩施供電公司，湖北 恩施 445000)
 
    摘 要：為充分發(fā)掘歷史信息，解決氣象數(shù)據(jù)不足影響預(yù)測精度的問題，采用灰色關(guān)聯(lián)分析 (GRA) 選取天氣相似日和 CNN-LSTM 混合神經(jīng)網(wǎng)絡(luò)的方法來預(yù)測電力負(fù)荷。利用 GRA 計算每日各氣象因素與日總負(fù)荷的灰色關(guān)聯(lián)度，再計算各日與典型日的相同氣象因素之間的歐氏距離，將各氣象因素的歐氏距離分別乘以對應(yīng)因素的關(guān)聯(lián)度，并將同一天的結(jié)果累加，得到一個綜合得分。選取待預(yù)測日之前分?jǐn)?shù)最低的 5 天作為相似日，將相似日各時刻的負(fù)荷數(shù)據(jù)輸入 CNN-LSTM 網(wǎng)絡(luò)中，預(yù)測出待預(yù)測日的負(fù)荷，通過與其他模型對比，驗證了該方法的有效性。
    關(guān)鍵詞： 灰色關(guān)聯(lián)分析；相似日；CNN-LSTM 混合神經(jīng)網(wǎng)絡(luò)；短期負(fù)荷預(yù)測
    中圖分類號：TM715     文獻(xiàn)標(biāo)識碼：A     文章編號：1007-3175(2022)08-0017-06
 
Short-Term Load Forecasting Based on Grey Relational
Analysis and CNN-LSTM
 
TONG Zhan-bei1, ZHONG Jian-wei1, LI Zhen-wei2, WU Jian-jun2, LI Jia-jun2
（1 College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China;
2 Enshi Power Supply Company, State Grid Hubei Electric Power Co., Ltd, Enshi 445000, China）
 
    Abstract: This paper used grey relational analysis(GRA) to select days with similar weather conditions and explore more historical information.In addition, it employed the CNN-LSTM hybrid neural network method to predict power load and solve the problem of insufficient meteorological data affecting prediction accuracy. This research used GRA to calculate the grey relational grade between daily meteorological factors and overall load. In addition, it computed the Euclidean distance of the same meteorological factors between each day and the typical day. The Euclidean distance of each meteorological factor was multiplied by the relevancy of corresponding factors. The accumulation of the calculation results in the same day could obtain an overall score. This study took five days with the lowest score before predicted days as the similar days. It inputted load data into the CNN-LSTM network to forecast the load of prediction days. Compared with other models, the effectiveness of this method is verified.
    Key words: grey relational analysis; similar day; CNN-LSTM hybrid neural network; short-term load forecasting
 
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