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

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基于長(zhǎng)短時(shí)記憶網(wǎng)絡(luò)的電力系統(tǒng)負(fù)荷預(yù)測(cè)方法研究

來源:電工電氣發(fā)布時(shí)間:2019-11-19 13:19 瀏覽次數(shù):699
基于長(zhǎng)短時(shí)記憶網(wǎng)絡(luò)的電力系統(tǒng)負(fù)荷預(yù)測(cè)方法研究
 
王鑫琪,李闖,焦晗,李焱飛
(南京工程學(xué)院 電力工程學(xué)院,江蘇 南京 211167)
 
    摘 要:準(zhǔn)確的負(fù)荷預(yù)測(cè)對(duì)保持電網(wǎng)的穩(wěn)定性和提高當(dāng)?shù)亟?jīng)濟(jì)效益、節(jié)約成本有重大幫助??紤]到負(fù)荷數(shù)據(jù)帶有時(shí)序性,以及智能電網(wǎng)的發(fā)展所帶來的數(shù)據(jù)量的增大,建立了長(zhǎng)短時(shí)記憶網(wǎng)絡(luò)(LSTM)模型來對(duì)未來用電量進(jìn)行短期負(fù)荷預(yù)測(cè)。針對(duì)Adam訓(xùn)練算法可能存在的收斂問題,對(duì)其進(jìn)行了改進(jìn),并通過MATLAB軟件對(duì)LSTM網(wǎng)絡(luò)進(jìn)行建模,通過與BP神經(jīng)網(wǎng)絡(luò)進(jìn)行對(duì)比,結(jié)果表明,LSTM模型具有更高的精確度以及實(shí)用性。
    關(guān)鍵詞:短期負(fù)荷預(yù)測(cè);BP神經(jīng)網(wǎng)絡(luò);長(zhǎng)短時(shí)記憶網(wǎng)絡(luò);Adam算法
    中圖分類號(hào):TM715     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2019)11-0017-04
 
Research on Power System Load Forecasting Method Based on Long-Term and Short-Term Memory Network
 
WANG Xin-qi, LI Chuang, JIAO Han, LI Yan-fei
(School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 2111 67, China)
 
    Abstract: Load forecasting is an important part of power system dispatching. Accurate load forecasting is of great help to maintain grid stability and improve local economic benefits and cost. Considering the sequential nature of load data and the increase in data volume brought about by the development of smart grids, a long-term and short-term memory network (LSTM) model was established to make shortterm predictions of future electricity consumption. On this basis, the convergence problem of Adam training algorithm may be improved. It is simulated by MATLAB software and compared with BP neural network. The results show that the LSTM model has higher accuracy and practicability.
    Key words: short-term load forecast; BP neural network; long-term and short-term memory network; Adam algorithm
 
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