本文選題：智能電網(wǎng)　切入點(diǎn)：超短期負(fù)荷預(yù)測(cè)　出處：《華北電力大學(xué)(北京)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著智能電網(wǎng)的發(fā)展,越來越多的新能源接入其中,如太陽能、風(fēng)能等,形成分布式電網(wǎng)模式。然而,這些新能源的發(fā)電量易受光照、風(fēng)速等自然條件的影響,尤其隨著新能源接入量的增加,其本身的波動(dòng)性對(duì)智能電網(wǎng)的穩(wěn)定性帶來很大影響。在電網(wǎng)穩(wěn)定性狀態(tài)評(píng)估和電網(wǎng)實(shí)時(shí)動(dòng)態(tài)無功電壓優(yōu)化控制等方面,超短期負(fù)荷預(yù)測(cè)具有重要的參考意義。超短期負(fù)荷預(yù)測(cè)具有預(yù)測(cè)時(shí)間短、實(shí)時(shí)性要求高等特點(diǎn),目前正處于研究階段。智能電網(wǎng)中大量的時(shí)序數(shù)據(jù)對(duì)于超短期負(fù)荷預(yù)測(cè)具有重要的參考價(jià)值,如何有效地利用智能電網(wǎng)中的時(shí)序數(shù)據(jù),充分挖掘其中潛在信息的關(guān)聯(lián)性進(jìn)行超短期負(fù)荷預(yù)測(cè),成為智能電網(wǎng)系統(tǒng)的一個(gè)熱門研究方向。本文針對(duì)目前超短期負(fù)荷預(yù)測(cè)算法存在的穩(wěn)定性差和忽略用戶行為相似性等問題,提出了基于虛擬用戶模型和預(yù)測(cè)區(qū)間的超短期預(yù)測(cè)模型;然后結(jié)合電力用戶數(shù)據(jù)的數(shù)據(jù)流特點(diǎn),提出了基于數(shù)據(jù)流聚類的超短期負(fù)荷預(yù)測(cè)方法,提高了預(yù)測(cè)速度。本文主要的研究有如下幾個(gè)方面。首先,對(duì)現(xiàn)有的超短期負(fù)荷預(yù)測(cè)算法進(jìn)行了綜述,分析了現(xiàn)有預(yù)測(cè)算法的缺點(diǎn)。其次,針對(duì)現(xiàn)有的預(yù)測(cè)算法中未考慮到用戶用電行為的相似性的問題,通過分析用戶負(fù)荷曲線的特點(diǎn),提出虛擬用戶模型;再次,考慮到用戶用電行為的隨機(jī)性特點(diǎn),引入預(yù)測(cè)區(qū)間以提高預(yù)測(cè)算法的穩(wěn)定性,結(jié)合虛擬用戶模型,提出了基于虛擬用戶模型和預(yù)測(cè)區(qū)間的超短期預(yù)測(cè)模型;然后,根據(jù)智能電網(wǎng)中用戶負(fù)荷數(shù)據(jù)的時(shí)序特性,采用數(shù)據(jù)流聚類技術(shù)對(duì)虛擬用戶模型的超短期負(fù)荷預(yù)測(cè)算法進(jìn)行改進(jìn),提高了算法的預(yù)測(cè)速度;最后,通過實(shí)驗(yàn)驗(yàn)證,本文提出的基于虛擬用戶模型的超短期負(fù)荷預(yù)測(cè)算法的準(zhǔn)確率要優(yōu)于對(duì)比試驗(yàn)中的其它算法,并且引入數(shù)據(jù)流聚類分析技術(shù)后在預(yù)測(cè)精度可接受范圍內(nèi),預(yù)測(cè)速度也得到了顯著提升。
[Abstract]:With the development of smart grid, more and more new energy sources are connected to it, such as solar energy, wind energy and so on. However, the generation of these new energy sources is easily affected by natural conditions such as light, wind speed, etc. In particular, with the increase of new energy access, its own volatility has a great impact on the stability of smart grid. In the aspects of power grid stability evaluation and real-time dynamic reactive power and voltage optimization control, etc. Ultra-short-term load forecasting has important reference significance. Ultra-short-term load forecasting has the characteristics of short forecasting time, high real-time requirement and so on. At present, a lot of time series data in smart grid have important reference value for ultra-short-term load forecasting, how to utilize the time series data in smart grid effectively. Fully mining the correlation of potential information for ultra-short-term load forecasting, It has become a hot research direction in smart grid system. This paper aims at the problems of poor stability and neglecting the similarity of user behavior in the current ultra-short-term load forecasting algorithm. An ultra-short-term forecasting model based on virtual user model and prediction interval is proposed, and then a method of ultra-short-term load forecasting based on data stream clustering is proposed, which is based on the characteristics of data flow of power user data. The main research in this paper is as follows: firstly, the existing ultra-short-term load forecasting algorithms are reviewed, and the shortcomings of the existing forecasting algorithms are analyzed. In view of the problem that the existing prediction algorithms do not consider the similarity of the user's power consumption behavior, by analyzing the characteristics of the user load curve, a virtual user model is proposed. Thirdly, considering the randomness of the user's power consumption behavior, The prediction interval is introduced to improve the stability of the prediction algorithm. Combined with the virtual user model, the ultra-short-term forecasting model based on the virtual user model and the prediction interval is proposed. Then, according to the time series characteristics of the user load data in the smart grid, The data stream clustering technique is used to improve the ultra-short-term load forecasting algorithm of the virtual user model, which improves the forecasting speed of the algorithm. The accuracy of the proposed algorithm based on virtual user model is better than that of other algorithms in the contrast experiment, and the prediction accuracy is acceptable after the introduction of data stream clustering analysis technology. The predicted speed has also improved significantly.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM76;TM715

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