本文關(guān)鍵詞： 負(fù)荷預(yù)測 對角遞歸神經(jīng)網(wǎng)絡(luò) 粒子群算法 灰色關(guān)聯(lián)分析 主成分分析　出處：《東北石油大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：負(fù)荷分析是實現(xiàn)當(dāng)?shù)仉娏σ?guī)劃的前提和基礎(chǔ)。在國家發(fā)展智能電網(wǎng)的大背景下,智能小區(qū)將是未來人們住宅小區(qū)呈現(xiàn)的主要形式,因此研究小區(qū)的用電負(fù)荷變化趨勢以及特點具有重要的意義。目前供電企業(yè)對智能小區(qū)用電負(fù)荷的分析以及預(yù)測結(jié)果的精度在實際應(yīng)用中并不是非常理想,主要原因是負(fù)荷本身會受到多種因素的影響而發(fā)生變化,所以不考慮干擾因素的簡單預(yù)測對預(yù)測結(jié)果和數(shù)據(jù)分析來說準(zhǔn)確度并不會很高。為了提高精度,基于原有的數(shù)據(jù)統(tǒng)計信息的采集模式和硬件設(shè)備,通過改變處理數(shù)據(jù)的方式來達(dá)到理想的效果。本文在研究了目前主要負(fù)荷預(yù)測方法的基礎(chǔ)上,通過對智能小區(qū)用電負(fù)荷的影響因素、數(shù)據(jù)維度進(jìn)行分析處理并以此作為理論,提出了一種適合智能小區(qū)用電負(fù)荷預(yù)測的分析方法。首先我們分析了與智能小區(qū)負(fù)荷變化相關(guān)的一些影響因素的特點,建立主要影響因素對智能小區(qū)用電負(fù)荷影響的灰關(guān)聯(lián)分析模型,得到了主要參數(shù)與用電負(fù)荷的灰關(guān)聯(lián)度,分析比較了用電量與各個影響因素的影響程度及其權(quán)重,分析結(jié)果表明小區(qū)用電量與大部分影響因素存在較強(qiáng)關(guān)聯(lián)性,并且每個影響因素的影響關(guān)聯(lián)度隨季節(jié)、地域、時間段變化都有著明顯的不同;其次,為提升后續(xù)負(fù)荷預(yù)測數(shù)據(jù)的處理速度,提出基于主成分分析方法的歷史數(shù)據(jù)預(yù)處理方法,可以降低數(shù)據(jù)維度,不僅減少數(shù)據(jù)量、剔除冗余數(shù)據(jù),提高了運算速度,而且避免數(shù)據(jù)量過少,保證運算精度。在進(jìn)行負(fù)荷預(yù)測前,對數(shù)據(jù)降維處理以及影響因素分析的基礎(chǔ)上,本文提出以對角遞歸神經(jīng)網(wǎng)絡(luò)算法為核心的智能小區(qū)用電負(fù)荷數(shù)據(jù)處理方法,以季節(jié)、常用電器、人口等影響因素為輸入量,以小區(qū)用電負(fù)荷為輸出量,通過對大量歷史數(shù)據(jù)進(jìn)行訓(xùn)練,并用粒子群算法優(yōu)化了神經(jīng)網(wǎng)絡(luò)的權(quán)值,從而預(yù)測目標(biāo)時段小區(qū)用電量,再與實際統(tǒng)計值進(jìn)行對比,從而判斷該預(yù)測模型的預(yù)測準(zhǔn)確度。經(jīng)過MATLAB仿真驗證表明,本文提出的方法可以對小區(qū)的負(fù)荷做出準(zhǔn)確度較高的預(yù)測,有效性得到了證實。
[Abstract]:Load analysis is the premise and foundation of local electric power planning. In the context of the national development of smart grid, intelligent community will be the main form of people's residential district in the future. Therefore, it is of great significance to study the changing trend and characteristics of power load in residential areas. At present, the accuracy of power load analysis and prediction in intelligent residential areas is not very ideal in practical application. The main reason is that the load itself will be affected by a variety of factors, so a simple prediction that does not take into account interference factors will not be very accurate for forecasting results and data analysis. Based on the original data statistical information collection mode and hardware equipment, the method of data processing is changed to achieve ideal results. This paper studies the main load forecasting methods. Through the analysis and processing of the influencing factors and data dimension of the power load in the intelligent residential area, this paper takes it as the theory. This paper presents an analysis method suitable for power load forecasting in intelligent residential areas. Firstly, we analyze the characteristics of some influencing factors related to load changes in intelligent residential areas. The grey correlation analysis model of the influence of main factors on the power load of intelligent residential area is established, and the grey correlation degree between the main parameters and the power load is obtained, and the influence degree and weight of the power consumption and each influencing factor are analyzed and compared. The results show that there is a strong correlation between electricity consumption and most of the influencing factors, and the correlation degree of each influencing factor varies significantly with the seasons, regions and time periods. Secondly, In order to improve the processing speed of subsequent load forecasting data, a historical data preprocessing method based on principal component analysis (PCA) is proposed, which can reduce the data dimension, reduce the amount of data, eliminate redundant data, and improve the operation speed. And to avoid too little data, to ensure the accuracy of the operation. Before load forecasting, the data dimension reduction processing and impact factors analysis, In this paper, a data processing method based on diagonal recurrent neural network algorithm is proposed for power load data processing in intelligent residential area. The data processing method takes the influence factors such as season, commonly used electrical appliances and population as the input quantity, and takes the cell power load as the output quantity. By training a large number of historical data and optimizing the weights of neural network with particle swarm optimization algorithm, the power consumption of the target period is forecasted, and then compared with the actual statistical value. The MATLAB simulation results show that the method proposed in this paper can predict the load of the residential area with high accuracy and its validity is verified.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張彥宇;肖茜;;國內(nèi)外關(guān)于電力系統(tǒng)負(fù)荷預(yù)測的研究現(xiàn)狀分析[J];山東工業(yè)技術(shù);2016年11期

2 魯思光;李碩;;基于改進(jìn)加權(quán)灰關(guān)聯(lián)度的變壓器故障診斷[J];高壓電器;2014年07期

3 黨存祿;吳青峰;葛智平;;智能電網(wǎng)技術(shù)在智能小區(qū)中的應(yīng)用研究[J];電網(wǎng)與清潔能源;2013年11期

4 朱敏捷;練振輝;王天智;;電力負(fù)荷影響因素研究[J];電子世界;2013年17期

5 黃欣;趙錦成;解璞;;基于BP神經(jīng)網(wǎng)絡(luò)的電站負(fù)荷預(yù)測仿真研究[J];國外電子測量技術(shù);2012年11期

6 張素香;;智能小區(qū)的商業(yè)智能[J];北京郵電大學(xué)學(xué)報;2012年05期

7 林弘宇;田世明;;智能電網(wǎng)條件下的智能小區(qū)關(guān)鍵技術(shù)[J];電網(wǎng)技術(shù);2011年12期

8 彭顯剛;胡松峰;呂大勇;;基于RBF神經(jīng)網(wǎng)絡(luò)的短期負(fù)荷預(yù)測方法綜述[J];電力系統(tǒng)保護(hù)與控制;2011年17期

9 李瓊;孟慶林;;基于RBF神經(jīng)網(wǎng)絡(luò)的建筑逐時空調(diào)負(fù)荷預(yù)測模型[J];華南理工大學(xué)學(xué)報(自然科學(xué)版);2008年10期

10 葛繼科;邱玉輝;吳春明;蒲國林;;遺傳算法研究綜述[J];計算機(jī)應(yīng)用研究;2008年10期

相關(guān)碩士學(xué)位論文 前7條

1 楊明;基于改進(jìn)神經(jīng)網(wǎng)絡(luò)算法的智能電網(wǎng)短期負(fù)荷預(yù)測[D];哈爾濱理工大學(xué);2016年

2 白純;智能小區(qū)居民負(fù)荷需求響應(yīng)策略研究[D];湖南大學(xué);2016年

3 韓迎春;基于灰色關(guān)聯(lián)度分析和改進(jìn)推理鏈的電網(wǎng)故障診斷[D];西南交通大學(xué);2016年

4 白晶;Apriori算法及其在智能小區(qū)用電分析中的應(yīng)用研究[D];華北電力大學(xué);2014年

5 李慧姣;基于主成分分析法的遺傳神經(jīng)網(wǎng)絡(luò)短期負(fù)荷預(yù)測的研究[D];湖南大學(xué);2012年

6 林承就;福州市住宅小區(qū)用電負(fù)荷計算的研究[D];中南大學(xué);2008年

7 張欣;遞歸神經(jīng)網(wǎng)絡(luò)的研究及在非線性動態(tài)系統(tǒng)辨識中的應(yīng)用[D];太原理工大學(xué);2005年

，

本文編號：1512609