發(fā)布時(shí)間：2018-04-15 22:23

  本文選題：產(chǎn)業(yè)電量 + 關(guān)聯(lián)分析�。� 參考：《長(zhǎng)沙理工大學(xué)》2014年碩士論文

【摘要】：用電量規(guī)律分析及預(yù)測(cè)是電網(wǎng)規(guī)劃與建設(shè)的基礎(chǔ),也是電力需求側(cè)管理的有效指南。隨著近年來(lái)經(jīng)濟(jì)的高速發(fā)展,產(chǎn)業(yè)結(jié)構(gòu)的復(fù)雜變化,使得電力需求內(nèi)部結(jié)構(gòu)發(fā)生著一定程度的變動(dòng)。同時(shí),用電量的季節(jié)波動(dòng)性也導(dǎo)致部分地區(qū)電力需求仍存在季節(jié)性缺失。因此,需要在考慮季節(jié)因素的情況下,對(duì)不同類(lèi)型的用電量進(jìn)行分析與預(yù)測(cè),以實(shí)現(xiàn)更加精細(xì)化的用電管理及制定更加經(jīng)濟(jì)化的購(gòu)售電策略。本論文從產(chǎn)業(yè)和生活用電出發(fā),做了如下研究：首先,從用電量自相關(guān)、互相關(guān)和氣溫影響因素三個(gè)方面對(duì)第一、第二、第三產(chǎn)業(yè)以及城、鄉(xiāng)居民用電量進(jìn)行相關(guān)性分析,即產(chǎn)業(yè)及生活用電量與自身歷史數(shù)據(jù)的關(guān)聯(lián)關(guān)系,各季節(jié)下五個(gè)用電量指標(biāo)之間的內(nèi)在關(guān)聯(lián)關(guān)系和氣溫對(duì)其的外在影響關(guān)系。運(yùn)用相關(guān)分析趨勢(shì)圖對(duì)以上三方面情況下各用電量趨勢(shì)進(jìn)行初步判斷以后,計(jì)算其皮爾森相關(guān)系數(shù),量化其相關(guān)密切程度,得到產(chǎn)業(yè)及生活用電量在以上三種情況下的對(duì)應(yīng)關(guān)聯(lián)關(guān)系。其次,根據(jù)得到各季節(jié)下三大產(chǎn)業(yè)以及城鄉(xiāng)居民用電量相互之間的關(guān)聯(lián)關(guān)系和氣溫對(duì)其的影響關(guān)系,構(gòu)建各季節(jié)下VEC(Vector Error Correction)用電量預(yù)測(cè)模型。模型構(gòu)建過(guò)程包括以下三部分：第一部分對(duì)各季節(jié)下五個(gè)用電量序列和氣溫序列進(jìn)行平穩(wěn)性檢驗(yàn)；第二部分對(duì)于通過(guò)平穩(wěn)性檢驗(yàn)的用電量序列進(jìn)行協(xié)整關(guān)系分析并對(duì)存在協(xié)整關(guān)系的各用電量建立協(xié)整方程；第三部分基于用電量之間的協(xié)整關(guān)系,構(gòu)建各季節(jié)下VEC用電量預(yù)測(cè)模型。基于此模型進(jìn)行了預(yù)測(cè)算例分析及對(duì)比,證明了其預(yù)測(cè)效果的準(zhǔn)確性。最后,考慮運(yùn)用從產(chǎn)業(yè)及生活用電量自相關(guān)關(guān)系和互相關(guān)關(guān)系兩個(gè)角度分別構(gòu)建的預(yù)測(cè)模型進(jìn)行組合預(yù)測(cè)。即先基于產(chǎn)業(yè)及生活用電量指標(biāo)自相關(guān)關(guān)聯(lián)關(guān)系,根據(jù)各用電量平穩(wěn)性檢驗(yàn)和相關(guān)關(guān)系圖構(gòu)建各季節(jié)下ARIMA(Auto-Regressive Integrated Moving Average)用電量預(yù)測(cè)模型。根據(jù)ARIMA模型與VEC模型預(yù)測(cè)的相對(duì)誤差,計(jì)算各月份下兩模型的最優(yōu)權(quán)重分配結(jié)果,得到各月的組合預(yù)測(cè)模型。對(duì)某省網(wǎng)2011年產(chǎn)業(yè)及生活月度用電量進(jìn)行虛擬預(yù)測(cè)分析及對(duì)比,證明了該模型的準(zhǔn)確性和可靠性。本文從內(nèi)在關(guān)聯(lián)和外在影響的角度對(duì)產(chǎn)業(yè)及生活用電量進(jìn)行了相關(guān)性分析,并構(gòu)建了基于關(guān)聯(lián)關(guān)系的用電量預(yù)測(cè)模型。通過(guò)算例分析證明了所建立模型的準(zhǔn)確性和有效性,對(duì)電力企業(yè)進(jìn)行規(guī)劃與調(diào)度、提高電網(wǎng)經(jīng)濟(jì)運(yùn)行都具有一定的指導(dǎo)意義和參考價(jià)值。
[Abstract]:The analysis and prediction of power consumption law is the foundation of power network planning and construction, and it is also an effective guide for power demand side management.With the rapid development of economy and the complex changes of industrial structure in recent years, the internal structure of power demand has changed to a certain extent.At the same time, the seasonal fluctuation of electricity consumption also leads to the seasonal absence of electricity demand in some areas.Therefore, considering seasonal factors, different types of electricity consumption should be analyzed and forecasted, in order to achieve more refined management of electricity consumption and to formulate more economical power purchase and sale strategy.In this paper, the following research is done from industry and daily electricity consumption: firstly, from the three aspects of power consumption autocorrelation, cross-correlation and temperature influencing factors, the first, second, tertiary industry and urban and rural residents' electricity consumption are analyzed.That is, the relationship between industry and household electricity consumption and its own historical data, the internal relationship between five electricity consumption indexes in each season and the external influence of temperature on it.After using the correlation analysis trend map to judge the electricity consumption trends in the above three conditions, the Pearson correlation coefficient is calculated, and the correlation degree is quantified.Get the industry and living electricity consumption in the above three cases of the corresponding correlation.Secondly, according to the relationship between the three major industries and urban and rural residents' electricity consumption in different seasons and the influence of temperature on it, the VEC(Vector Error Correction (VEC(Vector Error Correction) electricity consumption prediction model in each season is constructed.The model construction process includes the following three parts: in the first part, five electricity consumption series and temperature series under each season are tested smoothly;In the second part, we analyze the cointegration relation of the electricity consumption series that pass the stationary test and establish the cointegration equation for the electricity consumption with cointegration relationship; the third part is based on the cointegration relationship between the electricity consumption.The prediction model of VEC power consumption in different seasons was constructed.Based on this model, the prediction results are analyzed and compared, and the accuracy of the prediction results is proved.Finally, the combined forecasting model which is constructed from two angles of industrial and household electricity consumption autocorrelation and cross-correlation is considered.Firstly, based on the autocorrelation relation of industry and household electricity consumption index, the forecast model of ARIMA(Auto-Regressive Integrated Moving average power consumption under different seasons is constructed according to the power consumption stationary test and correlation diagram.According to the relative error between the ARIMA model and the VEC model, the optimal weight distribution results of the two models in each month are calculated, and the combined prediction model for each month is obtained.The virtual prediction and comparison of the industry and monthly consumption of electricity in a province in 2011 proved the accuracy and reliability of the model.This paper analyzes the correlation between industry and household electricity consumption from the angle of internal relation and external influence, and constructs a model of electricity consumption prediction based on correlation relationship.The model is proved to be accurate and effective by example analysis. It has certain guiding significance and reference value for electric power enterprises to plan and dispatch, and to improve the economic operation of power network.
【學(xué)位授予單位】：長(zhǎng)沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F426.61

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本文編號(hào)：1756069