本文選題：空間負(fù)荷預(yù)測(cè) + 負(fù)荷規(guī)律性�。� 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：空間負(fù)荷預(yù)測(cè)(spatial load forecasting,SLF)是電力系統(tǒng)規(guī)劃設(shè)計(jì)的先決條件,因此,高精度的空間負(fù)荷預(yù)測(cè)結(jié)果對(duì)城市電網(wǎng)的規(guī)劃設(shè)計(jì)具有極其重要的意義。與系統(tǒng)的負(fù)荷預(yù)測(cè)相比,空間電力負(fù)荷具有明顯的時(shí)空分布特性。歷史數(shù)據(jù)是SLF的根基,預(yù)測(cè)時(shí)所使用數(shù)據(jù)的真實(shí)性及準(zhǔn)確性將影響預(yù)測(cè)精度的高低。當(dāng)電力系統(tǒng)由于通信等原因而出現(xiàn)人為干擾因素時(shí),歷史負(fù)荷數(shù)據(jù)中會(huì)夾雜許多可疑數(shù)據(jù)的情況。這些可疑數(shù)據(jù)的出現(xiàn)將可能導(dǎo)致預(yù)測(cè)模型和結(jié)果與實(shí)際水平間的差異超出系統(tǒng)的閾值,從而使預(yù)測(cè)工作失去實(shí)際意義。因此,欲提高預(yù)測(cè)結(jié)果的可信度,對(duì)預(yù)測(cè)基礎(chǔ)數(shù)據(jù)進(jìn)行分析處理就顯得非常重要。選取恰當(dāng)合理的歷史負(fù)荷進(jìn)行合理性分析,去偽存真,準(zhǔn)確、經(jīng)濟(jì)、合理地進(jìn)行電網(wǎng)變電站布點(diǎn)、線路走廊規(guī)劃,使SLF在城市電網(wǎng)規(guī)劃中發(fā)揮更大的作用。本文以大量歷史負(fù)荷數(shù)據(jù)為基礎(chǔ),著重分析了城市配電網(wǎng)電力負(fù)荷的變化特點(diǎn),充分挖掘了歷史負(fù)荷數(shù)據(jù)中蘊(yùn)含的變化規(guī)律性,提取出歷史負(fù)荷數(shù)據(jù)本身含有的趨勢(shì)性和規(guī)律性分量,剝離對(duì)負(fù)荷預(yù)測(cè)帶來不利影響的隨機(jī)性分量,確定不含內(nèi)蘊(yùn)隨機(jī)分量的元胞負(fù)荷的合理最大值,實(shí)現(xiàn)了較為精確的負(fù)荷預(yù)測(cè)。研究負(fù)荷周期性分析理論,結(jié)合集合經(jīng)驗(yàn)?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition,EEMD)理論,提出了空間負(fù)荷預(yù)測(cè)中基于EEMD分解來確定元胞合理最大值的方法,用以避免元胞負(fù)荷實(shí)測(cè)數(shù)據(jù)將測(cè)量、通信等過程中的誤差帶入預(yù)測(cè)過程,而導(dǎo)致預(yù)測(cè)結(jié)果精度降低的問題。通過EEMD分解將各元胞負(fù)荷分解成一系列本征模函數(shù),并建立濾取機(jī)制,分別重構(gòu)表征規(guī)律性部分的主體分量和表征隨機(jī)性部分的隨機(jī)分量。剔除對(duì)預(yù)測(cè)結(jié)果帶來不利影響的隨機(jī)誤差,將剩余部分最大值作為元胞負(fù)荷的合理最大值。針對(duì)城市電網(wǎng)總量負(fù)荷預(yù)測(cè),提出一種城市電網(wǎng)總量負(fù)荷的雙向預(yù)測(cè)方法。利用用電量與電力負(fù)荷之間的相關(guān)關(guān)系,將歷史用電量數(shù)據(jù)轉(zhuǎn)化為電力負(fù)荷數(shù)據(jù),并采用雙向預(yù)測(cè)的方法進(jìn)行預(yù)測(cè)。該方法充分挖掘歷史用電量數(shù)據(jù)與電力負(fù)荷數(shù)據(jù)之間的內(nèi)在聯(lián)系,以歷史用電量數(shù)據(jù)為基礎(chǔ)求得電力負(fù)荷數(shù)據(jù),從而豐富負(fù)荷數(shù)據(jù)結(jié)構(gòu),避免了歷史數(shù)據(jù)的不充分和直接在原始電力負(fù)荷年最大值的歷史數(shù)據(jù)的基礎(chǔ)上進(jìn)行預(yù)測(cè)的缺陷,提高了數(shù)據(jù)的準(zhǔn)確性與穩(wěn)定性,降低了電力負(fù)荷年最大值數(shù)據(jù)的波動(dòng)性對(duì)預(yù)測(cè)帶來的不利影響。將元胞負(fù)荷合理最大值的確定方法引入到空間負(fù)荷預(yù)測(cè)中,在網(wǎng)格化負(fù)荷密度指標(biāo)法的基礎(chǔ)上,通過分析不同分辨率下對(duì)預(yù)測(cè)結(jié)果的誤差趨勢(shì),以及不同分辨率下的計(jì)算速度,選取最佳的空間分辨率以提高空間負(fù)荷預(yù)測(cè)的準(zhǔn)確性。
[Abstract]:Spatial load forecasting (spatial load forecasting) is a prerequisite for power system planning and design. Therefore, high precision spatial load forecasting results are of great significance for urban power network planning and design. Compared with the load forecasting of the system, the spatial electric load has obvious spatial and temporal distribution characteristics. Historical data is the foundation of SLF. The accuracy and authenticity of the data used in prediction will affect the accuracy of prediction. When artificial interference occurs in power system due to communication and other reasons, the historical load data will be mixed with many suspicious data. The appearance of these suspicious data may cause the difference between the prediction model and the actual level to exceed the threshold of the system, so that the prediction work will lose its practical significance. Therefore, in order to improve the reliability of prediction results, it is very important to analyze and process the basic prediction data. Selecting appropriate and reasonable historical load for rationality analysis, distinguishing false things, retaining real things, accurately, economically and reasonably carrying out substation distribution and line corridor planning in power grid makes SLF play a more important role in urban power network planning. Based on a large number of historical load data, this paper emphatically analyzes the characteristics of power load variation in urban distribution network, and fully excavates the variation regularity contained in historical load data. The trend and regularity components contained in the historical load data are extracted, the random components that bring adverse effects on load forecasting are stripped, and the reasonable maximum value of the cellular load without intrinsic random components is determined. More accurate load forecasting is realized. Based on the theory of periodic load analysis and the theory of Ensemble Empirical Mode decomposition (EEMD), a method based on EEMD decomposition to determine the reasonable maximum value of cell in spatial load forecasting is proposed, which is used to avoid the measurement of the measured data of cell load. The error in the process of communication is brought into the prediction process, which leads to the decrease of the precision of the prediction result. The cellular load is decomposed into a series of eigenmode functions by EEMD decomposition, and the filtering mechanism is established to reconstruct the principal component representing the regular part and the random component representing the random part, respectively. The maximum value of the remaining part is taken as the reasonable maximum value of the cell load. This paper presents a bidirectional forecasting method for the total load of urban power network. Based on the correlation between electricity consumption and power load, the historical power consumption data is transformed into power load data, and the bidirectional forecasting method is used to forecast the data. This method fully excavates the internal relation between the historical electricity consumption data and the power load data, and obtains the electric power load data based on the historical electricity consumption data, thus enriches the load data structure. It avoids the insufficiency of historical data and the defect of forecasting directly on the basis of historical data of the original annual maximum value of electric load, and improves the accuracy and stability of the data. The volatility of annual maximum data of power load is reduced to the adverse effect of forecasting. The method of determining the reasonable maximum value of cellular load is introduced into spatial load forecasting. On the basis of grid load density index method, the error trend of prediction results under different resolutions and the calculation speed at different resolutions are analyzed. The best spatial resolution is chosen to improve the accuracy of spatial load forecasting.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM715

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本文編號(hào)：1824983