外文翻譯---電力系統(tǒng)負(fù)荷預(yù)測及方法

1、<p><b>　　中文3420字</b></p><p><b>　　附錄A　外文資料</b></p><p>　　Power system load forecasting methods and characteristics of</p><p>　　Abstract: The load forecast

2、ing in power system planning and operation play an important role, with obvious economic benefits, in essence, the electricity load forecasting market demand forecast. In this paper, a systematic description and analysis

3、 of a variety of load forecasting methods and characteristics and that good load forecasting for power system has become an important means of modern management.</p><p>　　Keywords: power system load forecas

4、ting electricity market construction planning</p><p>　　1. Introduction</p><p>　　Load forecasting demand for electricity from a known starting to consider the political, economic, climate and o

5、ther related factors, the future demand for electricity to make predictions. Load forecast includes two aspects: on the future demand (power) projections and future electricity consumption (energy) forecast. Electricity

6、demand projections decision 　generation, transmission and distribution system, the size of new capacity; power generating equipment determine the type of prediction (su</p><p>　　Load forecasting purposes is

7、to provide load conditions and the level of development, while identifying the various supply areas, each year planning for the 　power consumption for maximum power load and the load of planning the overall level of deve

8、lopment of each plan year to determine the load composition.</p><p>　　2. load forecasting methods and characteristics of</p><p>　　2.1 Unit Consumption Act</p><p>　　Output of product

9、s in accordance with national arrangements, planning and electricity intensity value to determine electricity demand. Sub-Unit Consumption Act; Product Unit Consumption; and the value of Unit Consumption Act; two. the pr

10、ojection of load before the key is to determine the appropriate value of the product unit consumption or unit consumption. Judging from China's actual situation, the general rule is the product unit consumption incre

11、ased year by year, the output value unit consum</p><p>　　2.2 Trend extrapolation</p><p>　　When the power load in accordance with time-varying present some kind of upward or downward trend, and n

12、o obvious seasonal fluctuations, but also to find a suitable function curve to reflect this change in trend, you can use the time t as independent variables, timing value of y for the dependent variable to establish the

13、trend model y = f (t). When the reason to believe that this trend will extend to the future, we assigned the value of the variable t need to, you can get the corresponding time</p><p>　　Application of the tr

14、end extrapolation method has two assumptions: (1) assuming there is no step change in load; (2)assume that the development of load factors also determine the future development of load and its condition is unchanged or c

15、hanged little. Select the appropriate trend model is the application of the trend extrapolation an important part of pattern recognition method and finite difference method is to select the trend model are two basic ways

16、.</p><p>　　A linear trend extrapolation forecasting method, the logarithmic trend forecasting method, quadratic curve trend forecasting method, exponential curve trend forecasting method, growth curve of the

17、 trend prediction method. Trend extrapolation method's advantages are: only need to historical data, the amount of data required for less. The disadvantage is that: If a change in load will cause large errors.</p&

18、gt;<p>　　2.3 Elastic Coefficient Method </p><p>　　Elasticity coefficient is the average growth rate of electricity consumption to GDP ratio of between, according to the gross domestic product growth r

19、ate of coefficient of elasticity to be planning with the end of the total electricity consumption. Modulus of elasticity law is determined on power development from a macro with the relative speed of national economic de

20、velopment, which is a measure of national economic development and an important parameter in electricity demand. The advantages of</p><p>　　2.4 Regression Analysis Method </p><p>　　Regression es

21、timate is based on past history of load data, build up a mathematical analysis of the mathematical model. Of mathematical statistics, regression analysis of the variables in statistical analysis of observational data in

22、order to achieve load to predict the future. Regression model with a linear regression, multiple linear regression, nonlinear regression and other regression prediction models. Among them, linear regression for the mediu

23、m-term load forecast. Advantages are: a higher </p><p>　　2.5 Time Series Analysis </p><p>　　The load is on the basis of historical data, trying to build a mathematical model, using this mathemat

24、ical model to describe the power load on the one hand this random variable of statistical regularity of the change process; the other hand, the mathematical model based on the re-establishment of the mathematical express

25、ion of load forecasting type, to predict the future load. Time series are mainly autoregressive AR (p), moving average MA (q) and self-regression and moving average ARMA (p, q) a</p><p>　　2.6 Gray model meth

26、od </p><p>　　Gray prediction is a kind of a system containing uncertain factors to predict approach. Gray system theory based on the gray forecasting techniques may be limited circumstances in the data to id

27、entify the role of law within a certain period, the establishment of load forecasting models. Is divided into ordinary gray system model and optimization model for two kinds of gray. </p><p>　　Ordinary gray

28、prediction model is an exponential growth model, when the electric load in strict accordance with exponentially growing, this method has high accuracy and required less sample data to calculate simple and testable etc.;

29、drawback is that for a change in volatility The power load, the prediction error larger, does not meet actual needs. And the gray model optimization can have ups and downs of the original data sequence transformed into i

30、ncreased exponentially increasing regularity c</p><p>　　2.7 Delphi Method </p><p>　　The Delphi method is based on the special knowledge of direct experience, research problems of judgment, a met

31、hod for prediction of, also called experts investigation. Delphi method has feedback, anonymity and statistical characteristics. Delphi method advantage is: (1) can accelerate prediction speed and save prediction cost; (

32、2)can get different but valuable ideas and opinions; (3)suitable for long-term forecasts in historical data, insufficient or unpredictable factors is particularly applica</p><p>　　2.8 Expert System Approach

33、</p><p>　　Expert system prediction is stored in the database over the past few years, even decades, the hourly load and weather data analysis, which brings together experienced staff knowledge load forecasti

34、ng, extract the relevant rules, according to certain rules, load prediction. Practice has proved that accurate load forecasting requires not only high-tech support, but also need to reconcile the experience and wisdom of

35、 mankind itself. Therefore, you need expert systems such technologies. Expert syste</p><p>　　2.9 Neural Network Method </p><p>　　Neural network (ANN, Artificial Neural Network) forecasting techn

36、iques to mimic the human brain to do intelligent processing, a large number of non-structural, non-deterministic laws of adaptive function. ANN used in short-term load forecasting and long-term load forecast than that ap

37、plied to be more appropriate. Because short-term load changes can be regarded as a stationary random process. And long-term load forecasting may be due to political, economic and other major turning point leading to</

38、p><p>　　2.10 Optimum Combination Forecasting Method </p><p>　　Optimal combination has two meanings: First, several forecasting methods from the results obtained by selecting the appropriate weight

39、in the weighted average; 2 refers to the comparison of several prediction methods, choose the best or the degree of preparation and the standard deviation of the smallest prediction model forecast. For the combined forec

40、asting method must also noted that the combined forecast is a single forecasting model can not completely correct to describe the changes of the </p><p>　　2.11 Wavelet analysis and forecasting techniques <

41、;/p><p>　　Wavelet analysis is a time-domain - frequency domain analysis method, it is in the time domain and frequency domain at the same time has good localization properties, and can automatically adjust acco

42、rding to the signal sampling frequency of high and low density, it is easy to capture and analysis of weak signals and signal, images of any small parts. The advantage is: Can the different frequency components gradually

43、 refined using a sampling step, which can be gathered in any of the details of t</p><p>　　3. Conclusion </p><p>　　Load forecasting is the electric power system scheduling, real-time control, ope

44、ration plan and development planning, the premise is a grid dispatching departments and planning departments must have the basic information. Improve load forecasting technology level, be helpful for program management,

45、reasonable arrangement of the electricity grid operation mode for the maintenance plan and the crew, to section coal, fuel-efficient and reduce generating cost, be helpful for formulate rational power</p><p>

46、;　　From: Power System Technology</p><p>　　電力系統(tǒng)負(fù)荷預(yù)測及方法</p><p>　　摘要：負(fù)荷預(yù)測在電力系統(tǒng)規(guī)劃和運(yùn)行方面發(fā)揮的重要作用，具有明顯的經(jīng)濟(jì)效益，負(fù)荷預(yù)測實(shí)質(zhì)上是對電力市場需求的預(yù)測。該文系統(tǒng)地介紹和分析了各種負(fù)荷預(yù)測的方法及特點(diǎn)，并指出做好負(fù)荷預(yù)測已成為實(shí)現(xiàn)電力系統(tǒng)管理現(xiàn)代化的重要手段。</p><p>　

47、　關(guān)鍵詞：電力系統(tǒng)　負(fù)荷預(yù)測　電力市場　建設(shè)規(guī)劃 </p><p><b>　　1.　引言 　　</b></p><p>　　負(fù)荷預(yù)測是從已知的用電需求出發(fā)，考慮政治、經(jīng)濟(jì)、氣候等相關(guān)因素，對未來的用電需求做出的預(yù)測。負(fù)荷預(yù)測包括兩方面的含義：對未來需求量(功率)的預(yù)測和未來用電量(能量)的預(yù)測。電力需求量的預(yù)測決定發(fā)電、輸電、配電系統(tǒng)新增容量的大?。浑娔茴A(yù)測決定發(fā)電

48、設(shè)備的類型(如調(diào)峰機(jī)組、基荷機(jī)組等)。 負(fù)荷預(yù)測的目的就是提供負(fù)荷發(fā)展?fàn)顩r及水平，同時(shí)確定各供電區(qū)、各規(guī)劃年供用電量、供用電最大負(fù)荷和規(guī)劃地區(qū)總的負(fù)荷發(fā)展水平，確定各規(guī)劃年用電負(fù)荷構(gòu)成。 </p><p>　　2. 負(fù)荷預(yù)測的方法及特點(diǎn) </p><p>　　2.1　單耗法 　　</p><p>　　按照國家安排的產(chǎn)品產(chǎn)量、產(chǎn)值計(jì)劃和用電單耗確定需電量。單耗法分“

49、產(chǎn)品單耗法”和“產(chǎn)值單耗法”兩種。采用“單耗法”預(yù)測負(fù)荷前的關(guān)鍵是確定適當(dāng)?shù)漠a(chǎn)品單耗或產(chǎn)值單耗。從我國的實(shí)際情況來看，一般規(guī)律是產(chǎn)品單耗逐年上升，產(chǎn)值單耗逐年下降。單耗法的優(yōu)點(diǎn)是：方法簡單，對短期負(fù)荷預(yù)測效果較好。缺點(diǎn)是：需做大量細(xì)致的調(diào)研工作，比較籠統(tǒng)，很難反映現(xiàn)代經(jīng)濟(jì)、政治、氣候等條件的影響。 </p><p>　　2.2　趨勢外推法 　　</p><p>　　當(dāng)電力負(fù)荷依時(shí)間變化呈現(xiàn)

50、某種上升或下降的趨勢，并且無明顯的季節(jié)波動，又能找到一條合適的函數(shù)曲線反映這種變化趨勢時(shí)，就可以用時(shí)間t為自變量，時(shí)序數(shù)值y為因變量，建立趨勢模型y＝f(t)。當(dāng)有理由相信這種趨勢能夠延伸到未來時(shí)，賦予變量t所需要的值，可以得到相應(yīng)時(shí)刻的時(shí)間序列未來值。這就是趨勢外推法。應(yīng)用趨勢外推法有兩個(gè)假設(shè)條件：(1)假設(shè)負(fù)荷沒有跳躍式變化；(2)假定負(fù)荷的發(fā)展因素也決定負(fù)荷未來的發(fā)展，其條件是不變或變化不大。選擇合適的趨勢模型是應(yīng)用趨勢外推法的重

51、要環(huán)節(jié)，圖形識別法和差分法是選擇趨勢模型的兩種基本方法。外推法有線性趨勢預(yù)測法、對數(shù)趨勢預(yù)測法、二次曲線趨勢預(yù)測法、指數(shù)曲線趨勢預(yù)測法、生長曲線趨勢預(yù)測法。趨勢外推法的優(yōu)點(diǎn)是：只需要?dú)v史數(shù)據(jù)、所需的數(shù)據(jù)量較少。缺點(diǎn)是：如果負(fù)荷出現(xiàn)變動，會引起較大的誤差。 </p><p>　　2.3　彈性系數(shù)法 　</p><p>　　彈性系數(shù)是電量平均增長率與國內(nèi)生產(chǎn)總值之間的比值，根據(jù)國內(nèi)生產(chǎn)總值的增

52、長速度結(jié)合彈性系數(shù)得到規(guī)劃期末的總用電量。彈性系數(shù)法是從宏觀上確定電力發(fā)展同國民經(jīng)濟(jì)發(fā)展的相對速度，它是衡量國民經(jīng)濟(jì)發(fā)展和用電需求的重要參數(shù)。該方法的優(yōu)點(diǎn)是：方法簡單，易于計(jì)算。缺點(diǎn)是：需做大量細(xì)致的調(diào)研工作。 </p><p>　　2.4　回歸分析法 　　</p><p>　　回歸預(yù)測是根據(jù)負(fù)荷過去的歷史資料，建立可以進(jìn)行數(shù)學(xué)分析的數(shù)學(xué)模型。用數(shù)理統(tǒng)計(jì)中的回歸分析方法對變量的觀測數(shù)據(jù)統(tǒng)計(jì)

53、分析，從而實(shí)現(xiàn)對未來的負(fù)荷進(jìn)行預(yù)測?；貧w模型有一元線性回歸、多元線性回歸、非線性回歸等回歸預(yù)測模型。其中，線性回歸用于中期負(fù)荷預(yù)測。優(yōu)點(diǎn)是：預(yù)測精度較高，適用于在中、短期預(yù)測使用。缺點(diǎn)是：(1)規(guī)劃水平年的工農(nóng)業(yè)總產(chǎn)值很難詳細(xì)統(tǒng)計(jì)；(2)用回歸分析法只能測算出綜合用電負(fù)荷的發(fā)展水平，無法測算出各供電區(qū)的負(fù)荷發(fā)展水平，也就無法進(jìn)行具體的電網(wǎng)建設(shè)規(guī)劃。</p><p>　　2.5　時(shí)間序列法 　　</p>

54、<p>　　就是根據(jù)負(fù)荷的歷史資料，設(shè)法建立一個(gè)數(shù)學(xué)模型，用這個(gè)數(shù)學(xué)模型一方面來描述電力負(fù)荷這個(gè)隨機(jī)變量變化過程的統(tǒng)計(jì)規(guī)律性；另一方面在該數(shù)學(xué)模型的基礎(chǔ)上再確立負(fù)荷預(yù)測的數(shù)學(xué)表達(dá)式，對未來的負(fù)荷進(jìn)行預(yù)測。時(shí)間序列法主要有自回歸AR(p)、滑動平均MA(q)和自回歸與滑動平均ARMA(p,q)等。這些方法的優(yōu)點(diǎn)是：所需歷史數(shù)據(jù)少、工作量少。缺點(diǎn)是：沒有考慮負(fù)荷變化的因素，只致力于數(shù)據(jù)的擬合，對規(guī)律性的處理不足，只適用于負(fù)荷變

55、化比較均勻的短期預(yù)測的情況。</p><p>　　2.6　灰色模型法 　　</p><p>　　灰色預(yù)測是一種對含有不確定因素的系統(tǒng)進(jìn)行預(yù)測的方法。以灰色系統(tǒng)理論為基礎(chǔ)的灰色預(yù)測技術(shù)，可在數(shù)據(jù)不多的情況下找出某個(gè)時(shí)期內(nèi)起作用的規(guī)律，建立負(fù)荷預(yù)測的模型，分為普通灰色系統(tǒng)模型和最優(yōu)化灰色模型兩種。普通灰色預(yù)測模型是一種指數(shù)增長模型，當(dāng)電力負(fù)荷嚴(yán)格按指數(shù)規(guī)律持續(xù)增長時(shí)，此法有預(yù)測精度高、所需樣本

56、數(shù)據(jù)少、計(jì)算簡便、可檢驗(yàn)等優(yōu)點(diǎn)；缺點(diǎn)是對于具有波動性變化的電力負(fù)荷，其預(yù)測誤差較大，不符合實(shí)際需要。而最優(yōu)化灰色模型可以把有起伏的原始數(shù)據(jù)序列變換成規(guī)律性增強(qiáng)的成指數(shù)遞增變化的序列，大大提高預(yù)測精度和灰色模型法的適用范圍?；疑Ｐ头ㄟm用于短期負(fù)荷預(yù)測?；疑A(yù)測的優(yōu)點(diǎn)：要求負(fù)荷數(shù)據(jù)少、不考慮分布規(guī)律、不考慮變化趨勢、運(yùn)算方便、短期預(yù)測精度高、易于檢驗(yàn)。缺點(diǎn)：一是當(dāng)數(shù)據(jù)離散程度越大，即數(shù)據(jù)灰度越大，預(yù)測精度越差；二是不太適合于電力系統(tǒng)的長期

57、后推若干年的預(yù)測。 </p><p>　　2.7　德爾菲法 　　</p><p>　　德爾菲法是根據(jù)有專門知識的人的直接經(jīng)驗(yàn)，對研究的問題進(jìn)行判斷、預(yù)測的一種方法，也稱專家調(diào)查法。德爾菲法具有反饋性、匿名性和統(tǒng)計(jì)性的特點(diǎn)。德爾菲法的優(yōu)點(diǎn)是：(1)可以加快預(yù)測速度和節(jié)約預(yù)測費(fèi)用；(2)可以獲得各種不同但有價(jià)值的觀點(diǎn)和意見；(3)適用于長期預(yù)測，在歷史資料不足或不可預(yù)測因素較多尤為適用。缺點(diǎn)是

58、：(1)對于分地區(qū)的負(fù)荷預(yù)測則可能不可靠；(2)專家的意見有時(shí)可能不完整或不切實(shí)際。</p><p>　　2.8　專家系統(tǒng)法 　　</p><p>　　專家系統(tǒng)預(yù)測法是對數(shù)據(jù)庫里存放的過去幾年甚至幾十年的，每小時(shí)的負(fù)荷和天氣數(shù)據(jù)進(jìn)行分析，從而匯集有經(jīng)驗(yàn)的負(fù)荷預(yù)測人員的知識，提取有關(guān)規(guī)則，按照一定的規(guī)則進(jìn)行負(fù)荷預(yù)測。實(shí)踐證明，精確的負(fù)荷預(yù)測不僅需要高新技術(shù)的支撐，同時(shí)也需要融合人類自身的經(jīng)驗(yàn)

59、和智慧。因此，就會需要專家系統(tǒng)這樣的技術(shù)。專家系統(tǒng)法，是對人類的不可量化的經(jīng)驗(yàn)進(jìn)行轉(zhuǎn)化的一種較好的方法。但專家系統(tǒng)分析本身就是一個(gè)耗時(shí)的過程，并且某些復(fù)雜的因素(如天氣因素)，即使知道其對負(fù)荷的影響，但要準(zhǔn)確定量地確定他們對負(fù)荷地區(qū)的影響也是很難的。專家系統(tǒng)預(yù)測法適用于中、長期負(fù)荷預(yù)測。此法的優(yōu)點(diǎn)是：(1)能匯集多個(gè)專家的知識和經(jīng)驗(yàn)，最大限度地利用專家的能力；(2)占有的資料、信息多，考慮的因素也比較全面，有利于得出較為正確的結(jié)論。缺點(diǎn)

60、是：(1)不具有自學(xué)習(xí)能力，受數(shù)據(jù)庫里存放的知識總量的限制；(2)對突發(fā)性事件和不斷變化的條件適應(yīng)性差。</p><p>　　2.9　神經(jīng)網(wǎng)絡(luò)法 　　</p><p>　　神經(jīng)網(wǎng)絡(luò)(ANN, Artificial Neural Network)預(yù)測技術(shù)，可以模仿人腦做智能化處理，對大量非結(jié)構(gòu)性、非確定性規(guī)律具有自適應(yīng)功能。ANN應(yīng)用于短期負(fù)荷預(yù)測比應(yīng)用于中長期負(fù)荷預(yù)測更為適宜。因?yàn)椋唐谪?fù)

61、荷變化可以認(rèn)為是一個(gè)平穩(wěn)隨機(jī)過程。而長期負(fù)荷預(yù)測可能會因政治、經(jīng)濟(jì)等大的轉(zhuǎn)折導(dǎo)致其模型的數(shù)學(xué)基礎(chǔ)的破壞。優(yōu)點(diǎn)是：(1)可以模仿人腦的智能化處理；(2)對大量非結(jié)構(gòu)性、非精確性規(guī)律具有自適應(yīng)功能；(3)具有信息記憶、自主學(xué)習(xí)、知識推理和優(yōu)化計(jì)算的特點(diǎn)。缺點(diǎn)是：(1)初始值的確定無法利用已有的系統(tǒng)信息，易陷于局部極小的狀態(tài)；(2)神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)過程通常較慢，對突發(fā)事件的適應(yīng)性差。</p><p>　　2.10　優(yōu)選組

62、合預(yù)測法 　</p><p>　　優(yōu)選組合有兩層含義：一是從幾種預(yù)測方法得到的結(jié)果中選取適當(dāng)?shù)臋?quán)重加權(quán)平均；二是指在幾種預(yù)測方法中進(jìn)行比較，選擇擬和度最佳或標(biāo)準(zhǔn)偏差最小的預(yù)測模型進(jìn)行預(yù)測。對于組合預(yù)測方法也必需注意到，組合預(yù)測是在單個(gè)預(yù)測模型不能完全正確地描述預(yù)測量的變化規(guī)律時(shí)發(fā)揮作用。一個(gè)能夠完全反映實(shí)際發(fā)展規(guī)律的模型進(jìn)行預(yù)測完全可能比用組合預(yù)測方法預(yù)測效果好。該方法的優(yōu)點(diǎn)是：優(yōu)選組合了多種單一預(yù)測模型的信息，

63、考慮的影響信息也比較全面，因而能夠有效地改善預(yù)測效果。缺點(diǎn)是：(1)權(quán)重的確定比較困難；(2)不可能將所有在未來起作用的因素全包含在模型中，在一定程度上限制了預(yù)測精度的提高。</p><p>　　2.11 小波分析預(yù)測技術(shù) 　　</p><p>　　小波分析是一種時(shí)域-頻域分析法，它在時(shí)域和頻域上同時(shí)具有良好的局部化性質(zhì)，并且能根據(jù)信號頻率高低自動調(diào)節(jié)采樣的疏密，它容易捕捉和分析微弱信號

64、以及信號、圖像的任意細(xì)小部分。其優(yōu)點(diǎn)是：能對不同的頻率成分采用逐漸精細(xì)的采樣步長，從而可以聚集到信號的任意細(xì)節(jié)，尤其是對奇異信號很敏感，能很好的處理微弱或突變的信號，其目標(biāo)是將一個(gè)信號的信息轉(zhuǎn)化成小波系數(shù)，從而能夠方便地加以處理、儲存、傳遞、分析或被用于重建原始信號。這些優(yōu)點(diǎn)決定了小波分析可以有效地應(yīng)用于負(fù)荷預(yù)測問題的研究。 </p><p><b>　　3.　結(jié)束語 　　</b></