电力系统负荷预测及方法(外文翻译)

Power system load forecasting methods and characteristics of Abstract: The load forecasting 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 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.

Keywords: power system load forecasting electricity market construction Planning

1.Introduction

Load forecasting demand for electricity from a known starting to consider the political, economic, climate and other 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 demand projections decision generation, transmission and distribution system, the sic of new Capacity; power generating equipment determine the type of prediction (.such as peaking units, base load units, etc}.

Load forecasting purposes is 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 development of each plan year to determine the load composition.

2. load forecasting methods and characteristics of

2.1 Unit Consumption Act

Output of products 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 projection 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 increased year by year, the output value unit consumption is declining. Unit consumption method advantages arc: The method is simple, short-torn load forecasting effective. Disadvantages arc: need to do a lot of painstaking research work, more general, it is difficult to reflect modern economic, political and climate conditions.

2.2 Trend extrapolation

When the power load in accordance with time-varying present same kind of upward or downward trend, and no 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 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 tune series of the future value of the moment. This is the trend extrapolation.

Application of the trend 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 changed 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 arc two basic ways.

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 trend prediction method. Trend extrapolation method's advantages arc: 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.

2.3 Elastic Coefficient Method

Elasticity coefficient is the average growth rate of electricity consumption to GDP ratio of between, according to the gross domestic product growth rate of coefficient of elasticity to be planning with the end of the total electricity consumption. Modules of elasticity law is determined on power development from a macro with the relative speed of national economic development, which is a measure of national economic development and an important parameter in electricity demand. The advantages of this method arc: The method is simple, easy to calculate. Disadvantages arc: need to do a lot of detailed research work.

2.4 Regression Analysis Method

Regression estimate 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 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 medium-torn toad forecast. Advantages arc: a higher prediction accuracy for the medium and the use of short-term forecasts. The disadvantage is that: (1) planning level it is difficult years of industrial and agricultural output statistics; (2) regression analysis can only be measured out the level of development of an integrated electricity load can not be measured out the power supply area of the loading level of development, thus can not

be the specific grid construction plan.

2.5 Time Series Analysis

The load is on the basis of historical data, trying to build a mathematical model, using this mathematical 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 expression of load forecasting type, to predict the future load. Time series are mainly autoregressive AR (p), moving average MA (q) and self-regression and n3oving average ARMA (p, q) and so on. The advantages of these methods arc: the historical data required for less, work less. The disadvantage is that: There is no change in load factor to consider, only dedicated to the data fitting, the lack of regularity of treatment is only applicable to relatively uniform changes in the short-term load forecasting situation.

2.6 Gray model method

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 identify 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.

Ordinary gray 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.; drawback is that for a change in volatility The power load, the prediction error largo, does not meet actual needs. And the gray model

optimization can have ups and downs of the original data sequence transformed into increased exponentially increasing regularity changes in sequence, greatly improving prediction accuracy and the gray model method of application. Gray Model Law applies to short-torn load forecast. Gray predicted advantages: smaller load data requirements, without regard to the distribution of laws and do not take into account trends, computing convenient, short-term forecasts of high precision, easy to test. Drawbacks: First, when the data the greater the degree of dispersion, namely, the greater the gray level data, prediction accuracy is worse; 2 is not very suitable for the long-term power system to push a number of years after the forecast.

2.7 Delphi Method

The Delphi method is based on the special knowledge of direct experience, research problems of judgment, a method 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; (2)can get different but valuable ideas and opinions; (3)suitable for long-term forecasts in historical data, insufficient or unpredictable factors is particularly applicable more. Detect is: (1)the load forecasting far points area may not reliable; (2)the expert opinions sometimes may not complete or impractical.

2.8 Expert System Approach

Expert system prediction is stored in the database over the past tow years, even decades, the Hourly load and weather data analysis, which brings together experienced staff knowledge load forecasting, 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 mankind itself: Therefore, you need expert systems such technologies. Expert systems approach is a non-quantifiable human experience translated into a better way But experts systems analysis itself is a time-consuming process, and some complex factors (such as weather factors), even though aware of its load impact, ht}t to accurately and quantitatively determine their influence on the load area is also very difficult. Expert system for forecasting method suitable for medium and long-term load forecast. The advantages of this method: (1)can bring together multiple expert knowledge and experience to maximize the ability of experts; (2) possession of data, information and mort factors to consider a more comprehensive and beneficial to arrive at mart accurate conclusions. The disadvantage is that: (1)do not have the self-learning ability, subject to the knowledge stored in the database limits the total; (2) pairs of unexpected incidents and poor adaptability to changing conditions 2.9 Neural Network Method

Neural network (ANN, Artificial Neural Network) forecasting techniques 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 applied to be mart 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 fuming point leading to a mathematical model-based damage. Advantages arc:(1) to mimic the human brain, intelligence processing; (2}a large number of non-structural. non-adaptive function of the accuracy of the law; (3)with the information memory, self-learning, knowledge, reasoning and optimization of computing features. The disadvantage is that:(1) the determination of the initial value can not take advantage of existing system information, easily trapped in local minimum of the state; (2) neural network learning process is usually slow, poor

adaptability to sudden events.

2.10 Optimum Combination Forecasting Method

Optimal combination has two meanings: First, several forecasting methods from the results obtained by selecting the appropriate a0cight 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 forecasting method must also noted that the combined forecast is a single forecasting model can not completely correct to describe the changes of the amount predicted to play a role. One can fully reflect the actual law of development of the model predictions agree well with the combination forecasting method than predicted good results. This method has the advantage: To optimize the combination of a wide range of information on a single prediction model, consider the impact of information is also mart comprehensive, so it can effectively improve the prediction. The disadvantage is that: (1) the weight is difficult to determine; (2) all possible factors that play a role in the future, all included in the model, to a certain extent, limit the prediction accuracy improved.

2.11 Wavelet analysis and forecasting techniques

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 according to the signal sampling frequency of high and low density, it is cast' to capture and analysis of weak signals and signal, images of any small parts. The advantage is: Can the different frequency components gradually refined using a sampling step, which can be gathered in any of the details of the signal, especially for singular signal is very sensitive to the treatment well or mutation weak signals, their goal is to a signal information into wavelet coefficients, which can easily be dealt with, storage, transmission, analysis or for the reconstruction of the original signal. These advantages determine the wavelet analyses can be effectively applied to load forecasting issues.

3. Conclusion

Load forecasting is the electric power system scheduling, real-time control, operation 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, 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 construction planning of the power system, improve the economic benefit and social benefit. Therefore, the load forecast has become a power system management modernization realization of the important content.

电力系统负荷预测及方法

摘要：负荷预测在电力系统规划和运行方面发挥的重要作用，具有明显的经济效益，负荷预测实质上是对电力市场需求的预测。该文系统地介绍和分析了各种负荷预测的方法及特点，并指出做好负荷预测己成为实现电力系统管理现代化的重要手段。

关键词：电力系统负荷预测电力市场建设规划

1. 引言

负荷预测是从已知的用电需求出发，考虑政治、经济、气候等相关因素，对未来的用电需求做出的顶测。负荷预测包括两方而的含义:对未来需求量(功率)的顶测和未来用电量(能量)的顶测。电力需求量的预测决定发电、输电、配电系统新增容量的大小;电能预测决定发电设备的类型(如调峰机组、基荷机组等)。负荷预测的日的就是提供负荷发展状况及水平，同时确定各供电区、各规划年供用电量、供用电最大负荷和规划地区总的负荷发展水平，确定各规划年用电负荷构成。

2.负荷预测的方法及特点

2.1单耗法

按照国家女排的产品产量、产值计划和用电单耗确定需电量。单耗法分“产品单耗法”和“产值单耗法”两种。采用“单耗法”预测负荷前的关键是确定适当的产品单耗或产值单耗。从我国的实际情况来看，一般规律是产品单耗逐年上升，产值单耗逐年卜降。单耗法的优点是:方法简单，对短期负荷预测效果较好。缺点是:需做大量细致的调研工作，比较笼统，很难反映现代经济、政治、气候等条件的影响。

2.2趋势外推法

当电力负荷依时间变化呈现某种上升或下降的趋势，并且无明显的季节波动，又能找到一条合适的函数曲线反映这种变化趋势时，就可以用时间t为自变量，时序数值y为因变量，建立趋势模型Y=f(t)。当有理由相信这种趋势能够延伸到未来时，赋予变量t所需要的值，可以得到相应时刻的时间序列米来值。这就是趋势外推法。应用趋势外推法有两个假设条件：(1)假设负荷没有跳跃式变化; (2)假定负荷的发展因素也决定负荷未来的发展，其条件是不变或变化不大。选择合适的趋势模型是应用趋势外推法的重要环节，图形识别法和差分法是选择趋势模型的两种基本方法。外推法有线性趋势预测法、对数趋势顶测法、二次曲线趋势顶测法、指数曲线趋势预测法、生长曲线趋势预测法。趋势外推法的优点是:只需要历史数据、所需的数据量较少。缺点是:如果负荷出现变动，会引起较大的误差。

2. 3弹性系数法

弹性系数是电量平均增长率与国内生产总值之间的比值，根据国内生产总值的增长速度结合弹性系数得到规划期末的总用电量。弹性系数法是从宏观上确定电力发展同国民经济发展的相对速度，它是衡量国民经济发展和用电需求的重要参数。该方法的优点是：方法简单，易于计算。缺点是：需做大量细致的调研工作。

2.4回归分析法

回归预测是根据负荷过去的历史资料，建立可以进行数学分析的数学模型。用数理统计中的回归分析方法对变量的观测数据统计分析，从而实现对未来的负荷进行预测。回归模型有一元线性回归、多元线性回归、非线性回归等回归预测模型。其中，线性回归用于中期负荷预测。优点是:顶测精度较高，适用于在中、短期顶测使用。缺点是:(1)规划水平年的工农业总产值很难详细统计;(2)用回归分析法只能测算出综合用电负荷的发展水平，无法测算

出各供电区的负荷发展水平，也就无法进行具体的电网建设规划。

2.5时间序列法

就是根据负荷的历史资料，设法建立一个数学模型，用这个数学模型一方面来描述电力负荷这个随机变量变化过程的统计规律性;另一方而在该数学模型的基础上再确立负荷预测的数学表达式，对未来的负荷进行预测。时间序列法主要有自回归AR (p) .滑动平均MA (q)和自回归与滑动平均ARMA (p, q)等。这些方法的优点是：所需历史数据少、工作量少。缺点是：没有考虑负荷变化的因素，只致力于数据的拟合，对规律性的处理不足，只适用于负荷变化比较均匀的短期顶测的情况。

2.6灰色模型法

灰色预测是一种对含有不确定因素的系统进行预测的方法。以灰色系统理论为基础的灰色顶测技术，可在数据不多的情况下找出某个时期内起作用的规律，建立负荷顶测的模型，分为普通灰色系统模型和最优化灰色模型两种。普通灰色预测模型是一种指数增长模型，当电力负荷严格按指数规律持续增长时，此法有预测精度高、所需样本数据少、计算简便、可检验等优点；缺点是对于具有波动性变化的电力负荷，其预测误差较大，不符合实际需要。向最优化灰色模型可以把有起伏的原始数据序列变换成规律性增强的成指数递增变化的序列，大大提高顶测精度和灰色模型法的适用范围。灰色模型法适用于短期负荷预测。灰色预测的优点：要求负荷数据少、不考虑分布规律、不考虑变化趋势、运算方便、短期顶测精度高易于检验。缺点：一是当数据离散程度越大，即数据灰度越大，预测精度越差；二是不太适合于电力系统的长期后推若干年的预测。

2.7德尔菲法

德尔菲法是根据有专门知识的人的直接经验，对研究的问题进行判断、预测的一种方法，也称专家调查法。德尔菲法其有反馈性、展名性和统计性的特点。德尔菲法的优点是：(1)可以加快顶测速度和节约预测费用;(2)可以获得各种不同但有价值的观点和意见;(3)适用于长期预测，在历史资料不足或不可预测因素较多尤为适用。缺点是：(1)对于分地区的负荷预测则可能不可靠;(2)专家的意见有时可能不完整或不切实际。

2.8专家系统法

专家系统预测法是对数据库只存放的过去几年甚至几十年的，每小时的负荷和大气数据进行分析，从而汇集有经验的负荷顶测人员的知识，提取有关规则，按照一定的规则进行负荷预测。实践证明，精确的负荷预测不仅需要高新技术的支撑，同时也需要融合人类自身的经验和智慧。因此，就会需要专家系统这样的技术。专家系统法，是对人类的不可量化的经验进行转化的一种较好的方法。但专家系统分析本身就是一个耗时的过程，并且某些复杂的因素(如大气因素)，即使知道其对负荷的影响，但要准确定量地确定他们对负荷地区的影响也是很难的。专家系统顶测法适用于中、长期负荷预测。此法的优点是：(1)功能汇集多个专家的知识和经验，最大限度地利用专家的能力;(2)占有的资料、信息多，考虑的

因素也比较全而，有利于得出较为正确的结论。缺点是：(1)不具有自学习能力，受数据库里存放的知识总量的限制;(2)对突发性事件和不断变化的条件适应性差。

2. 9神经网络法

神经网络(ANN, Artificial Neural Network)预测技术，可以模仿人脑做智能化处理，对大量非结构性、非确定性规律具有自适应功能。ANN应用于短期负荷预测比应用于中长期负荷预测更为适宜。因为，短期负荷变化可以认为是一个平稳随机过程。而长期负荷预测可能会因政治、经济等大的转折导致其模型的数学基础的破坏。优点是：(1)可以模仿人脑的智能化处理;(2)对大量非结构性、非精确性规律具有自适应功能;(3)具有信息记忆、自主学习、知识推理和优化计算的特点。缺点是:(1)初始值的确定无法利用己有的系统信息，易陷于局部极小的状态;(2)神经网络的学习过程通常较慢，对突发事件的适应性斧。

2. 10优选组合顶测法

优选组合有两层含义：一是从几种预测方法得到的结果中选取适当的权重加权平均;二是指在几种预测方法中进行比较，选择拟和度最佳或标准偏差最小的预测模型进行预测。对于组合预测方法也必需注意到，组合预测是在单个预测模型不能完全正确地描述顶测量的变化规律时发挥作用。一个能够完全反映实际发展规律的模型进行预测完全可能比用组合预测方法预测效果好。该方法的优点是:优选组合了多种单一预测模型的信息，考虑的影响信息也比较全而，因而能够有效地改善顶测效果。缺点是:(1)权重的确定比较困难;(2)不可能将所有在未来起作用的因素全包含在模型中，在一定程度上限制了预测精度的提高。

2. 11小波分析顶测技术

小波分析是一种时域-频域分析法，它在时域和频域上同时具有良好的局部化性质，并且能根据信号频率高低自动调节采样的疏密，它容易捕捉和分析微弱信号以及信号、图像的任意细小部分。其优点是：能对不同的频率成分采用逐渐精细的采样步长，从而可以聚集到信号的任意细节，尤其是对奇异信号很敏感，能很好的处理微弱或突变的信号，其日标是将一个信号的信息转化成小波系数，从而能够方便地加以处理、储存、传递、分析或被用于重建原始信一号。这些优点决定了小波分析可以有效地应用于负荷预测问题的研究。

3.结束语

负荷预测是电力系统调度、实时控制、运行计划和发展规划的前提，是一个电网调度部门和规划部门所必须具有的基本信息。提高负荷预测技术水平，有利于计划用电管理，有利于合理女排电网运行方式和机组检修计划，有利于节煤、节油和降低发电成本，有利于制定合理的电源建设规划，有利于提高电力系统的经济效益和社会效益。因此，负荷预测己成为实现电力系统管理现代化的重要内容。