WO2021213192A1 - 一种基于通用分布的负荷预测方法及负荷预测*** - Google Patents

一种基于通用分布的负荷预测方法及负荷预测*** Download PDF

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WO2021213192A1
WO2021213192A1 PCT/CN2021/086342 CN2021086342W WO2021213192A1 WO 2021213192 A1 WO2021213192 A1 WO 2021213192A1 CN 2021086342 W CN2021086342 W CN 2021086342W WO 2021213192 A1 WO2021213192 A1 WO 2021213192A1
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temperature
power
load
data
histogram
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PCT/CN2021/086342
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French (fr)
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陶叶炜
徐箭
董甜
华夏
麦锦雯
柏筱飞
项敏
周力
童充
钱艺琳
李荷婷
王丹
陆峰
廖思阳
吴迪
杨昊
沈韵
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国网江苏省电力有限公司苏州供电分公司
武汉大学
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Publication of WO2021213192A1 publication Critical patent/WO2021213192A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

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  • the invention belongs to the technical field of power load forecasting, and specifically relates to a load forecasting method and a forecasting system based on the influence of universally distributed meteorological factors on load power consumption.
  • the existing research methods of electricity consumption forecasting can be divided into two categories, one is the indirect calculation method based on probability statistics, and the more common is the multiple regression model.
  • the dependent variable of the regression equation is often power load, which can be short-term load or long-term load.
  • the independent variables are various factors that affect power load, such as economy, policy, electricity price, and climate.
  • the disadvantage of regression analysis is that its realization requires a certain number of samples, and the samples must have a good distribution law and a definite development trend, and the computational workload is relatively large.
  • the other is the indirect method based on historical electricity consumption data.
  • the affected power supply units, grid capacity, user conditions, and other information are known.
  • the electrical load is a gray system.
  • Grey theory accumulates the irregular historical data series into a series of ascending shapes with exponential growth law. Since the formation of the first-order differential equation is an exponential growth form, a differential equation can be established for the generated series. Model. So the gray model is actually a model built by generating a sequence of numbers. The data obtained from the GM model must be inversely generated, that is, the cumulative subtractive generation and reduction can be applied.
  • the disadvantage is that the forecasting process does not consider the impact of the load by economy, climate, and policies, but directly processes and analyzes the time series data of electricity consumption.
  • the neural network theory uses the learning function of the neural network to allow the computer to learn the mapping relationship contained in the historical load data, and then use this mapping relationship to predict the future load.
  • BP neural network can simulate the nonlinear mapping relationship between any input and output.
  • a three-layer BP network is composed of input layer, hidden layer and output layer.
  • the training method is to calculate the actual output through a BP neural network for a set of input samples.
  • the error between the actual output of the BP network and the output sample is used to correct the connection weight of the network until the error between the two reaches the set value.
  • the input samples include load, temperature, weather conditions, and date types.
  • the target sample during training is load by time period.
  • the model application is to predict the load value of the next day from the load data of the previous one.
  • the disadvantage is that there are certain difficulties in processing non-textual historical data information and the existing experience and knowledge of dispatchers; it has high requirements for input data, and when the number of samples is relatively small, the prediction accuracy is reduced; for those that are far away from the training samples Data and forecast results are not ideal.
  • the wind power probability prediction method based on the numerical weather forecast ensemble forecast results
  • the specific method steps are: 1. Establish a short-term wind power prediction model for each member of the numerical weather forecast ensemble forecast, and input the numerical weather forecast results separately The wind power prediction result of each member is obtained, and the wind power prediction result of the ensemble forecast of the weather forecast is obtained. 2. Identify the error type of each set according to the wind power prediction result of each member in the set. 3. Divide the power levels of each set according to the wind power prediction result of the set. 4. Calculate the set of relative errors for sets of different error types and power levels. 5. Use the kernel estimation method to obtain the probability density function of each sample in the set. 6. Use non-parametric fitting methods to fit the probability density distribution and fit the regression function. 7. Perform regression verification on the fitted regression results. 8. Calculate the upper limit of error and the lower limit of error under a certain confidence level. 9. Calculate the estimated interval of power prediction under a certain confidence level according to the upper and lower limits of the error.
  • the multiple regression model has a simple model and perfect theory, which can fully consider various influencing factors, but it is easy to cause poor forecast accuracy due to improper selection of factors.
  • Conventional mathematical models have good short-term forecasting effects, but they rely too much on mathematical and physical mechanisms, and have greater limitations in long-term forecasts and practical applications.
  • the present invention discloses a load forecasting method and a forecasting system based on universal distribution, and is applied to load power forecasting.
  • a load forecasting method based on general distribution including the following steps:
  • Step 1 Collect historical data of load power and temperature at each moment of the load to be predicted within a set time period:
  • Step 2 Process the load power and temperature history data collected in Step 1, and calculate an array containing the temperature increment and the power ratio, where the temperature increment refers to the day after the same time minus the previous day at each time point The temperature increment of, the power ratio refers to the ratio of the power of the day after the same moment at each time point to the power of the previous day;
  • Step 3 Perform binning processing on the arrays containing temperature increments and power ratios obtained in step 2, and all the arrays are stored in their corresponding temperature increment grade data boxes:
  • Step 4 Establish the actual distribution model of the load power in each level data box based on the histogram:
  • Step 5 Use the general distribution function model to fit the data in each level data box to obtain the general distribution function corresponding to each level data box;
  • Step 6 According to the current power value of the load to be predicted and the temperature of the forecast day, based on the general distribution function fitted in step 5, determine the confidence interval of the power ratio of the load to be predicted under the forecast daily temperature condition to achieve the given weather Power prediction.
  • the present invention further includes the following preferred solutions.
  • step 1 collect the load power of the load to be predicted in the last three months and the temperature data of the corresponding time at 24 hours and minutes; among them, in the historical data, the sampling period of the load power is 1 min, which corresponds to every Each load power data point is the active power at 1 min; the sampling period of the temperature data is 5 min, that is, the temperature corresponding to each temperature data point at 5 min.
  • step 2 further include the following:
  • Step 2.1 for the historical data collected in step 1, establish a load power time series and a temperature time series respectively, where the load power time series is a time series every 1 minute; the temperature time series is a time series every 5 minutes;
  • Step 2.2 perform frequency reduction processing on the load power time series, take a point every 5 minutes, and unify the power and temperature into a time period of 5 minutes;
  • Step 2.3 Calculate the temperature increment of the next day minus the previous day at the same time at each time point and the ratio of the corresponding power of the next day to the power of the previous day; thus obtain the temperature increment sequence and the power ratio sequence, which correspond to each other Form an array, the first column of the array is the temperature increment, and the second column is the power ratio.
  • step 3 specifically include the following:
  • Step 3.1 classify the temperature increment, divide the temperature increment into R levels, that is, R data boxes, the R levels are divided into R levels evenly between the maximum and minimum temperature increments;
  • Step 3.2 according to the size of the "temperature increment" in each historical data array, store all the arrays calculated in step 2 into their corresponding temperature increment level data boxes to complete the binning process of the arrays.
  • the R 7.
  • step 4 draw the probability density histogram of the power ratio for each data box according to the "temperature increment" in the data box of each level.
  • step 4 specifically include the following:
  • Step 4.1 determine the total width of the histogram; obtain the range from the difference between the maximum value and the minimum value of the power ratio in the data box sample sets of different temperature increment levels. Product) to take the number greater than the range;
  • Step 4.2 determine the group number, group distance, and each group limit of the histogram, assuming that the group number of the histogram is N; the width of each group is 1/N, which is the group distance;
  • Step 4.3 determine the frequency of each group, divide the power ratio data in the sample set into each group of the histogram according to the size, and count the number of data in each group, which is the frequency;
  • Step 4.4 draw a probability density histogram
  • the abscissa is the power ratio
  • the ordinate is the probability density
  • each group of the histogram corresponds to a rectangle
  • the width of the rectangle is the group distance
  • the height is the probability density of each group
  • probability density frequency/total number of samples * number of groups.
  • N 50.
  • step 5 the general distribution is defined as follows:
  • the shape parameters ⁇ , ⁇ and ⁇ respectively satisfy:
  • the cumulative distribution function (Cumulative Distribution Function, CDF) F(x) expression of the general distribution is:
  • CDF F -1 (c) The inverse function of CDF F -1 (c) is expressed as:
  • f(x) is the probability density of the general distribution function
  • F(x) is the cumulative distribution value of the general distribution function
  • step 6 the temperature forecast value at each time point of the day to be predicted and the temperature value at the corresponding time point of the current day are compared to obtain the temperature increment sequence.
  • step 5 Determine the general distribution function shape parameter value fitted by the different temperature increment data boxes, calculate the expression of the general distribution CDF inverse function F -1 (c), and obtain the confidence interval of the load forecast power under the set confidence level :
  • w l,up and w l,low are the upper and lower bounds of the load power confidence interval, respectively.
  • the preferred confidence level conf is 0.95.
  • This application also discloses a load forecasting system based on the aforementioned load forecasting method, which includes a historical database, a data collection and processing unit, a data binning processing unit, a histogram unit, a fitting calculation unit, and a load power prediction calculation unit; its features Lies in:
  • the data collection and processing unit collects time-by-time load power and temperature in the historical data of the load to be predicted, unifies the load power and temperature sampling values into the same sampling frequency, and establishes an array containing a temperature increment sequence and a power ratio sequence;
  • the data binning processing unit performs binning processing on the array established by the data acquisition and processing unit based on the temperature increment;
  • the histogram unit draws a power ratio probability density histogram for each bin data, and calculates the power ratio probability density in each group in the histogram;
  • the fitting calculation unit uses a general distribution function model to fit the data in each level data box to obtain a general distribution function corresponding to each level data box;
  • the load power prediction calculation unit calculates the temperature of the load to be predicted on the predicted day based on the general distribution function of each corresponding level data box fitted by the fitting calculation unit according to the input current power value and the weather forecast temperature value of the predicted day
  • the confidence interval of the power ratio under the conditions can realize the power prediction under the given weather.
  • the associated probability model of the load change and the change of meteorological information is established.
  • the histogram model is used to approximate the actual distribution of the load power change under different temperature increments.
  • the general distribution model is selected to compare the actual distribution histogram.
  • the graph is fitted, and the model can effectively characterize the peak characteristics and off-axis characteristics of the distribution law with high fitting accuracy.
  • the incremental interval of the load power under the known weather forecast results can be analytically calculated under a certain confidence level, so as to realize the influence of weather on the power system load power forecast results. Dynamic correction after the factor.
  • the probabilistic model of the correlation between the temperature change and the temperature change can provide a quantitative data analysis method for analyzing the influence of temperature change on the load power, and this method is also suitable for analyzing the quantitative influence of other external factors such as humidity and rainfall on the load power change. relation.
  • Figure 1 is a schematic flow chart of a general distributed load forecasting method according to this application.
  • Fig. 2 is a probability distribution histogram of the fourth data box in the embodiment of the application.
  • FIG. 3 is an effect diagram after fitting the "power ratio" in the fourth box according to the embodiment of the application.
  • Figure 4 is a structural block diagram of a general distributed load forecasting system of this application.
  • FIG. 1 it is a schematic flowchart of a general distributed load forecasting method disclosed in this application. It includes the following steps:
  • a load forecasting method based on general distribution including the following steps:
  • Step 1 Collect historical data of load power and temperature at each moment of the load to be predicted within a set time period:
  • This application preferably collects the load power of the load to be predicted in the last three months, 24 hours a day, and the corresponding temperature data; among them, in the historical data, the sampling period of the load power is 1 min, which corresponds to each load The power data point is the active power at 1 min; the sampling period of the temperature data is 5 min, which means that each temperature data point corresponds to the temperature at 5 min.
  • Step 2 Process the load power and temperature history data collected in Step 1, and calculate an array containing the temperature increment and the power ratio, where the temperature increment refers to the day after the same time minus the previous day at each time point The temperature increment of, the power ratio refers to the ratio of the power of the day after the same moment at each time point to the power of the previous day;
  • Step 2.1 for the historical data collected in step 1, establish a load power time series and a temperature time series respectively, where the load power time series is a time series every 1 minute; the temperature time series is a time series every 5 minutes;
  • Step 2.2 perform frequency reduction processing on the load power time series, take a point every 5 minutes, and unify the power and temperature into a time period of 5 minutes;
  • Step 2.3 Calculate the temperature increment of the next day minus the previous day at the same time at each time point and the ratio of the corresponding power of the next day to the power of the previous day; thus obtain the temperature increment sequence and the power ratio sequence, which correspond to each other Form an array, the first column of the array is the temperature increment, and the second column is the power ratio.
  • Step 3 Perform binning processing on the arrays containing temperature increments and power ratios obtained in step 2, and all the arrays are stored in their corresponding temperature increment level data boxes;
  • Step 3 specifically includes the following:
  • Step 3.1 classify the temperature increment, divide the temperature increment into R levels, that is, R data boxes, the R levels are divided into R levels evenly between the maximum and minimum temperature increments;
  • Step 3.2 according to the size of the "temperature increment" in each historical data array, store all the arrays calculated in step 2 into their corresponding temperature increment level data boxes to complete the binning process of the arrays.
  • Step 4 Establish the actual distribution model of the load power in each level data box based on the histogram
  • step 4 specifically include the following:
  • Step 4.1 determine the total width of the histogram; subtract the minimum value from the maximum value in the temperature increment sequence to obtain the difference, and the total width of the histogram shall be the smallest integer greater than the difference;
  • Step 4.2 determine the group number, group distance, and each group limit of the histogram, assuming that the group number of the histogram is N; the width of each group is 1/N, which is the group distance;
  • Step 4.3 determine the frequency of each group, divide the power ratio data in the sample set into each group of the histogram according to the size, and count the number of data in each group, which is the frequency;
  • Step 4.4 draw a probability density histogram
  • the abscissa is the power ratio
  • the ordinate is the probability density
  • each group of the histogram corresponds to a rectangle
  • the width of the rectangle is the group distance
  • the height is the probability density of each group
  • probability density frequency/total number of samples * number of groups.
  • Step 5 Use the general distribution function model to fit the data in each level data box to obtain the general distribution function corresponding to each level data box;
  • the shape parameters ⁇ , ⁇ and ⁇ respectively satisfy:
  • the cumulative distribution function (Cumulative Distribution Function, CDF) F(x) expression of the general distribution is:
  • CDF F -1 (c) The inverse function of CDF F -1 (c) is expressed as:
  • f(x) is the probability density of the general distribution function
  • F(x) is the cumulative distribution value of the general distribution function
  • Step 6 According to the current power value of the load to be predicted and the temperature of the forecast day, based on the general distribution function fitted in step 5, determine the confidence interval of the power ratio of the load to be predicted under the forecast daily temperature condition to achieve the given weather Power prediction.
  • step 6 the temperature forecast value at each time point of the day to be predicted and the temperature value at the corresponding time point of the current day are compared to obtain the temperature increment sequence.
  • step 5 Determine the general distribution function shape parameter value fitted by the different temperature increment data boxes, calculate the expression of the general distribution CDF inverse function F -1 (c), and obtain the confidence interval of the load forecast power under the set confidence level :
  • w l,up and w l,low are the upper and lower bounds of the load power confidence interval, respectively.
  • the preferred confidence level conf is 0.95.
  • this application also discloses a load forecasting system based on the aforementioned load forecasting method, including a historical database, a data collection and processing unit, a data binning processing unit, a histogram unit, a fitting calculation unit, and a load forecasting system. Power prediction calculation unit.
  • the data collection and processing unit collects time-by-time load power and temperature in the historical data of the load to be predicted, unifies the load power and temperature sampling values into the same sampling frequency, and establishes an array containing a temperature increment sequence and a power ratio sequence;
  • the data binning processing unit performs binning processing on the array established by the data acquisition and processing unit based on the temperature increment;
  • the histogram unit draws a power ratio probability density histogram for each binning data, and calculates the histogram The power ratio probability density in each group;
  • the fitting calculation unit uses a general distribution function model to fit the data in each level data box to obtain the general distribution function corresponding to each level data box;
  • the load power prediction calculation Based on the input current power value and the weather forecast temperature value of the forecast day, the unit calculates the power ratio confidence of the load to be predicted under the forecast daily temperature based on the general distribution function of each corresponding level data box fitted by the fitting calculation unit Interval, to realize the power prediction under a given weather.
  • Step 1 Collect historical data of load power and temperature at each moment of the load to be predicted within a set time period.
  • the sampling period of load power is 1min, that is, the active power corresponding to each load power data point is 1min;
  • the sampling period of temperature data is 5min, that is, the temperature corresponding to each temperature data point is 5min.
  • Step 2 Data processing:
  • Step 2.1 In the collected data, the power is a time series of one period every 1min, corresponding to each data point is the active power at 1min; the temperature is a time series of every 5min, corresponding to each data point is 5min The temperature; every 24 hours a day, there are a total of 144 1min time periods and 288 5min time periods, so each power time series P has a total of 144 data, and each temperature time series has a total of 288 data.
  • Step 2.2 perform down-sampling on the power time series, take a point every 5 minutes, and unify the power and temperature into a time period of 5 minutes;
  • Step 2.3 Calculate the temperature increment of the next day minus the previous day at the same time at each time point and the ratio of the corresponding power of the next day to the power of the previous day; thus obtain the temperature increment sequence and the power ratio sequence, which correspond to each other Form an array, the first column of the array is the temperature increment, and the second column is the power ratio.
  • Step 3 Binning processing based on the temperature increment prediction level:
  • Step 3.1 classify the temperature increment:
  • the temperature increment it is divided into 7 levels, that is, 7 data boxes. Store the corresponding arrays into the temperature increment level data box respectively.
  • the value range of the temperature increment is within [-15.5°C, 18.5°C]. Divide it into 7 boxes, and the temperature increment in each box ranges from [-16,-11], [-11,-6], [-6,-1], [-1,4] , [4,9], [9,14], [14,19].
  • Step 3.2 after the data processing of step 2, the historical data has been divided into arrays with historical moments as the unit; according to the size of the "temperature increment" in each historical data array, all the arrays in the database are stored in it. In the corresponding temperature increment level data box, the binning process of the array is completed.
  • Step 4 The actual distribution model based on the histogram:
  • Step 4.1 determine the total width of the histogram; subtract the minimum value from the maximum value in the temperature increment sequence to obtain the difference, and the total width of the histogram shall be the smallest integer greater than the difference;
  • Step 4.3 determine the frequency of each group, divide the power ratio data in the sample set into the histogram groups according to the size, and count the number of data in each group, which is the frequency; step 4.4, draw the probability density histogram, the abscissa is Power ratio, the ordinate is the probability density, each group of the histogram corresponds to a rectangle, the width of the rectangle is the group distance, and the height is the probability density of each group.
  • probability density frequency/total number of samples * group number.
  • Step 5 Fit the data in the box:
  • the cumulative distribution function (Cumulative Distribution Function, CDF) F(x) expression of the general distribution is:
  • CDF F -1 (c) The inverse function of CDF F -1 (c) is expressed as:
  • the histogram is the actual distribution, and the continuous curve is the general distribution model effect.
  • Fig. 2 shows the probability distribution histogram of the fourth data box according to the embodiment of the application, and
  • Fig. 3 shows the effect diagram after fitting the "power ratio" in the fourth box according to the embodiment of the application.
  • Step 6 According to the current power value of the load to be predicted and the temperature of the forecast day, based on the general distribution function fitted in step 5, determine the confidence interval of the power ratio of the load to be predicted under the forecast daily temperature condition to achieve the given weather Power prediction.
  • the histogram is the actual distribution, and the continuous curve is the general distribution model effect.
  • Fig. 2 shows the probability distribution histogram of the fourth data box according to the embodiment of the application, and
  • Fig. 3 shows the effect diagram after fitting the "power ratio" in the fourth box according to the embodiment of the application.

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Abstract

一种基于通用分布的负荷预测方法及预测***，以实现电力***短期负荷功率预测。该方法将历史气象信息数据和电气数据相结合，针对不同的温度增量水平分别构建相应的统计分析模型。首先采用直方图模型来近似表征实际分布，然后选取适当的模型对实际分布进行拟合，本文采用通用分布函数模型进行拟合，并且依据通用分布CDF函数的逆函数的闭合解析表达式，得出一定置信水平下预测功率的置信区间。

Description

一种基于通用分布的负荷预测方法及负荷预测*** 技术领域：
本发明属于电力负荷预测技术领域，具体涉及一种基于通用分布的气象因素对负荷用电影响的负荷预测方法及预测***。
背景技术：
随着国民经济水平的提高，对电力的需求也越来越大，要进行电力的科学合理的调度，需要考虑的因素很多，其中气温是重要的影响因素。气温的变化在民用电和工业用电上都会造成区域负荷功率的显著变化。一方面，民用大功率耗电设备(例如制冷、取暖等用电设备)的使用与温度等气象因素密切相关；另一方面，气温变化同样会影响企业生产计划从而造成工业负荷功率的变化。因此，分析负荷功率与气温的定量关系并将其应用到负荷预测过程中是极为必要的。
目前，现有的用电量预测的研究方法可以分为两大类，一类是基于概率统计的间接计算方法，较为常见的是多元回归模型。在回归分析中，回归方程的因变量往往是电力负荷，可以是短期负荷，也可以是长期负荷，自变量是影响电力负荷的各种因素，比如经济、政策、电价和气候等。而回归分析的缺点在于它的实现要求有一定的样本数量，并且样本要有较好的分布规律和确定的发展趋势，计算工作量较大。另一类是基于用电量历史数据的间接法。比如常规数学模型，例如灰色预测模型和神经网络模型。对于电力***，对其影响的供电机组、电网容量、用户情况、等信息是已知的，但是，影响负荷的其他很多因素，比如气象情况、行政与管理政策的变化、地区经济活动等是难以确切知道的，因此，电力负荷是灰色***。灰色理论将无规律的历史数据列经累加生成后，使其变为具有指数增长规律的上升形状数列，由于一阶微分方程解的形成即是指数增长形式，所以可对生成后数列建立微分方程模型。所以灰色模型实际上是生成数列所建模型。GM模型所得数据必须经过逆生成，即累减生成做还原后才能应用。其缺点在于预测过程没有考虑负荷受经济、气候、政策的影响，而是直接对用电量的时间序列数据进行处理分析，此外，原始数据离散程度越大，灰度越大，预测精度越低。神经网络理论利用神经网络的学习功能，让计算机学习包含在历史负荷数据中的 映射关系，再利用这种映射关系预测未来负荷。BP神经网络能够模拟任意输入和输出之间的非线性映射关系。一个三层BP网络由输入层、隐含层和输出层组成。训练方法是，对于一组输入样本，通过BP神经网络计算实际输出。用BP网络的实际输出与输出样本之间的误差来修正网络的连接权值，直至二者的误差达到设定值。一般输入样本包含负荷、温度、天气情况和日期类型，训练时的目标样本是分时段负荷，模型应用是由前一条的负荷数据来预测第二天的负荷值。其缺点在于处理非文本类历史数据信息和调度人员的已有经验知识时存在一定的困难；对输入数据要求高，在样本数比较小的情况下，预测精度降低；对于离训练样本比较远的数据，预测效果也不理想。
现有技术中，基于数值天气预报集合预报结果的风电功率概率预测方法，具体方法步骤为：1.对数值天气预报集合预报的每个成员建立短期风电功率预测模型，分别输入数值天气预测结果后获得各个所述成员的风功率预测结果，得到天气预报集合预报的集合的风功率预测结果。2.根据所述集合中各成员的所述风电功率预测结果识别各个所述集合的误差类型。3.根据所述集合的风功率预测结果将各个所述集合的功率水平进行划分。4.计算不同误差类型和功率水平的集合的相对误差的集合。5.用核估计的方法得到所述集合中各个样本的概率密度函数。6.采用非参数拟合的方法拟合概率密度分布和拟合回归函数。7.对拟合回归结果进行回归校验。8.计算一定置信水平下的误差上限和误差下限。9.根据误差上下限计算得到一定置信度水平下功率预测的估计区间。
在现有的用电量预测的研究方法中，多元回归模型的模型简单、理论完善，能够充分考虑各种影响因素，但容易因为因素选取不当而造成预测精度不高。常规数学模型短期预测效果好，但是过度依赖于数学物理机制，在长期预测和现实应用中有较大的局限性。
发明内容：
针对现有技术存在的以上问题，本发明公开了一种基于通用分布的负荷预测方法及预测***，并应用于负荷功率预测。
本发明的上述技术问题主要通过以下技术方案得以解决：
一种基于通用分布负荷预测方法，包括以下步骤：
步骤1，采集设定时间段内待预测负荷的分时刻负荷功率及温度的历史数据：
步骤2，对步骤1所采集负荷功率以及温度历史数据的进行处理，计算得到包含温度增量和功率比值的数组，其中，所述温度增量是指每个时间点同一时刻后一天减前一天的温度增量，所述功率比值是指每个时间点同一时刻后一天的功率与前一天的功率的比值；
步骤3，对步骤2获得的包含温度增量和功率比值的数组进行分箱处理，所有数组分别存入其对应的温度增量等级数据箱中：
步骤4，建立基于直方图的各等级数据箱中负荷功率实际分布模型：
步骤5，采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；
步骤6，根据待预测负荷的当前功率值和预测日的温度，基于步骤5拟合得到的通用分布函数，确定待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
本发明进一步包括以下优选方案。
在步骤1中，采集待预测负荷在最近三个月内的全天24小时分时刻的负荷功率以及对应时刻的温度数据；其中，在历史数据中，负荷功率的采样周期为1min，即对应每个负荷功率数据点为1min时刻的有功功率；温度数据的采样周期为5min，即对应每个温度数据点为5min时刻的温度。
在步骤2中，进一步包括以下内容：
步骤2.1，对于步骤1所采集的历史数据，分别建立负荷功率时间序列和温度时间序列，其中，负荷功率时间序列为每1min一个时段的时间序列；温度时间序列为每5min一个时段的时间序列；
步骤2.2，对负荷功率时间序列进行降频处理，每隔5min取一个点，将功率与温度统一为5min一个时间段；
步骤2.3，求出每个时间点同一时刻后一天减前一天的温度增量以及对应的后一天的功率与前一天的功率的比值；从而得到温度增量序列与功率比值序列，二者对应起来形成数组，数组第一列为温度增量，第二列为功率比值。
在步骤3中，具体包括以下内容：
步骤3.1，对温度增量进行等级划分，按照温度增量划分为R个等级，即R个数据箱，所述R个等级是在温度增量最大值和最小值之间平均划分R个等级；
步骤3.2，根据每个历史数据数组中的“温度增量”的大小，将步骤2计算得到的所有数组分别存入其对应的温度增量等级数据箱中，完成数组的分箱过程。
优选地，所述R＝7。
在步骤4中，根据各等级数据箱中的“温度增量”，对各数据箱分别绘制功率比值的概率密度直方图。
在步骤4中，具体包括以下内容：
步骤4.1，确定直方图的总宽度；由不同温度增量等级数据箱样本集合中的功率比值的最大值与最小值之差求得极差，直方图的总宽度(即组距与组数的乘积)取大于极差的数；
步骤4.2，确定直方图的组数、组距和各组界限，假设直方图的组数为N；每组的宽度为1/N,即组距；
步骤4.3，确定各组频数，把样本集合中的功率比值数据按照大小划分到直方图各组，统计各组中数据的个数，即为频数；
步骤4.4，绘制概率密度直方图，横坐标为功率比值，纵坐标为概率密度，直方图每组对应一个矩形，矩形的宽度为组距，高度为各组的概率密度，概率密度与频数的换算关系：概率密度＝频数/总样本数*组数。
优选N＝50。
在步骤5中，通用分布定义如下：
若连续型随机变量X服从一个形状参数为α、β和γ的通用分布，则记为：
X～V(α,β,γ)
其中，形状参数α、β和γ分别满足：
α＞0,β＞0,-∞＜γ＜+∞
通用分布的概率密度函数(Probability Density Function，PDF)f(x)表达式为：
Figure PCTCN2021086342-appb-000001
通用分布的累积分布函数(Cumulative Distribution Function，CDF)F(x)表达式为：
F(x)＝(1+e -α(x-γ)) -β
CDF的反函数F -1(c)表达式为：
Figure PCTCN2021086342-appb-000002
其中，f(x)为通用分布函数的概率密度，F(x)为通用分布函数的累计分布值。
在步骤6中，将待预测日各时间点的温度预报值，与当前日对应时间点的温度值做差得到温度增量序列，根据所对应所属的不同温度增量数据箱，利用步骤5所确定的不同温度增量数据箱所拟合得到的通用分布函数形状参数值，计算通用分布CDF逆函数F -1(c)的表达式，从而获取设定置信水平下的负荷预测功率的置信区间：
Figure PCTCN2021086342-appb-000003
Figure PCTCN2021086342-appb-000004
其中，w l,up和w l,low分别为负荷功率置信区间的上界和下界。
优选置信水平conf为0.95。
本申请还公开了一种基于前述负荷预测方法的负荷预测***，包括历史数据库、数据采集及处理单元、数据分箱处理单元、直方图单元、拟合计算单元、负荷功率预测计算单元；其特征在于：
所述数据采集及处理单元采集待预测负荷历史数据中的分时刻负荷功率及温度，并将负荷功率和温度采样值统一为相同的采样频率，建立包含温度增量序列与功率比值序列的数组；
所述数据分箱处理单元对数据采集及处理单元所建立的数组基于温度增量进行分箱处理；
所述直方图单元针对每一分箱数据绘制功率比值概率密度直方图，并计算直方图中各分组中的功率比值概率密度；
所述拟合计算单元采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；
所述负荷功率预测计算单元根据输入的当前功率值和预测日的天气预报温度值，基于拟合计算单元拟合得到的各对应等级数据箱的通用分布函数，计算得到待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
相对于现有技术，本申请具有以下有益的技术效果。
通过概率建模的方法，建立负荷变化量与气象信息变化量的关联概率模型，采用直方图模型来近似表征不同温度增量条件下负荷功率变化量的实际分布，选取通用分布模型对实际分布直方图进行拟合，该模型能够有效表征分布规律的尖峰特性和偏轴特性且拟合精度高。利用该分布函数的累积概率密度函数可逆的数学性质，在确定的置信水平条件下能够解析求出负荷功率在已知气象预测结果下的增量区间，实现对电力***负荷功率预测结果加入气象影响因素后的动态修正。基于在不同日期的相同时刻下，温度增加了多少度，负荷功率相应地会增加多少倍，探究温度增量与功率比值之间的定量关系，具有创新性与可实现性，所建立的负荷变化量与温度变化量的关联概率模型，能够为分析温度变化对负荷功率的影响提供定量的数据分析方法，且该方法亦适用于分析其他外部因素如湿度、降雨量等对负荷功率变化的定量影响关系。
附图说明：
图1为本申请一种通用分布负荷预测方法流程示意图；
图2为本申请实施例第4个数据箱的概率分布直方图；
图3为本申请实施例对第4个箱内的“功率比值”进行拟合后的效果图；
图4为本申请一种通用分布负荷预测***结构框图。
具体实施方式：
下面结合说明书附图以及具体实施例对本发明的技术方案做进一步详细介绍。
如附图1所示为本申请公开的一种通用分布负荷预测方法流程示意图。所述包括以下步骤：
一种基于通用分布负荷预测方法，包括以下步骤：
步骤1，采集设定时间段内待预测负荷的分时刻负荷功率及温度的历史数据：
本申请优选采集待预测负荷在最近三个月内的全天24小时分时刻的负荷功率以及对应时刻的温度数据；其中，在历史数据中，负荷功率的采样周期为1min，即对应每个负荷功率数据点为1min时刻的有功功率；温度数据的采样周期为5min，即对应每个温度数据点为5min时刻的温度。
步骤2，对步骤1所采集负荷功率以及温度历史数据的进行处理，计算得到包含温度增量和功率比值的数组，其中，所述温度增量是指每个时间点同一时刻后一天减前一天的温度增量，所述功率比值是指每个时间点同一时刻后一天的功率与前一天的功率的比值；
步骤2.1，对于步骤1所采集的历史数据，分别建立负荷功率时间序列和温度时间序列，其中，负荷功率时间序列为每1min一个时段的时间序列；温度时间序列为每5min一个时段的时间序列；
步骤2.2，对负荷功率时间序列进行降频处理，每隔5min取一个点，将功率与温度统一为5min一个时间段；
步骤2.3，求出每个时间点同一时刻后一天减前一天的温度增量以及对应的后一天的功率与前一天的功率的比值；从而得到温度增量序列与功率比值序列，二者对应起来形成数组，数组第一列为温度增量，第二列为功率比值。
步骤3，对步骤2获得的包含温度增量和功率比值的数组进行分箱处理，所有数组分别存入其对应的温度增量等级数据箱中；
步骤3具体包括以下内容：
步骤3.1，对温度增量进行等级划分，按照温度增量划分为R个等级，即R个数据箱，所述R个等级是在温度增量最大值和最小值之间平均划分R个等级；
步骤3.2，根据每个历史数据数组中的“温度增量”的大小，将步骤2计算得到的所有数组分别存入其对应的温度增量等级数据箱中，完成数组的分箱过程。
其中，在本申请的优选方案中，优选R＝7。
步骤4，建立基于直方图的各等级数据箱中负荷功率实际分布模型；
在步骤4中，具体包括以下内容：
步骤4.1，确定直方图的总宽度；将温度增量序列中的最大值减去最小值，得到差值，直方图总宽度取大于这个差值的最小整数；
步骤4.2，确定直方图的组数、组距和各组界限，假设直方图的组数为N；每组的宽度为1/N,即组距；
根据大量的统计结果以及计算分析，本申请选取N＝50。
步骤4.3，确定各组频数，把样本集合中的功率比值数据按照大小划分到直方图各组，统计各组中数据的个数，即为频数；
步骤4.4，绘制概率密度直方图，横坐标为功率比值，纵坐标为概率密度，直方图每组对应一个矩形，矩形的宽度为组距，高度为各组的概率密度，概率密度与频数的换算关系：概率密度＝频数/总样本数*组数。
步骤5，采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；
通用分布定义如下：
若连续型随机变量X服从一个形状参数为α、β和γ的通用分布，则记为：
X～V(α,β,γ)
其中，形状参数α、β和γ分别满足：
α＞0,β＞0,-∞＜γ＜+∞
通用分布的概率密度函数(Probability Density Function，PDF)f(x)表达式为：
Figure PCTCN2021086342-appb-000005
通用分布的累积分布函数(Cumulative Distribution Function，CDF)F(x)表达式为：
F(x)＝(1+e -α(x-γ)) -β
CDF的反函数F -1(c)表达式为：
Figure PCTCN2021086342-appb-000006
其中，f(x)为通用分布函数的概率密度，F(x)为通用分布函数的累计分布值。
步骤6，根据待预测负荷的当前功率值和预测日的温度，基于步骤5拟合得到的通用分布函数，确定待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
在步骤6中，将待预测日各时间点的温度预报值，与当前日对应时间点的温度值做差得到温度增量序列，根据所对应所属的不同温度增量数据箱，利用步骤5所确定的不同温度增量数据箱所拟合得到的通用分布函数形状参数值，计算通用分布CDF逆函数F -1(c)的表达式，从而获取设定置信水平下的负荷预测功率的置信区间：
Figure PCTCN2021086342-appb-000007
Figure PCTCN2021086342-appb-000008
其中，w l,up和w l,low分别为负荷功率置信区间的上界和下界。
优选置信水平conf为0.95。
如附图4所示，本申请还公开了一种基于前述负荷预测方法的负荷预测***，包括历史数据库、数据采集及处理单元、数据分箱处理单元、直方图单元、拟合计算单元、负荷功率预测计算单元。
所述数据采集及处理单元采集待预测负荷历史数据中的分时刻负荷功率及温度，并将负荷功率和温度采样值统一为相同的采样频率，建立包含温度增量序列与功率比值序列的数组；所述数据分箱处理单元对数据采集及处理单元所建立的数组基于温度增量进行分箱处理；所述直方图单元针对每一分箱数据绘制功率比值概率密度直方图，并计算直方图中各分组中的功率比值概率密度；所述拟合计算单元采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；所述负荷功率预测计算单元根据输入的当前功率值和预测日的天气预报温度值，基于拟合计算单元拟合得到的各对应等级数据箱的通用分布函数，计算得到待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
下面以某工业园区作为实施例进行介绍：
步骤1，采集设定时间段内待预测负荷的分时刻负荷功率及温度的历史数据在本实施例中，采集2016/11/1-2016/11/30；2017/7/1-2017/8/31一共三个月的全天24小时分时刻的负荷功率以及对应时刻的温度。其中，负荷功率的采样周期为1min，即对应每个负荷功率数据点为1min时刻的有功功率；温度数据的采样周期为5min，即对应每个温度数据点为5min时刻的温度。
步骤2，数据的处理：
步骤2.1，所收集到的数据中，功率为每1min一个时段的时间序列，对应每个数据点为1min时刻的有功功率；温度为每5min一个时段的时间序列，对应每个数据点为5min时刻的温度；每一天24小时,共有144个1min时间段和288个5min时间段，因此每一功率时间序列P共有144个数据，每一温度时间序列共有288个数据。
Figure PCTCN2021086342-appb-000009
步骤2.2，对功率时间序列进行降频采样，每隔5min取一个点，将功率与温度统一为5min一个时间段；
步骤2.3，求出每个时间点同一时刻后一天减前一天的温度增量以及对应的后一天的功率与前一天的功率的比值；从而得到温度增量序列与功率比值序列，二者对应起来形成数组，数组第一列为温度增量，第二列为功率比值。
步骤3，基于温度增量预测等级的分箱处理：
步骤3.1，对温度增量进行等级划分：
按照温度增量划分为7个等级，即7个数据箱。将对应数组分别存入温度增量等级数据箱。在本实施例中，温度增量的取值范围在[-15.5℃,18.5℃]之内。将其分为7个箱，每个箱内的温度增量取值范围为[-16,-11]，[-11,-6]，[-6,-1]，[-1,4]，[4,9]，[9,14]，[14,19]。
步骤3.2，经过步骤2的数据处理后,历史数据已被划分为了以历史时刻为单位的数组；根据每个历史数据数组中的“温度增量”的大小，将数据库中所有数组分别存入其对应的温度增量等级数据箱中，完成数组的分箱过程。
步骤4，基于直方图的实际分布模型：
根据各等级数据箱中的“温度增量”样本，对各数据箱分别绘制功率比值的概率密度直方图；
步骤4.1，确定直方图的总宽度；将温度增量序列中的最大值减去最小值，得到差值，直方图总宽度取大于这个差值的最小整数；
步骤4.2，确定直方图的组数、组距和各组界限，假设直方图的组数为N＝50；每组的宽度为1/N,即组距；
步骤4.3，确定各组频数，把样本集合中的功率比值数据按照大小划分到直 方图各组，统计各组中数据的个数，即为频数；步骤4.4，绘制概率密度直方图，横坐标为功率比值，纵坐标为概率密度，直方图每组对应一个矩形，矩形的宽度为组距，高度为各组的概率密度，概率密度与频数的换算关系：概率密度＝频数/总样本数*组数。
步骤5，箱内数据拟合：
采用通用分布函数模型对箱内数据进行拟合；
通用分布定义如下：
若连续型随机变量X服从一个形状参数为α、β和γ的通用分布，则记为：
X～V(α,β,γ)其中，形状参数α、β和γ分别满足：
α＞0,β＞0,-∞＜γ＜+∞
通用分布的概率密度函数(Probability Density Function，PDF)f(x)表达式为：
Figure PCTCN2021086342-appb-000010
通用分布的累积分布函数(Cumulative Distribution Function，CDF)F(x)表达式为：
F(x)＝(1+e -α(x-γ)) -β
CDF的反函数F -1(c)表达式为：
Figure PCTCN2021086342-appb-000011
对于不同的温度增量等级分别进行拟合，拟合求得的α，β和γ值分别如下表1所示：表1
Figure PCTCN2021086342-appb-000012
直方图是实际分布，连续曲线是通用分布模型效果。如图2所示为本申请实施例第4个数据箱的概率分布直方图，图3为本申请实施例对第4个箱内的“功率比值”进行拟合后的效果图。
步骤6，根据待预测负荷的当前功率值和预测日的温度，基于步骤5拟合得到的通用分布函数，确定待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
求取一定置信水平(以95％为例)下功率比值的最大值与最小值，以及给定气象下的功率预测。假定已知当前的功率值为10，可以计算得出95％置信水平下明天的功率最小值与最大值如下表2所示:
表2
Figure PCTCN2021086342-appb-000013
直方图是实际分布，连续曲线是通用分布模型效果。如图2所示为本申请实施例第4个数据箱的概率分布直方图，图3为本申请实施例对第4个箱内的“功率比值”进行拟合后的效果图。
最后应当说明的是：以上实施例仅用以说明本发明的技术方案而非对其限制，尽管参照上述实施例对本发明进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本发明的具体实施方式进行修改或者等同替换，而未脱离本发明精神和范围的任何修改或者等同替换，其均应涵盖在本发明的权利要求保护范围之内。

Claims (13)

  1. 一种基于通用分布的负荷预测方法，其特征在于：将历史气象信息数据和电气数据相结合，针对不同的温度增量水平分别构建相应的统计分析模型，从而计算得到设定置信水平下预测功率的置信区间。
  2. 一种基于通用分布的负荷预测方法，其特征在于，所述负荷预测方法包括以下步骤：
    步骤1，采集设定时间段内待预测负荷的分时刻负荷功率及温度的历史数据；
    步骤2，对步骤1所采集负荷功率以及温度历史数据进行处理，计算得到包含温度增量和功率比值的数组，其中，所述温度增量是指每个时间点同一时刻后一天温度减前一天温度所得的温度增量，所述功率比值是指每个时间点同一时刻后一天的功率与前一天的功率的比值；
    步骤3，对步骤2获得的包含温度增量和功率比值的数组进行分箱处理，即将所有数组分别存入其对应的温度增量等级数据箱中；
    步骤4，建立基于直方图的各等级数据箱中负荷功率实际分布模型；
    步骤5，采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；
    步骤6，根据待预测负荷的当前功率值和预测日的温度，基于步骤5拟合得到的通用分布函数，确定待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
  3. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤1中，采集待预测负荷在最近三个月内的全天24小时分时刻的负荷功率以及对应时刻的温度数据；其中，在历史数据中，负荷功率的采样周期为1min，即对应每个负荷功率数据点为1min时刻的有功功率；温度数据的采样周期为5min，即对应每个温度数据点为5min时刻的温度。
  4. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤2中，进一步包括以下内容：
    步骤2.1，对于步骤1所采集的历史数据，分别建立负荷功率时间序列和温度时间序列，其中，负荷功率时间序列为每1min一个时段的时间序列；温度时间序列为每5min一个时段的时间序列；
    步骤2.2，对负荷功率时间序列进行降频处理，每隔5min取一个点，将功率与温度的时间序列统一为5min一个时段；
    步骤2.3，求出每个时间点同一时刻后一天温度减前一天温度所得的温度增量以及对应的后一天的功率与前一天的功率所得的功率比值；从而得到温度增量序列与功率比值序列，二者对应起来形成数组，数组第一列为温度增量，第二列为功率比值。
  5. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤3中，具体包括以下内容：
    步骤3.1，对温度增量进行等级划分，按照温度增量划分为R个等级，即R个数据箱，所述R个等级是指在温度增量最大值和最小值之间平均划分的R个等级；
    步骤3.2，根据每个历史数据数组中的温度增量的大小，将步骤2计算得到的所有数组分别存入其对应的温度增量等级数据箱中，完成数组的分箱过程。
  6. 根据权利要求5所述的负荷预测方法，其特征在于：
    所述R＝7。
  7. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤4中，根据各等级数据箱中的温度增量，对各数据箱分别绘制功率比值的概率密度直方图。
  8. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤4中，具体包括以下内容：
    步骤4.1，确定直方图的总宽度；将温度增量序列中的最大值减去最小值，得到差值，直方图的总宽度(即组距与组数的乘积)取大于这个差值的最小整数；
    步骤4.2，确定直方图的组数、组距和各组界限，假设直方图的组数为N；每组的宽度为1/N,即组距；
    步骤4.3，确定各组频数，把样本集合即分箱处理的所有数组中的功率比值数据按照大小划分到直方图各组，统计各组中数据的个数，即为频数；
    步骤4.4，绘制概率密度直方图，横坐标为功率比值，纵坐标为概率密度，直方图每组对应一个矩形，矩形的宽度为组距，高度为各组的概率密度，概率密度与频数的换算关系：概率密度＝频数/总样本数*组数。
  9. 根据权利要求8所述的负荷预测方法，其特征在于：
    直方图的组数N＝50。
  10. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤5中，通用分布定义如下：
    若连续型随机变量X服从一个形状参数为α、β和γ的通用分布，则记为：
    X～V(α,β,γ)
    其中，形状参数α、β和γ分别满足：
    α＞0,β＞0,-∞＜γ＜+∞
    通用分布的概率密度函数(Probability Density Function，PDF)f(x)表达式为：
    Figure PCTCN2021086342-appb-100001
    通用分布的累积分布函数(Cumulative Distribution Function，CDF)F(x)表达式为：
    F(x)＝(1+e -α(x-γ)) -β
    CDF的反函数F -1(c)表达式为：
    Figure PCTCN2021086342-appb-100002
    其中，f(x)为通用分布函数的概率密度，F(x)为通用分布函数的累计分布值。
  11. 根据权利要求2所述的负荷预测方法，其特征在于：
    在步骤6中，将待预测日各时间点的温度预报值，与当前日对应时间点的温度值作差得到温度增量序列，根据对应所属的不同温度增量数据箱，利用步骤5所确定的不同温度增量数据箱所拟合得到的通用分布函数形状参数值，计算通用分布CDF逆函数F -1(c)的表达式，从而获取设定置信水平下的负荷预测功率的置信区间：
    Figure PCTCN2021086342-appb-100003
    Figure PCTCN2021086342-appb-100004
    其中，w l,up和w l,low分别为负荷功率置信区间的上界和下界。
  12. 根据权利要求11所述的负荷预测方法，其特征在于：
    优选置信水平conf为0.95。
  13. 一种基于前述权利要求1-12任一项权利要求所述负荷预测方法的负荷预测***，包括历史数据库、数据采集及处理单元、数据分箱处理单元、直方图单元、拟合计算单元、负荷功率预测计算单元；其特征在于：
    所述数据采集及处理单元采集待预测负荷历史数据中的分时刻负荷功率及温度，并将负荷功率和温度采样值统一为相同的采样频率，建立包含温度增量序列与功率比值序列的数组；
    所述数据分箱处理单元对数据采集及处理单元所建立的数组基于温度增量进行分箱处理；
    所述直方图单元针对每一分箱数据绘制功率比值概率密度直方图，并计算直方图中各分组中的功率比值概率密度；
    所述拟合计算单元采用通用分布函数模型对各等级数据箱内数据进行拟合，得到每一等级数据箱所对应的通用分布函数；
    所述负荷功率预测计算单元根据输入的当前功率值和预测日的天气预报温度值，基于拟合计算单元拟合得到的各对应等级数据箱的通用分布函数，计算得到待预测负荷在预测日温度条件下的功率比值置信区间，实现给定气象下的功率预测。
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