WO2021082612A1 - 一种配电台区负荷预测方法及装置 - Google Patents

一种配电台区负荷预测方法及装置 Download PDF

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WO2021082612A1
WO2021082612A1 PCT/CN2020/108293 CN2020108293W WO2021082612A1 WO 2021082612 A1 WO2021082612 A1 WO 2021082612A1 CN 2020108293 W CN2020108293 W CN 2020108293W WO 2021082612 A1 WO2021082612 A1 WO 2021082612A1
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load
value
time period
set time
factor
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French (fr)
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庞杰锋
魏勇
李俊刚
史宏光
孟乐
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许昌许继软件技术有限公司
许继电气股份有限公司
许继集团有限公司
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    • 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 application relates to a method and device for predicting load in a distribution station area, and belongs to the technical field of power systems.
  • Electric load forecasting is a series of forecasting work for electric load. It is based on the development of past and present load, as well as the development and planning of social economy in the past, present and future, to make the future electric load level, occurrence time, location, etc. A scientific and reasonable speculation is made.
  • power load forecasting includes the forecast of future electricity demand, the forecast of future electricity consumption, and the forecast of load curve.
  • the forecast of the load curve is mainly based on historical load data and environmental data. For example, in a scheme, based on the minimum absolute contraction operator to fit the temperature value corresponding to the load value at different times, based on the temperature to the load fitting Degree requirements, divide all 24 hours into critical moments and non-critical moments.
  • the purpose of this application is to provide a load forecasting method and device for the distribution station area, so as to solve the problems of complexity in the current load forecasting process and low accuracy of forecasting results.
  • an embodiment of the present application provides a load forecasting method for a distribution station area, including:
  • the historical load data includes the absolute time of the historical load data measurement point, the date type of the measurement point, and the actual load value.
  • the relevant factor data includes the actual temperature value and Length of power outage;
  • the forecasting model including historical load data, related factor data, date type weights, temperature factor influence weights, and power outage duration influence weights;
  • the embodiment of the present application also provides a load forecasting device for a distribution station area.
  • the load forecasting device includes a memory and a processor, and a computer program stored in the memory and running on the processor.
  • the processor Coupled with the memory, when the processor executes the computer program, the foregoing method for predicting the load of the distribution station area is realized.
  • the historical load data and related factor data of the set time period of the distribution station area are first obtained, and then the historical load data, related factor data, date type weight, temperature factor influence weight and The power outage duration affects the prediction model of the weight, and finally the future load value is predicted based on the model.
  • the embodiment of the application not only considers the influence of temperature and date type, but also the influence of power outage duration.
  • the established prediction model can more accurately describe the relationship between load and various factors, improve the prediction accuracy, and the solution does not need to be complicated.
  • the data processing is simple and easy to implement.
  • the prediction model is:
  • Y(t) is the predicted load value
  • X 1 (t) is The square root value of the difference between the average load value of the working day in the set time period
  • X 2 (t) is The square root value of the difference between the average load value of the holiday in the set time period
  • X 3 (t) is The square root value of the difference with the average load value whose temperature change does not exceed the set threshold in the set time period
  • X 4 (t) is The square root value of the difference between the average load value with no power outage during the set time period
  • a 1 is the weight of the working day factor
  • a 2 is the weight of the holiday factor
  • a 3 is the weight of the temperature factor
  • a 4 is the off The weight of the electricity duration factor.
  • the prediction model is:
  • Y(t) is the predicted load value
  • X 1 (t) is The square root value of the difference between the average load value of the working day in the set time period
  • X 1 (t) is The square root value of the difference with the average load value of non-working days in the set time period
  • X 2 (t) is The square root value of the difference between the average load value of the holiday in the set time period
  • X 2 (t) is The square root value of the difference with the average load value of non-holidays in the set time period
  • X 3 (t) is The square root value of the difference between the average load value whose temperature change does not exceed the set threshold in the set time period
  • X 4 (t) is The square root value of the difference between the average load value with no power outage during
  • a specific method for determining the weight of the temperature factor is also given.
  • the weight of the temperature factor is determined according to the temperature change within a set time period. When the temperature change within the set time period When it is not greater than the set threshold, the weight of the temperature factor is 0.02, otherwise the weight of the temperature factor is 0.025.
  • a specific method for determining the weight of the power outage duration is also given.
  • the weight of the power outage duration factor is determined according to whether there is a power outage within a set time period. When a power outage occurs within the set time period, The weight of the power outage duration factor is 0.01. If there is no power outage within the set time period, the weight of the power outage duration factor is 0.02.
  • the set time period is one week.
  • the measurement point date types include working days, non-working days, holidays and non-holidays.
  • FIG. 1 is a schematic diagram of the implementation of the method for predicting the load in a distribution station area provided by an embodiment of the present application;
  • Fig. 2 is a schematic diagram of the structural composition of a load forecasting device for a distribution station area provided by an embodiment of the present application.
  • the prediction method of the embodiment of the present application first obtains historical load data and related factor data for a set time period in the distribution station area.
  • the historical load data includes the absolute time of the historical load data measurement point, the date type of the measurement point, and the actual load value.
  • the factor data includes the actual temperature value and the duration of power outage; then a forecast model is established based on the obtained historical load data.
  • the forecast model includes historical load data, related factor data, date type weights, temperature factor influence weights and power outage duration influence weights Value; finally predict the load data after the set time period according to the established forecasting model, and obtain the predicted load value of each measurement point consistent with the historical load data measurement point.
  • the implementation principle of this method is shown in FIG. 1.
  • the specific implementation process of the prediction method in the embodiment of the present application will be described in detail below by taking the data of the past week as an example.
  • historical load data and related factor data for 7 days in the past week are acquired.
  • the historical load data includes multiple measurement point data. In this embodiment, there is one measurement point every 15 minutes, and there are 96 measurement points in one day, seven days a week. There are 672 measuring points in total, and the setting of measuring points can be flexibly changed according to actual needs.
  • the historical load data of each measuring point includes the serial number of the measuring point, the absolute time of the measuring point, the date type of the measuring point, and the actual load value of the measuring point, as shown in Table 1.
  • Relevant factor data includes actual temperature data and power outage duration data. Among them, the actual temperature value of each day in the past week and the power outage duration of each day in the past week, the actual temperature value of each day The highest temperature, lowest temperature and average temperature of each day are shown in Table 2. Show.
  • the power outage duration data includes the power outage duration of each day in the past week, as shown in Table 3.
  • a prediction model is established based on the acquired historical load data and related factor data.
  • the prediction model established in this embodiment is:
  • Y(t) is the predicted load value
  • X 1 (t) is The square root value of the difference between the average load value of the working day in the set time period
  • X 2 (t) is The square root value of the difference between the average load value of the holiday in the set time period
  • X 3 (t) is The square root value of the difference with the average load value whose temperature change does not exceed the set threshold in the set time period
  • X 4 (t) is The square root value of the difference between the average load value without power failure during the set time period
  • a 3 is the temperature factor weight, based on the average temperature change of the
  • a 1 and a 2 are set values according to the probabilities of working days and holidays, and for the forecast day, the date type is fixed. For example, if the forecast day is a specific day, then that day Whether it is a working day or a holiday is clear. It is not necessary to use a 1 and a 2 to describe the corresponding probability. For this reason, this application also provides another prediction model:
  • X 1 (t) is The square root value of the difference between the average load value of the working day in the set time period; when the forecast day is a non-working day, X 1 (t) is The square root value of the difference with the average load value of non-working days in the set time period; when the forecast day is a holiday, X 2 (t) is The square root value of the difference between the average load value of the holiday in the set time period; when the forecast day is a non-holiday, X 2 (t) is The square root value of the difference with the average load value of non-holidays in the set time period.
  • the forecast objects of the above two models are measurement points.
  • the forecast model established above can measure the load value of each measurement point on the forecast day or forecast week, and the load value of each measurement point on the forecast day and the daily measurement point in the historical load data can be measured.
  • the absolute time is the same.
  • the above prediction model can be used to predict the load of one day or several days in the future. Taking the future day as an example, the load prediction value of 96 measuring points in the future can be obtained through the above model.
  • the load forecasting device for a distribution station area of the present application includes a memory 201 and a processor 202, and is stored on the memory 201 and on the processor.
  • a running computer program the processor 202 is coupled with the memory 201, and the processor 202 implements the distribution station area load forecasting method of the present application when the processor 202 executes the computer program.
  • the specific implementation process of the method has been described in detail in the method embodiment. I won't repeat it here.
  • the technical solution of the embodiment of the application not only considers the influence of temperature and date type, but also the influence of power outage duration.
  • the established prediction model can more accurately describe the relationship between load and various factors, improve the prediction accuracy, and This scheme does not require complex data processing, and is simple and easy to implement.
  • this application realizes the accurate forecast of the load in the distribution station area, and can economically and reasonably adjust the operation mode, reduce the standby capacity of the superior power station, arrange the maintenance plan, reduce the operating cost, and improve the economic efficiency; at the same time, the load forecast result is electricity
  • the plan provides a basis for the capacity expansion and reconstruction of the power grid.

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Abstract

一种配电台区负荷预测方法及装置，属于电力系统技术领域。首先获取配电台区设定时间段的历史负荷数据和相关因素数据，然后建立包括历史负荷数据、相关因素数据、日期类型权值、温度因素影响权值和断电时长影响权值的预测模型，最后根据该模型对未来的负荷值进行预测。该方法不仅考虑了温度和日期类型的影响，还考虑了断电时长的影响，所建立的预测模型能够更加精准地描述负荷与各因素的关系，提高了预测精度，且该方案无需复杂的数据处理，简单、容易实现。

Description

一种配电台区负荷预测方法及装置
相关申请的交叉引用
本申请基于申请号为201911031980.0、申请日为2019年10月28日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及一种配电台区负荷预测方法及装置，属于电力系统技术领域。
背景技术
电力负荷预测是以电力负荷为对象进行的一系列预测工作，是根据过去和现在负荷的发展，以及过去、现在和将来社会经济的发展、规划，对未来电力负荷水平、出现时间、地点等做出科学合理的推测，从预测对象来看，电力负荷预测包括对未来电力需求量的预测和对未来用电量的预测以及对负荷曲线的预测。而目前对负荷曲线的预测主要是基于历史负荷数据和环境数据，例如在一种方案中，基于最小绝对收缩算子对不同时刻的温度值对应负荷值进行拟合，基于温度对于负荷的拟合度要求，将24小时的全部时刻划分为关键时刻与非关键时刻，给定关键时刻与非关键时刻不同权重，计算历史温度数据与预测日温度数据间加权欧式距离，构建相似日集，基于相似日集中负荷数据及温度因素数据，构建差分自回归移动平均模型，结合预测日当天的温度数据，以及前两日的负荷数据进行负荷预测。该方案虽然能够实现负荷预测，但是该方案实现过程复杂，且仅考虑历史负荷和温度，考虑因素不全面，导致预测结果不准确。
发明内容
本申请的目的是提供一种配电台区负荷预测方法及装置，以解决目前负荷预测过程存在复杂、预测结果准确性低的问题。
为解决上述技术问题，本申请实施例提供一种配电台区负荷预测方法，包括：
获取配电台区设定时间段的历史负荷数据和相关因素数据，所述历史负荷数据包括历史负荷数据测量点的绝对时间、测量点日期类型和实际负荷值，相关因素数据包括实际温度值和断电时长；
根据获取的负荷历史数据建立预测模型，该预测模型包括历史负荷数据、相关因素数据、日期类型权值、温度因素影响权值和断电时长影响权值；
根据所建立的预测模型预测对设定时间段后的负荷数据进行预测，得到与历史负荷数据测量点一致的各测量点的预测负荷值。
本申请实施例还提供了一种配电台区负荷预测装置，该负荷预测装置包括存储器和处理器，以及存储在所述存储器上并在所述处理器上运行的计算机程序，所述处理器与所述存储器相耦合，所述处理器执行所述计算机程序时实现上述的配电台区负荷预测方法。
本申请实施例的技术方案中，首先获取配电台区设定时间段的历史负荷数据和相关因素数据，然后建立包括历史负荷数据、相关因素数据、日期类型权值、温度因素影响权值和断电时长影响权值的预测模型，最后根据该模型对未来的负荷值进行预测。本申请实施例不仅考虑了温度和日期类型的影响，还考虑了断电时长的影响，所建立的预测模型能够更加精准地描述负荷与各因素的关系，提高了预测精度，且该方案无需复杂的数据处理，简单、容易实现。
在本申请一可选方式中，为提高模型的准确性，所述的预测模型为：
Figure PCTCN2020108293-appb-000001
其中Y(t)为预测负荷值，
Figure PCTCN2020108293-appb-000002
为设定时间段中同一测量点的负荷值的均值，X 1(t)为
Figure PCTCN2020108293-appb-000003
与设定时间段中工作日的平均负荷值的差值的平方根值，X 2(t)为
Figure PCTCN2020108293-appb-000004
与设定时间段中节假日的平均负荷值的差值的平方根值，X 3(t)为
Figure PCTCN2020108293-appb-000005
与设定时间段中温度变化没有超过设定阈值的平均负荷值的差值的平方根值，X 4(t)为
Figure PCTCN2020108293-appb-000006
与设定时间段中没有发生断电的平均负荷值的差值的平方根值，a 1为工作日因素权值，a 2为节假日因素权值，a 3为温度因素权值，a 4为断电时长因素权值。
在本申请一可选方式中，为提高模型的准确性，所述的预测模型为：
Figure PCTCN2020108293-appb-000007
其中Y(t)为预测负荷值；
Figure PCTCN2020108293-appb-000008
为设定时间段中同一测量点的负荷值的均值，当预测日为工作日时，X 1(t)为
Figure PCTCN2020108293-appb-000009
与设定时间段中工作日的平均负荷值的差值的平方根值；当预测日为非工作日时，X 1(t)为
Figure PCTCN2020108293-appb-000010
与设定时间段中非工作日的平均负荷值的差值的平方根值；当预测日为节假日时，X 2(t)为
Figure PCTCN2020108293-appb-000011
与设定时间段中节假日的平均负荷值的差值的平方根值；当预测日为非节假日时，X 2(t)为
Figure PCTCN2020108293-appb-000012
与设定时间段中非节假日的平均负荷值的差值的平方根值；X 3(t)为
Figure PCTCN2020108293-appb-000013
与设定时间段中温度变化没有超过设定阈值的平均负荷值的差值的平方根值；X 4(t)为
Figure PCTCN2020108293-appb-000014
与设定时间段中没有发生断电的平均负荷值的差值的平方根值；a 3为温度因素权值；a 4为断电时长因素权值。
在本申请一可选方式中，还给出了具体的温度因素权值确定方式，所述温度因素权值是根据设定时间段内的温度变化高低确定，当设定时间段内的温度变化不大于设定阈值时，温度因素权值为0.02，否则温度因素权值为0.025。
在本申请一可选方式中，还给出了具体的停电时长权值确定方式，所 述停电时长因素权值是根据设定时间段内是否停电确定的，当设定时间段内发生停电，则停电时长因素权值为0.01，若设定时间段内未发生停电，则停电时长因素权值为0.02。
在本申请一可选方式中，所述的设定时间段为一周时间。
在本申请一可选方式中，所述的测量点日期类型包括工作日、非工作日、节假日和非节假日。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
图1是本申请实施例提供的配电台区负荷预测方法的实现原理图；
图2是本申请实施例提供的配电台区负荷预测装置的结构组成示意图。
具体实施方式
下面结合附图对本申请的具体实施方式作进一步地说明。
预测方法的实施例
本申请实施例的预测方法首先获取配电台区设定时间段的历史负荷数据和相关因素数据，其中历史负荷数据包括历史负荷数据测量点的绝对时间、测量点日期类型和实际负荷值，相关因素数据包括实际温度值和断电时长；然后根据获取的负荷历史数据建立预测模型，该预测模型包括历史负荷数据、相关因素数据、日期类型权值、温度因素影响权值和断电时长影响权值；最后根据所建立的预测模型预测对设定时间段后的负荷数据进行预测，得到与历史负荷数据测量点一致的各测量点的预测负荷值。该方法实现原理如图1所示，下面以过去一周时间的数据为例对本申请实施例的预测方法的具体实现过程进行详细说明。
1.获取配电台区设定时间段的历史负荷数据和相关因素数据。
本实施例中获取的是过去一周7天的历史负荷数据和相关因素数据，历史负荷数据包括多个测量点数据，本实施例每隔15分钟一个测量点，一天共有96个测量点，一周七天共有672个测量点数据，测量点的设置可根据实际需求灵活变化。每个测量点的历史负荷数据包括测量点的序号、测量点的绝对时间、测量点的日期类型和测量点的实际负荷值，如表1所示。
表1
Figure PCTCN2020108293-appb-000015
相关因素数据包括实际温度数据和断电时长数据，其中过去一周每天的实际温度值和过去一周每天的断电时长，每天的实际温度值每天的最高温度、最低温度和平均温度，如表2所示。断电时长数据包括过去一周每天的断电时长，如表3所示。
表2
Figure PCTCN2020108293-appb-000016
表3
Figure PCTCN2020108293-appb-000017
Figure PCTCN2020108293-appb-000018
2.建立预测模型。
根据获取的历史负荷数据和相关因素数据建立预测模型，本实施例建立的预测模型为：
Figure PCTCN2020108293-appb-000019
其中Y(t)为预测负荷值，
Figure PCTCN2020108293-appb-000020
为设定时间段中同一测量点(绝对时间相同)的负荷值的均值，X 1(t)为
Figure PCTCN2020108293-appb-000021
与设定时间段中工作日的平均负荷值的差值的平方根值，X 2(t)为
Figure PCTCN2020108293-appb-000022
与设定时间段中节假日的平均负荷值的差值的平方根值，X 3(t)为
Figure PCTCN2020108293-appb-000023
与设定时间段中温度变化没有超过设定阈值的平均负荷值的差值的平方根值，X 4(t)为
Figure PCTCN2020108293-appb-000024
与设定时间段中没有发生断电的平均负荷值的差值的平方根值，a 1为工作日因素权值，根据工作日、非工作日概率，默认取0.7*0.97＝0.679，a 2为节假日因素权值，根据节假日、非节假日概率，默认取0.3*0.97＝0.291，a 3为温度因素权值，依据前一周平均温度变化高低，如果温度变化基本保持不变，默认取值0.02，如果温度持续上升或者持续下降，默认取值0.025；a 4为断电时长因素权值，根据前一周特殊事件断电事件，如果上周没有断电，默认修正系数取值0.01，0.01为小概率事件波动概率，作为修正系数，正常情况a 1＝0.679，a 2＝0.291，a 3＝0.02，a 4＝0.01，刚好为1，波动时系数可能超过1，不过都是小的修正，无影响；如果上周发生过断电事件，默认修正系数相关性增加，取值为0.02。
上述模型中a 1和a 2为按照工作日、节假日概率的一个设定值，而对于预测日而言，其所在日期类型是固定的，例如，若预测日为具体某一天时， 则该日是否为工作日，是否为节假日是明确的，没必要用a 1和a 2来描述其对应的概率，为此，本申请还提供了另一预测模型：
Figure PCTCN2020108293-appb-000025
与上个模型相比，X 1(t)和X 2(t)的含义有所变化，当预测日为工作日时，X 1(t)为
Figure PCTCN2020108293-appb-000026
与设定时间段中工作日的平均负荷值的差值的平方根值；当预测日为非工作日时，X 1(t)为
Figure PCTCN2020108293-appb-000027
与设定时间段中非工作日的平均负荷值的差值的平方根值；当预测日为节假日时，X 2(t)为
Figure PCTCN2020108293-appb-000028
与设定时间段中节假日的平均负荷值的差值的平方根值；当预测日为非节假日时，X 2(t)为
Figure PCTCN2020108293-appb-000029
与设定时间段中非节假日的平均负荷值的差值的平方根值。
上述两个模型针对的预测对象是测量点，通过上述建立的预测模型可以测到预测日或预测周各测量点的负荷值，且预测日的各测量点与历史负荷数据中每天的测量点的绝对时间相同。
3.根据预测模型进行预测。
利用上述预测模型可对未来一天或若干天的负荷进行预测，以未来一天为例，通过上述模型可得到未来一天96个测量点的负荷预测值。
装置实施例
图2是本申请实施例提供的配电台区负荷预测装置的结构组成示意图，本申请的配电台区负荷预测装置包括存储器201和处理器202，以及存储在存储器201上并在处理器上运行的计算机程序，处理器202与存储器201相耦合，处理器202执行计算机程序时实现本申请的配电台区负荷预测方法，其中方法具体实现过程已在方法的实施例中进行了详细说明，这里不再赘述。
本申请实施例的技术方案不仅考虑了温度和日期类型的影响，还考虑了断电时长的影响，所建立的预测模型能够更加精准地描述了负荷与各因素的关系，提高了预测精度，且该方案无需复杂的数据处理，简单、容易 实现。通过上述过程，本申请实现了配电台区的负荷的准确预测，可以经济合理地调整运行方式，减少上级电站备用容量，安排检修计划、降低运营成本，提高经济效益；同时负荷预测结果为电力规划提供依据，为电网的增容和改建提供依据。

Claims (8)

  1. 一种配电台区负荷预测方法，包括：
    获取配电台区设定时间段的历史负荷数据和相关因素数据，所述历史负荷数据包括历史负荷数据测量点的绝对时间、测量点日期类型和实际负荷值，相关因素数据包括实际温度值和断电时长；
    根据获取的负荷历史数据建立预测模型，该预测模型包括历史负荷数据、相关因素数据、日期类型权值、温度因素影响权值和断电时长影响权值；
    根据所建立的预测模型预测对设定时间段后的负荷数据进行预测，得到与历史负荷数据测量点一致的各测量点的预测负荷值。
  2. 根据权利要求1所述的配电台区负荷预测方法，其中，所述的预测模型为：
    Figure PCTCN2020108293-appb-100001
    其中Y(t)为预测负荷值，
    Figure PCTCN2020108293-appb-100002
    为设定时间段中同一测量点的负荷值的均值，X 1(t)为
    Figure PCTCN2020108293-appb-100003
    与设定时间段中工作日的平均负荷值的差值的平方根值，X 2(t)为
    Figure PCTCN2020108293-appb-100004
    与设定时间段中节假日的平均负荷值的差值的平方根值，X 3(t)为
    Figure PCTCN2020108293-appb-100005
    与设定时间段中温度变化没有超过设定阈值的平均负荷值的差值的平方根值，X 4(t)为
    Figure PCTCN2020108293-appb-100006
    与设定时间段中没有发生断电的平均负荷值的差值的平方根值，a 1为工作日因素权值，a 2为节假日因素权值，a 3为温度因素权值，a 4为断电时长因素权值。
  3. 根据权利要求1所述的配电台区负荷预测方法，其中，所述的预测模型为：
    Figure PCTCN2020108293-appb-100007
    其中Y(t)为预测负荷值；
    Figure PCTCN2020108293-appb-100008
    为设定时间段中同一测量点的负荷值的 均值，当预测日为工作日时，X 1(t)为
    Figure PCTCN2020108293-appb-100009
    与设定时间段中工作日的平均负荷值的差值的平方根值；当预测日为非工作日时，X 1(t)为
    Figure PCTCN2020108293-appb-100010
    与设定时间段中非工作日的平均负荷值的差值的平方根值；当预测日为节假日时，X 2(t)为
    Figure PCTCN2020108293-appb-100011
    与设定时间段中节假日的平均负荷值的差值的平方根值；当预测日为非节假日时，X 2(t)为
    Figure PCTCN2020108293-appb-100012
    与设定时间段中非节假日的平均负荷值的差值的平方根值；X 3(t)为
    Figure PCTCN2020108293-appb-100013
    与设定时间段中温度变化没有超过设定阈值的平均负荷值的差值的平方根值；X 4(t)为
    Figure PCTCN2020108293-appb-100014
    与设定时间段中没有发生断电的平均负荷值的差值的平方根值；a 3为温度因素权值；a 4为断电时长因素权值。
  4. 根据权利要求2或3所述的配电台区负荷预测方法，其中，所述温度因素权值是根据设定时间段内的温度变化高低确定，当设定时间段内的温度变化不大于设定阈值时，温度因素权值为0.02，否则温度因素权值为0.025。
  5. 根据权利要求2或3所述的配电台区负荷预测方法，其中，所述停电时长因素权值是根据设定时间段内是否停电确定的，当设定时间段内发生停电，则停电时长因素权值为0.01，若设定时间段内未发生停电，则停电时长因素权值为0.02。
  6. 根据权利要求1所述的配电台区负荷预测方法，其中，所述的设定时间段为一周时间。
  7. 根据权利要求1所述的配电台区负荷预测方法，其中，所述的测量点日期类型包括工作日、非工作日、节假日和非节假日。
  8. 一种配电台区负荷预测装置，该负荷预测装置包括存储器和处理器，以及存储在所述存储器上并在所述处理器上运行的计算机程序，所述处理器与所述存储器相耦合，所述处理器执行所述计算机程序时实现如权利要求1-7中任一项所述的配电台区负荷预测方法。
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