CN113780769A - Population mobility index calculation method based on electric power big data - Google Patents

Population mobility index calculation method based on electric power big data Download PDF

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CN113780769A
CN113780769A CN202110989951.6A CN202110989951A CN113780769A CN 113780769 A CN113780769 A CN 113780769A CN 202110989951 A CN202110989951 A CN 202110989951A CN 113780769 A CN113780769 A CN 113780769A
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唐泽洋
崔一铂
万磊
杨帆
黄杰
余飞
张科
靳经
桑田
孙秉宇
余鹤
饶玮
王文烁
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HUBEI ELECTRIC POWER Co JINGZHOU POWER SUPPLY Co
State Grid Hubei Electric Power Co Ltd
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Jingzhou Power Supply Co of State Grid Hubei Electric Power Co Ltd
Metering Center of State Grid Hubei Electric Power Co Ltd
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HUBEI ELECTRIC POWER Co JINGZHOU POWER SUPPLY Co
State Grid Hubei Electric Power Co Ltd
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Metering Center of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention provides a population mobility index calculation method based on electric power big data, which comprises the following steps of: A. acquiring temperature and daily electricity consumption data of a user; B. dividing air temperature intervals according to the air temperature data obtained in the step A; C. calculating daily average power consumption of the user in different temperature intervals according to the temperature intervals divided in the step B and the daily power consumption data of the user obtained in the step A; D. and D, judging the leaving and returning states according to the daily average electricity consumption of the user in different temperature intervals calculated in the step C, and further calculating the population flow index. According to the population flow index calculation method based on the electric power big data, the influence of the temperature on the daily power consumption is considered, the daily average power consumption of each user under different temperature conditions is calculated, and the accuracy of population index calculation can be improved due to the fact that the difference of individual users is considered.

Description

Population mobility index calculation method based on electric power big data
Technical Field
The invention relates to the technical field of electric power big data application, in particular to a population mobility index calculation method based on electric power big data.
Background
Population mobility refers to the wide variety of short-term, repetitive, or periodic movements of the population between regions. The population mobility index can accurately reflect the mobility tendency of regional personnel and provide data basis for epidemic prevention and control, personnel management and planning.
The invention relates to a method and a system for monitoring floating population, which is disclosed in the Chinese patent application No. 201710418520.8, wherein the method comprises the steps of acquiring face information, a living address and mobile terminal information of a resident population or a temporary population as standard information, acquiring related information of people entering an entrance and an exit as sample information, and comparing the sample information with the standard information to monitor the floating condition of the floating population. The invention discloses a Chinese patent application No. 2015106114816.8, which is a floating population management method based on autonomous reporting and community monitoring, wherein floating personnel autonomous registration reporting, third-party organization assisted registration reporting and community resident or captain assisted reporting are adopted, after reporting is successful, monitoring reporting data is implemented through a management platform, and authenticity is verified in an offline verification mode. The method for calculating the vacancy rate of the residence based on the daily electricity consumption data of the residential user, which is applied for 202011293772.0, judges the vacancy rate of the residence through the daily electricity consumption data. The invention discloses a method and a terminal for determining population mobility and house vacancy rate, which are applied to a Chinese patent with the application number of 202120182373.5, wherein three clustering centers are obtained by acquiring a historical electricity consumption set of houses of each household in a preset area and performing clustering analysis on the historical electricity consumption, and the population mobility rate is determined according to an energy consumption label of the houses of each household.
In the existing method related to people flow monitoring or calculation, a patent with application number 201710418520.8 relies on obtaining information such as human faces through equipment, a patent with application number 2015106114816.8 mainly relies on active reporting of personnel through an offline mode, and the two methods do not relate to calculation of population flow indexes based on large electric power data. The patent with the application number of 202011293772.0 judges the vacancy rate through daily electricity consumption, and can be used for judging the population mobility condition, but a single threshold value is adopted, and the influence of temperature factors is not considered; the patent with the application number of 202120182373.5 relies on the big data of electric power to calculate the population flow index, which gathers the electricity consumption of all users into 3 types, does not consider the individual difference of the resident users, and does not consider the influence of temperature on the electricity consumption of the residents when calculating the population flow index.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the population flow index calculation method based on the electric power big data, which considers the influence of temperature on daily power consumption, calculates the daily average power consumption of each user under different temperature conditions, considers the difference of individual users and can improve the accuracy of population index calculation.
The technical scheme adopted by the invention is as follows:
a population mobility index calculation method based on electric power big data comprises the following steps:
A. acquiring temperature and daily electricity consumption data of a user;
B. dividing air temperature intervals according to the air temperature data obtained in the step A;
C. calculating daily average power consumption of the user in different temperature intervals according to the temperature intervals divided in the step B and the daily power consumption data of the user obtained in the step A;
D. and D, judging the leaving and returning states according to the daily average electricity consumption of the user in different temperature intervals calculated in the step C, and further calculating the population flow index.
Further, the step a of obtaining the temperature and the daily electricity consumption data of the user specifically includes:
assume that the time range of the analysis is t1,t2,…tnGet the daily maximum temperature from the weather website and record it as
Figure BDA0003232126000000031
Acquiring daily electricity quantity of the user from the electricity utilization information acquisition system, and recording the daily electricity quantity of the ith user as the daily electricity quantity of the ith user under the assumption that the user set is {1,2, … I }
Figure BDA0003232126000000032
Further, in the step B, according to the air temperature data obtained in the step a, an air temperature section is divided, specifically:
dividing the daily maximum air temperature into six sections according to the size of the daily maximum air temperature T, wherein the T-th sectionjDaily maximum temperature
Figure BDA0003232126000000033
If it is
Figure BDA0003232126000000034
Dividing the first interval into a first interval; if it is
Figure BDA0003232126000000035
Dividing the first interval into a second interval; if it is
Figure BDA0003232126000000036
Dividing the data into a third interval; if it is
Figure BDA0003232126000000037
Dividing the data into a fourth interval; if it is
Figure BDA0003232126000000038
Dividing the data into a fifth interval; if it is
Figure BDA0003232126000000039
It is drawn into the sixth interval.
Further, in the step C, the daily average power consumption of the user in different temperature intervals is calculated according to the temperature intervals divided in the step B and the daily power consumption data of the user obtained in the step a, and specifically:
for user i, according to daily electricity consumption
Figure BDA00032321260000000310
Calculating electricity consumption characteristic index
Figure BDA00032321260000000311
In particular, if
Figure BDA00032321260000000312
Then
Figure BDA00032321260000000313
Otherwise, the reverse is carried out
Figure BDA00032321260000000314
Calculating the daily average electric quantity of the user i in a first interval:
Figure BDA00032321260000000315
calculating the daily average electric quantity of the user i in a second interval:
Figure BDA00032321260000000316
and calculating the daily average electric quantity of the user i in the third interval:
Figure BDA0003232126000000041
calculating the daily average electric quantity of the user i in the fourth interval:
Figure BDA0003232126000000042
calculating the daily average electric quantity of the user i in a fifth interval:
Figure BDA0003232126000000043
calculating the daily average electric quantity of the user i in a sixth interval:
Figure BDA0003232126000000044
if all the daily electricity consumption of the user in the first interval is 0, AWi,1The processing mode is applicable to all the intervals, and the values are recorded as null values.
Further, in the step D, according to the daily average power consumption calculated in the step C, the leaving and returning states are judged, and then the population flow index is calculated, specifically:
taking the first interval as an example, the other interval methods are similar:
for user ithjThe daily dose is
Figure BDA0003232126000000045
The highest temperature in the day satisfies
Figure BDA0003232126000000046
1) If AWi,1Is not empty and
Figure BDA0003232126000000047
if the state of the previous day is not an outgoing state or a continuous outgoing state, judging that the state of the current day is an outgoing state; otherwise, judging the state of the day as a continuous outgoing state;
2) if AWi,1If the state is not empty, the state of the previous day is going out or going out continuously, if the state meets the requirement
Figure BDA0003232126000000048
The state of the day is judged to be a return state.
Specifically, the average daily power consumption in the first interval is used to determine whether the user is an idle user, that is: AWi,1Less than or equal to 0.5 or AWi,1If the value is null, the user i is considered as an empty user.
According to the above method, the analysis time range { t ] is calculated1,t2,…tnThe state of all days in the page, the sum of the states is calculated, wherein the sum of the number of times of the outgoing state of the user i is recorded as WCiThe sum of the number of home return times is FJi，InullThe total number of the vacant users.
The user is collected as{1,2, … I } in time horizon { t1,t2,…tnThe population outflow index of } is:
Figure BDA0003232126000000051
set of users is {1, 2.. I } in time range { t1,t2,...tnThe population inflow index of } is:
Figure BDA0003232126000000052
the invention provides a population flow index calculation method based on electric power big data, which considers the influence of air temperature on population flow index calculation, wherein the air temperature has obvious influence on the daily electricity consumption of residents, if the temperature is high in summer, the residents turn on an air conditioner for refrigeration, the electricity consumption is higher, if the temperature is changed and reduced in summer, and the residents do not turn on the air conditioner, the electricity consumption is suddenly reduced when the temperature is higher than the temperature, and when the electricity consumption is calculated by using a traditional method, the influence of the air temperature is not considered, so that the residents can be judged to go out by mistake; similarly, when the temperature is lower in winter, due to the existence of the heating load, misjudgment can be caused when the influence of the air temperature is not considered. According to the method, the daily average power consumption of each user under different temperature conditions is calculated, the influence of the temperature on the daily power consumption is considered, the accuracy of population index calculation can be improved due to the fact that the difference of individual users is considered, and the effectiveness of the method is verified through actual data.
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Fig. 1 is a schematic flow chart of a population mobility index calculation method based on power big data according to an embodiment of the invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings.
Referring to fig. 1, a schematic flow chart of an embodiment of a method for calculating a population mobility index based on power big data according to the present invention is shown, the method includes the following steps:
A. acquiring temperature and daily electric quantity data of a user, specifically:
assume that the time range of the analysis is t1,t2,…tnGet the daily maximum temperature from the weather website and record it as
Figure BDA0003232126000000061
Acquiring daily electricity quantity of the user from the electricity utilization information acquisition system, and recording the daily electricity quantity of the ith user as the daily electricity quantity of the ith user under the assumption that the user set is {1,2, … I }
Figure BDA0003232126000000062
B. Dividing an air temperature interval according to the air temperature data obtained in the step A, and specifically:
dividing the daily maximum air temperature into six sections according to the size of the daily maximum air temperature T, wherein the T-th sectionjDaily maximum temperature
Figure BDA0003232126000000063
If it is
Figure BDA0003232126000000064
Dividing the first interval into a first interval; if it is
Figure BDA0003232126000000065
Dividing the first interval into a second interval; if it is
Figure BDA0003232126000000066
Dividing the data into a third interval; if it is
Figure BDA0003232126000000067
Dividing the data into a fourth interval; if it is
Figure BDA0003232126000000068
Dividing the data into a fifth interval; if it is
Figure BDA0003232126000000069
It is drawn into the sixth interval.
C. Calculating daily average power consumption of the user in different temperature intervals according to the temperature intervals divided in the step B and the daily power consumption data of the user acquired in the step A, and specifically:
the method specifically comprises the following steps:
for user i, according to daily electricity consumption
Figure BDA00032321260000000610
Calculating electricity consumption characteristic index
Figure BDA00032321260000000611
In particular, if
Figure BDA00032321260000000612
Then
Figure BDA00032321260000000613
Otherwise, the reverse is carried out
Figure BDA00032321260000000614
Calculating the daily average electric quantity of the user i in a first interval:
Figure BDA00032321260000000615
calculating the daily average electric quantity of the user i in a second interval:
Figure BDA0003232126000000071
and calculating the daily average electric quantity of the user i in the third interval:
Figure BDA0003232126000000072
calculating the daily average electric quantity of the user i in the fourth interval:
Figure BDA0003232126000000073
calculating the daily average electric quantity of the user i in a fifth interval:
Figure BDA0003232126000000074
calculating the daily average electric quantity of the user i in a sixth interval:
Figure BDA0003232126000000075
specially, if all the daily electricity consumption of the user in the first interval is 0, AWi,1The processing mode is applicable to all the intervals, and the values are recorded as null values.
D. And C, judging the leaving and returning states according to the daily average electricity consumption of the user in different temperature intervals calculated in the step C, and further calculating a population flow index, wherein the specific steps are as follows:
taking the first interval as an example, the other interval methods are similar:
for user ithjThe daily dose is
Figure BDA0003232126000000076
The highest temperature in the day satisfies
Figure BDA0003232126000000077
1) If AWi,1Is not empty and
Figure BDA0003232126000000078
if the state of the previous day is not an outgoing state or a continuous outgoing state, judging that the state of the current day is an outgoing state; otherwise, judging the state of the day as a continuous outgoing state;
2) if AWi,1If the state is not empty, the state of the previous day is going out or going out continuously, if the state meets the requirement
Figure BDA0003232126000000079
The state of the day is judged to be a return state.
Specifically, the average daily power consumption in the first interval is used to determine whether the user is an idle user, that is: AWi,1Less than or equal to 0.5 or AWi,1If the value is null, the user i is considered as an empty user.
According to the above method, the analysis time range { t ] is calculated1,t2,tnThe state of all days in the page, the sum of the states is calculated, wherein the sum of the number of times of the outgoing state of the user i is recorded as WCiThe sum of the number of home return times is FJi，InullThe total number of the vacant users.
Then the set of users is {1,2, … I } in the time range { t }1,t2,…tnThe population outflow index of } is:
Figure BDA0003232126000000081
set of users as {1,2, … I } in time range { t1,t2,…tnThe population inflow index of } is:
Figure BDA0003232126000000082
the technical scheme and effect of the invention are explained in detail by a specific embodiment as follows:
step a, obtaining data of air temperature and daily electricity consumption of the user, as shown in table 1 below as example data (taking user i as an example).
TABLE 1
Date Daily maximum temperature/. degree.C Daily electricity/kWh
2020/8/1 36 8.63
2020/8/2 36 4.45
2020/8/3 34 9.4
2020/8/4 37 9.91
2020/8/5 37 7.92
2020/8/6 34 9.69
2020/8/7 34 9.37
2020/8/8 34 10.22
2020/8/9 34 6.4
2020/8/10 32 9.08
2020/8/11 33 9.74
And step B, dividing the air temperature into the following six intervals according to the daily maximum air temperature:
[30℃，+∞)，[25℃，30℃)，[15℃，25℃)，[10℃，15℃)，[5℃，10℃)，(-∞，5℃)。
and C, calculating daily average electricity consumption of different temperature intervals, wherein daily average electricity consumption corresponding to six intervals is 7.98, 6.39, 9.19, 18.78, 18.07 and 18.90 (unit kWh, taking user i as an example).
And D, calculating to obtain 6 times of outgoing times and 6 times of home returning times of the user i between 8-1-2021-2-18 days in 2020. In addition, since the average daily power consumption of the first section of the user is 7.98 and is greater than 0.5, the user is a non-vacant user, and YZ is 0.3 in the embodiment.
The effectiveness of the scheme of the invention is illustrated below by comparing users i and j:
1) if the method of patent application No. 202011293772.0 is adopted for judgment, the user is out when the daily electricity consumption is less than 0.5, and the user can be seeniDaily electricity consumption during the period from 30 days in 12 months to 12 days in 1 month is more than 0.5kWh, which indicates that the user does not go out and return home during the period;
2) if the patent method with the application number of 202120182373.5 is adopted, the daily electric quantity of the user i is equivalent to that of the user j in the period of 1 month and 5 days to 9 days, and the fact that neither user is out is judged according to the patent method;
3) if the method provided by the invention is adopted, the calculation result is shown in the table 2, the daily electric quantity of the user i in 1 month and 1 day is
Figure BDA0003232126000000091
The highest temperature of the current day is 8 ℃, and the average daily electricity consumption of the user i in the temperature interval is AWi,518.07kWh, due to
Figure BDA0003232126000000092
Therefore, it is judged that the user i has an outgoing behavior in 1 month and 1 day. Similarly, the user can be judged to have a homereturning behavior in 4 days in 1 month, an outgoing behavior in 5 days in 1 month and a homereturning behavior in 10 days in 1 month. The verification proves the effectiveness of the method provided by the invention when the user i really has the outgoing and returning behaviors.
TABLE 2
Date Maximum air temperature User i daily electricity consumption Daily electricity consumption of user j
2020/12/30 3 27.07 1.48
2020/12/31 4 25.7 1.81
2021/1/1 8 3.33 2.52
2021/1/2 10 2.72 2.88
2021/1/3 8 3.66 2.94
2021/1/4 9 10.07 2.87
2021/1/5 6 2.15 2.95
2021/1/6 3 2.11 2.31
2021/1/7 1 2.05 1.78
2021/1/8 3 2.07 1.08
2021/1/9 6 2.06 2.5
2021/1/10 7 5.44 3.16
2021/1/11 8 30.65 1.36
2021/1/12 12 29.28 1.16
For 1305 users in the analysis range, the state indexes (outgoing times and returning times) are respectively calculated, wherein 142 users are idle users, so the outgoing index is as follows:
Figure BDA0003232126000000101
the inflow index is:
Figure BDA0003232126000000102
the above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (5)

1. A population mobility index calculation method based on electric power big data is characterized by comprising the following steps:
A. acquiring temperature and daily electricity consumption data of a user;
B. dividing air temperature intervals according to the air temperature data obtained in the step A;
C. calculating daily average power consumption of the user in different temperature intervals according to the temperature intervals divided in the step B and the daily power consumption data of the user obtained in the step A;
D. and D, judging the leaving and returning states according to the daily average electricity consumption of the user in different temperature intervals calculated in the step C, and further calculating the population flow index.
2. The method for calculating the population flow index based on the electric power big data as claimed in claim 1, wherein: step A, acquiring temperature and daily electric quantity data of a user, and specifically comprising the following steps:
assume that the time range of the analysis is t1,t2,…tnGet the daily maximum temperature from the weather website and record it as
Figure FDA0003232125990000011
Acquiring the daily electricity consumption of the user from the electricity consumption information acquisition system, and recording the daily electricity consumption of the ith user as the daily electricity consumption of the ith user if the user set is {1,2, … I }
Figure FDA0003232125990000012
3. The method for calculating the population flow index based on the electric power big data as claimed in claim 2, wherein: in the step B, according to the air temperature data obtained in the step A, dividing an air temperature interval, specifically:
dividing the daily maximum air temperature into six sections according to the size of the daily maximum air temperature T, wherein the T-th sectionjDaily maximum temperature
Figure FDA0003232125990000013
If it is
Figure FDA0003232125990000014
Dividing the first interval into a first interval; if it is
Figure FDA0003232125990000015
Dividing the first interval into a second interval; if it is
Figure FDA0003232125990000016
Dividing the data into a third interval; if it is
Figure FDA0003232125990000021
Dividing the data into a fourth interval; if it is
Figure FDA0003232125990000022
Dividing the data into a fifth interval; if it is
Figure FDA0003232125990000023
It is drawn into the sixth interval.
4. The method for calculating the population flow index based on the electric power big data as claimed in claim 3, wherein: in the step C, the daily average power consumption of the user in different temperature intervals is calculated according to the temperature intervals divided in the step B and the daily power consumption data of the user obtained in the step A, and the method specifically comprises the following steps:
for user i, according to daily electricity consumption
Figure FDA0003232125990000024
Calculating electricity consumption characteristic index
Figure FDA0003232125990000025
In particular, if
Figure FDA0003232125990000026
Then
Figure FDA0003232125990000027
Otherwise, the reverse is carried out
Figure FDA0003232125990000028
Calculating the daily average electric quantity of the user i in a first interval:
Figure FDA0003232125990000029
calculating the daily average electric quantity of the user i in a second interval:
Figure FDA00032321259900000210
and calculating the daily average electric quantity of the user i in the third interval:
Figure FDA00032321259900000211
calculating the daily average electric quantity of the user i in the fourth interval:
Figure FDA00032321259900000212
calculating the daily average electric quantity of the user i in a fifth interval:
Figure FDA00032321259900000213
calculating the daily average electric quantity of the user i in a sixth interval:
Figure FDA0003232125990000031
if all the daily electricity consumption of the user in the first interval is 0, AWi,1The processing mode is applicable to all the intervals, and the values are recorded as null values.
5. The method for calculating the population flow index based on the electric power big data as claimed in claim 4, wherein: and D, judging the leaving and returning states according to the daily average power consumption calculated in the step C, and further calculating a population flow index, wherein the specific steps are as follows:
for the first interval:
user ithjThe daily dose is
Figure FDA0003232125990000032
The highest temperature in the day satisfies
Figure FDA0003232125990000033
1) If AWi,1Is not empty and
Figure FDA0003232125990000034
if the state of the previous day is not an outgoing state or a continuous outgoing state, judging that the state of the current day is an outgoing state; otherwise, judging the state of the day as a continuous outgoing state;
2) if AWi,1If the state is not empty, the state of the previous day is going out or going out continuously, if the state meets the requirement
Figure FDA0003232125990000035
Judging that the state of the day is a returning state;
judging whether the user is an idle user by using the daily average electricity consumption of the first interval, namely: AWi,1Less than or equal to 0.5 or AWi,1If the value is null, the user i is considered as a null user;
according to the above method, the analysis time range { t ] is calculated1,t2,…tnThe state of all days in the page, the sum of the states is calculated, wherein the sum of the number of times of the outgoing state of the user i is recorded as WCiThe sum of the number of home return times is FJi，InullThe total number of the vacant users.
Then the set of users is {1,2, … I } in the time range { t }1,t2,…tnThe population outflow index of } is:
Figure FDA0003232125990000036
set of users as {1,2, … I } in time range { t1,t2,…tnThe population inflow index of } is:
Figure FDA0003232125990000041
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