CN113780660A - Resident electricity consumption prediction method, system and storage medium - Google Patents

Resident electricity consumption prediction method, system and storage medium Download PDF

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CN113780660A
CN113780660A CN202111068453.4A CN202111068453A CN113780660A CN 113780660 A CN113780660 A CN 113780660A CN 202111068453 A CN202111068453 A CN 202111068453A CN 113780660 A CN113780660 A CN 113780660A
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photovoltaic power
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CN113780660B (en
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汤新杰
刘洋海
张捷
雷小林
吴志敏
陈思龙
黄楚晴
丘文广
徐扬
王文颉
曾雅怡
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Guangdong Power Grid Co Ltd
Shaoguan Power Supply Bureau Guangdong Power Grid Co Ltd
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Shaoguan Power Supply Bureau Guangdong Power Grid Co Ltd
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Abstract

The embodiment of the invention discloses a method and a system for predicting residential electricity consumption and a storage medium. The resident electricity consumption prediction method comprises the steps of obtaining monthly historical electricity consumption and photovoltaic power generation historical data of a preset area; establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to monthly historical power consumption and photovoltaic power generation historical data; according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents and photovoltaic power generation amount of a preset area are predicted; and calculating the electric quantity actually consumed by residents in the preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity. According to the technical scheme provided by the embodiment of the invention, the actual monthly power consumption of residents in the next year is predicted and obtained by establishing the power consumption prediction model and the photovoltaic power generation prediction model, so that the change trend of the electric energy load in a certain area range can be effectively predicted, and a certain decision basis is provided for planning and constructing a regional power grid.

Description

Resident electricity consumption prediction method, system and storage medium
Technical Field
The embodiment of the invention relates to the technical field of power grids, in particular to a resident power consumption prediction method, a resident power consumption prediction system and a storage medium.
Background
In recent years, with the continuous improvement of the living standard of people and the continuous transformation of consumption concepts, the living power consumption of urban residents is rapidly increased, and the photovoltaic power generation is widely concerned by people. However, photovoltaic power generation is easily affected by atmospheric environment and has randomness and intermittence, so that stable electric energy cannot be provided, and the photovoltaic power generation and a power grid are required to provide electric energy together to ensure normal life of residents.
The existing resident power supply mode generally adopts photovoltaic power generation and power grid cooperative power supply, but the existing power supply mode cannot predict the actual consumed electric energy of regional residents, is not beneficial to the planning of urban power grids, and influences the realization of the goals of energy conservation and emission reduction.
Disclosure of Invention
The embodiment of the invention provides a resident electricity consumption prediction method, a resident electricity consumption prediction system and a storage medium, and aims to solve the problems that the existing power supply mode cannot predict the change trend of regional electric energy load and is not beneficial to regional power grid power supply planning and construction.
In order to realize the technical problem, the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for predicting electricity consumption of residents, including:
acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area;
establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data;
according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents and photovoltaic power generation amount of a preset area are predicted;
and calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Optionally, the acquiring monthly historical power consumption and photovoltaic power generation historical data of the preset area includes:
acquiring monthly historical power consumption of the previous N years in a preset area;
acquiring monthly photovoltaic power generation historical data of the previous N years in a preset area, historical temperature data and historical illumination intensity of the previous N years in the preset area and weather prediction information in the preset area, wherein N is a positive integer greater than or equal to 1.
Optionally, the establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time series according to the monthly historical power consumption and the photovoltaic power generation historical data includes:
predicting a power consumption prediction model of residents in the preset area in the next year by adopting a time series prediction method according to the monthly historical power consumption;
establishing a photovoltaic power generation prediction model based on a time sequence according to the photovoltaic power generation historical data and the meteorological prediction information in the preset area;
the weather prediction information in the preset area comprises rated power of a solar panel, an inclination angle of the solar panel and a temperature coefficient.
Optionally, the predicting the electricity consumption of the residents in the preset area in the next year by using a time series prediction method according to the monthly historical electricity consumption includes:
calculating a once-moving average sequence of monthly electricity consumption in N years before the history according to the historical monthly electricity consumption in a preset area;
obtaining a constant value sequence by the ratio of the monthly historical electricity consumption to the once moving average value sequence of the monthly electricity consumption N years before the calendar;
calculating a monthly index according to the constant value sequence, and correcting the monthly index to obtain a corrected monthly index;
fitting the monthly historical power consumption data by adopting a linear regression model to obtain a fitting value sequence and a predicted value sequence;
and predicting the monthly electricity consumption prediction model of the next year according to the predicted value sequence and the corrected monthly index.
Optionally, the monthly historical power consumption Y of the previous N years in the preset area is calculated by using the following formula:
Y＝[y1,y2,…,y12N]
wherein N represents year, that is, N is a positive integer of 1 or more and 12 or less, y1Indicating the monthly historical power usage, y, of the previous month 112NIndicating the monthly historical power usage of the previous 12N months.
Optionally, the power consumption once moving average sequence N years and months before the calendar is expressed by the following formula:
Figure BDA0003259497230000031
where i denotes each month, i.e., 1,2,3, …, 12N;
the constant value sequence is expressed by the following formula:
Figure BDA0003259497230000032
wherein i is 1,2,3, …, 12N;
the monthly index is expressed by the following formula:
Figure BDA0003259497230000041
wherein R isiThe index of each month is expressed, j represents the year, and j is more than or equal to 1 and less than or equal to N;
the corrected monthly index is expressed by the following formula:
Figure BDA0003259497230000042
wherein R'iRepresents a monthly correction index;
the fitting value sequence is expressed by the following formula:
Figure BDA0003259497230000043
the sequence of the predicted values is expressed by the following formula:
Figure BDA0003259497230000044
monthly power consumption prediction model for next year
Figure BDA0003259497230000045
The following formula is adopted:
Figure BDA0003259497230000046
optionally, the photovoltaic power generation prediction model is represented by the following formula:
PG＝k·PN·sinα·(1-ξ)
wherein, PGPredicting the electrical power for photovoltaic power generation, k being the correction factor, PNα is the incident angle of sunlight, where sin α ═ sin (180- β -Ag ═ sin (β + Ag), β denotes the solar altitude, and β denotes the angle of inclination of the solar panel;
xi is a temperature coefficient and is expressed by the following formula:
Figure BDA0003259497230000047
wherein, P303KRepresents the power loss, T, of the solar panel at 303KhighRepresenting the ambient temperature at the highest point of the sun;
the predicted photovoltaic power generation amount is expressed by the following formula:
X(t)＝PG·F(t)＝k·PN·sinα·(1-ξ)·F(t)
wherein F (t) represents the natural number of days per month.
Optionally, the calculating, according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation amount, the electricity amount actually consumed by the residents in the preset area includes:
and subtracting the predicted monthly electricity consumption of the residents from the photovoltaic power generation amount to obtain the actual electricity consumption of the residents in the preset area.
In a second aspect, an embodiment of the present invention provides a system for predicting electricity consumption of residents, including a historical data acquisition module, configured to acquire monthly historical electricity consumption and photovoltaic power generation historical data of a preset area;
the model establishing module is used for establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data;
the electric quantity prediction module is used for predicting monthly electric quantity of residents and photovoltaic power generation quantity of a preset area according to the electric quantity prediction model and the photovoltaic power generation prediction model;
and the calculation module is used for calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
In a third aspect, an embodiment of the present invention provides a readable storage medium, and when instructions in the readable storage medium are executed by a processor of a residential electricity consumption prediction system, the residential electricity consumption prediction system is enabled to execute any of the residential electricity consumption prediction methods of the first aspect.
According to the resident electricity consumption prediction method provided by the embodiment of the invention, in the range of the preset area, the predicted monthly electricity consumption and the predicted photovoltaic power generation amount of the residents in the next year are obtained by establishing the resident electricity consumption prediction model and the photovoltaic power generation prediction model based on the time sequence according to the monthly historical electricity consumption, the photovoltaic power generation historical data and the weather prediction information in the area, and the predicted actual electricity consumption of the residents is obtained, so that the electric energy load change trend of the area can be effectively predicted, the method has important significance for city planning construction, understanding of town process, energy consumption structure, construction of intelligent cells and the like, and meanwhile, the method provides a powerful basis for city power grid planning, and promotes energy conservation and emission reduction.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the contents of the embodiments of the present invention and the drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting residential electricity consumption according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for predicting residential electricity consumption according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for predicting residential electricity consumption according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method for predicting residential electricity consumption according to an embodiment of the present invention;
FIG. 5 is a flow chart of a method for predicting residential electricity consumption according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a residential electricity consumption prediction system according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a residential electricity consumption prediction system according to another embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a residential electricity consumption prediction system according to another embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a residential electricity consumption prediction system according to another embodiment of the present invention;
fig. 10 is a schematic structural diagram of a residential electricity consumption prediction apparatus according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The embodiment of the invention provides a method for predicting the electricity consumption of residents. Fig. 1 is a flowchart of a residential electricity consumption prediction method according to an embodiment of the present invention. As shown in fig. 1, a method for predicting electricity consumption of residents according to an embodiment of the present invention includes:
s101, acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area.
The preset area is a preset area with a certain range. Specifically, the predetermined area may include, but is not limited to, one residential cell in a city, several residential cells in a city, and/or several cities in a province, and is not limited thereto. The monthly historical electricity consumption is the electricity consumption of residents in each month of the year before the current time in the preset area. The photovoltaic power generation historical data comprises photovoltaic power generation amount of each month of the year before the current time in the preset area. The monthly historical power consumption and photovoltaic power generation historical data of the preset area can be obtained from a metering automation system.
And S102, establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data.
Specifically, a power consumption prediction model is established by adopting a time series prediction method and is used for predicting and calculating the power consumption of residents in the preset area in each month in the next year. And establishing a photovoltaic power generation prediction model based on a time series prediction method, and obtaining the photovoltaic power generation amount of the preset area through prediction calculation.
S103, according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents in a preset area and photovoltaic power generation amount are predicted.
Through the power consumption prediction model and the photovoltaic power generation prediction model, the monthly power consumption of residents and the photovoltaic power generation amount of a preset area are obtained through prediction, and whether the photovoltaic power generation amount meets the monthly power consumption of the residents is convenient to learn.
And S104, calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
According to the predicted relationship between the monthly electricity consumption of the residents in the next year and the photovoltaic power generation amount, the actual electricity consumption of the residents in the preset area can be calculated.
According to the resident electricity consumption prediction method provided by the embodiment of the invention, the monthly historical electricity consumption and the photovoltaic power generation historical data of the preset area are obtained, the electricity consumption prediction model and the photovoltaic power generation prediction model based on the time sequence are established, and the monthly electricity consumption and the photovoltaic power generation quantity of the residents in the preset area are predicted according to the established models, so that the actual electricity consumption of the residents in the preset area is calculated, the electric energy load change trend of the area can be effectively predicted, the method has important significance for city planning construction, knowledge of urbanization processes, energy consumption structures, intelligent cell construction and the like, meanwhile, a powerful basis is provided for city power grid planning, and energy conservation and emission reduction are promoted.
Optionally, fig. 2 is a flowchart of another residential electricity consumption prediction method according to an embodiment of the present invention. On the basis of the above embodiment, referring to fig. 2, the method for predicting the electricity consumption of the residents according to the embodiment of the present invention includes:
s201, acquiring monthly historical electricity consumption of the previous N years in a preset area.
Specifically, N represents year and N is a positive integer greater than or equal to 1, such as: n may be 1,2 and/or 3. Illustratively, in the embodiment of the present invention, taking N equal to 2 as an example, the monthly historical power consumption of the previous 2 years in the preset area is obtained. Monthly historical power usage may be obtained from a metering automation system.
S202, acquiring monthly photovoltaic power generation historical data of N years before in a preset area, historical temperature data and historical illumination intensity of N years before in the preset area, and weather prediction information in the preset area, wherein N is a positive integer greater than or equal to 1.
Specifically, N represents year and N is a positive integer greater than or equal to 1, such as: n may be 1,2 and/or 3. Illustratively, in the embodiment of the invention, the monthly photovoltaic power generation historical data of the previous 2 years in the preset area, the historical temperature data and the historical illumination intensity of the previous 2 years in the preset area, and the meteorological prediction information in the preset area are acquired.
The historical temperature data and the historical illumination intensity are temperature data and illumination intensity of each month of the previous N years in the preset area, and can be obtained from public meteorological information, and the meteorological forecast information comprises but is not limited to temperature data and illumination intensity. Likewise, in the embodiment of the present invention, the historical temperature data and the historical illumination intensity are temperature data and illumination intensity of each month of the previous 2 years in the preset area.
S203, establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data.
And S204, predicting monthly electricity consumption of residents in a preset area and photovoltaic power generation amount according to the electricity consumption prediction model and the photovoltaic power generation prediction model.
And S205, calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Optionally, fig. 3 is a flowchart of another residential electricity consumption prediction method according to an embodiment of the present invention. On the basis of the above embodiment, referring to fig. 3, the method for predicting the electricity consumption of the residents according to the embodiment of the present invention includes:
s301, acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area.
And S302, predicting the electricity consumption prediction model of residents in the preset area in the next year by adopting a time series prediction method according to the monthly historical electricity consumption.
The method adopts a time series prediction method to establish a prediction model, establishes different prediction models according to different conditions of each month, can more accurately predict the electric energy load change trend of a preset area, and further accurately predict the actual electricity consumption of residents.
S303, establishing a photovoltaic power generation prediction model based on a time sequence according to the photovoltaic power generation historical data and the meteorological prediction information in the preset area; the weather prediction information in the preset area comprises rated power of a solar panel, an inclination angle of the solar panel and a temperature coefficient.
Similarly, a photovoltaic power generation prediction model is established based on the time sequence, and the meteorological information is taken into account, so that the predicted photovoltaic power generation amount is closer to the actual photovoltaic power generation amount, and a more credible decision basis is provided for the work of a power supply department.
S304, according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents in a preset area and photovoltaic power generation amount are predicted.
S305, calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Optionally, fig. 4 is a flowchart of another residential electricity consumption prediction method according to an embodiment of the present invention. On the basis of the above embodiment, referring to fig. 4, the method for predicting the electricity consumption of the residents according to the embodiment of the present invention includes:
s401, monthly historical power consumption and photovoltaic power generation historical data of a preset area are obtained.
Optionally, the monthly historical power consumption Y of the previous N years in the preset area is calculated by using the following formula:
Y＝[y1,y2,…,y12N] (1)
wherein N represents year, that is, N is a positive integer of 1 or more and 12 or less, y1Represents the monthly historical power usage, y, of month 1 of the previous N years12NThe historical monthly power consumption in the 12 Nth month of the previous N years is shown, and the historical monthly power consumption Y is a row matrix.
Specifically, in the embodiment of the present invention, taking N equal to 2 as an example, the monthly historical power consumption Y in the previous 2 years in the preset area is calculated by using the following formula:
Y＝[y1，y2，…y23，y24]
wherein, y1Represents the monthly historical power usage, y, of month 1 of the previous 2 years2Represents the monthly historical power usage, y, of month 2 of the previous 2 years24Representing the monthly historical power usage in month 24 of the previous 2 years.
S402, calculating a once moving average sequence of monthly electricity consumption N years before the history according to the historical monthly electricity consumption of the preset area.
Optionally, the power consumption once moving average sequence N years and months before the calendar is expressed by the following formula:
Figure BDA0003259497230000111
where i denotes each month, i.e., 1,2,3, …, 12N; y isiRepresents the monthly historical power consumption of the ith month of the historical N years, AiRepresents the monthly once-through moving average sequence of the ith month of the historical N years.
Specifically, in the embodiment of the present invention, the sequence of once-through moving average of electricity consumption in 2 months in history is as follows:
Figure BDA0003259497230000112
where i denotes each month, i.e., i ═ 1,2,3, …, 24. Then the process of the first step is carried out,
Figure BDA0003259497230000113
Figure BDA0003259497230000114
and S403, obtaining a constant value sequence by the ratio of the historical monthly electricity consumption to the once moving average value sequence of monthly electricity consumption in N years before the history.
Optionally, the constant value sequence is expressed by the following formula:
Figure BDA0003259497230000115
wherein i is 1,2,3, …, 12N; b isiAnd the constant value sequence represents the monthly power utilization constant value sequence of the ith month of the historical N years, and can carry out quantitative analysis on the average value of the monthly historical power utilization and the monthly historical power utilization.
Specifically, in the embodiment of the invention, the ratio is a constant value sequence obtained by dividing the historical electricity consumption of the month and the once-moving average value sequence of the electricity consumption of the month in 2 years before the history, and the constant value sequence of the electricity consumption of the month in 1 st month in 2 years after the history is a constant value sequence
Figure BDA0003259497230000116
The sequence of the electricity consumption constant values of the month of month 2 in the past 2 years is
Figure BDA0003259497230000121
The sequence of the electricity consumption constant values of the 24 th month in the past 2 years is
Figure BDA0003259497230000122
The constant value sequence can be used for carrying out quantitative analysis on the monthly historical electricity consumption and the average value of the monthly historical electricity consumption.
S404, calculating a monthly index according to the constant value sequence, and correcting the monthly index to obtain a corrected monthly index.
Optionally, the monthly index is expressed by the following formula:
Figure BDA0003259497230000123
wherein R isiDenotes the monthly index, j denotes year, j is 1. ltoreq. N, i is 1,2,3, …, 12. The monthly index RiThe meaning of (c) is the average value of the sequence of electrical constant values for the month of the same month in each of the preceding N years.
Specifically, in the embodiment of the present invention, the monthly index of the previous 2 years is calculated according to the constant value sequence, and the monthly index of the 1 st month of the previous 2 years is
Figure BDA0003259497230000124
Monthly index of month 2 of the previous 2 years
Figure BDA0003259497230000125
Monthly index of month 12 of the previous 2 years
Figure BDA0003259497230000126
In order to improve the accuracy of monthly electricity consumption prediction, the monthly index is corrected to obtain a corrected monthly index.
The corrected monthly index is expressed by the following formula:
Figure BDA0003259497230000127
wherein R'iIndicating a lunar correction index, versus a lunar index RiThe accuracy of prediction of monthly electricity consumption can be improved by correctingAnd (5) determining.
Specifically, in the embodiment of the present invention, the monthly correction index of month 1 is
Figure BDA0003259497230000128
Monthly correction index at month 2 of
Figure BDA0003259497230000129
Monthly correction index at month 12 of
Figure BDA00032594972300001210
S405, fitting the monthly historical power consumption data by adopting a linear regression model to obtain a fitting value sequence and a predicted value sequence;
optionally, the fitting value sequence is expressed by the following formula:
Figure BDA0003259497230000131
specifically, in the embodiment of the invention, monthly historical power consumption data of 2 years before the history are fitted to obtain a fitting value sequence
Figure BDA0003259497230000132
Wherein the content of the first and second substances,
Figure BDA0003259497230000133
a fitted value representing monthly historical power usage for month 1 in the previous 2 years,
Figure BDA0003259497230000134
fitting value of the monthly historical power consumption of month 2 in the previous 2 years
Figure BDA0003259497230000135
According to the monthly historical power consumption yiAnd the rule among the data points is obtained by performing function fitting by adopting a linear regression model.
And further, calculating to obtain a corresponding predicted value sequence according to the quantity relation satisfied by the fitting value and the predicted value. The sequence of the predicted values is expressed by the following formula:
Figure BDA0003259497230000136
wherein the content of the first and second substances,
Figure BDA0003259497230000137
representing a first predicted monthly power usage for month 1 of the next year,
Figure BDA0003259497230000138
representing a first predicted monthly power usage for month 2 of the next year,
Figure BDA0003259497230000139
representing a first predicted monthly power usage for month 12 of the next year.
And S406, predicting the monthly electricity consumption prediction model of the next year according to the predicted value sequence and the corrected monthly index.
Optionally, the monthly power consumption prediction model of the next year
Figure BDA00032594972300001310
The following formula is adopted:
Figure BDA00032594972300001311
wherein the content of the first and second substances,
Figure BDA00032594972300001312
representing a first predicted monthly power usage for the ith month of the next year,
Figure BDA00032594972300001313
representing a second predicted monthly power usage for the ith month of the next year.
Specifically, in the present embodiment, month 1 of the next yearThe second predicted monthly power usage of
Figure BDA0003259497230000141
The second predicted monthly power usage of month 2 of the next year is
Figure BDA0003259497230000142
The second predicted monthly power usage of month 12 of the next year is
Figure BDA0003259497230000143
S407, establishing a photovoltaic power generation prediction model based on a time sequence according to the photovoltaic power generation historical data and the meteorological prediction information in the preset area; the weather prediction information in the preset area comprises rated power of a solar panel, an inclination angle of the solar panel and a temperature coefficient.
Optionally, the photovoltaic power generation prediction model is represented by the following formula:
PG＝k·PN·sinα·(1-ξ) (9)
wherein, PGPredicting the electrical power for photovoltaic power generation, k being the correction factor, PNα is the incident angle of sunlight, where sin α ═ sin (180- β -Ag ═ sin (β + Ag), β denotes the solar altitude, and β denotes the angle of inclination of the solar panel;
xi is a temperature coefficient and is expressed by the following formula:
Figure BDA0003259497230000144
wherein, P303KRepresents the power loss, T, of the solar panel at 303KhighRepresenting the ambient temperature at the highest point of the sun.
Specifically, the electric power P is predicted in photovoltaic power generationGIn the calculation formula (2), the rated power P of the solar panelNCan be obtained from the product information of the used solar cell panel when the solar cell panel leaves the factory,the Ag of the inclination angle of the solar panel can be obtained by measuring the angle of installation of the solar panel, the sun height beta and the ambient temperature T of the highest point of the sunhighCan be obtained by weather forecast information in a preset area.
And S408, predicting monthly electricity consumption of residents in a preset area and photovoltaic power generation amount according to the electricity consumption prediction model and the photovoltaic power generation prediction model.
Optionally, the predicted photovoltaic power generation amount is expressed by the following formula:
X(t)＝PG·F(t)＝k·PN·sinα·(1-ξ)·F(t) (11)
wherein F (t) represents the natural number of days per month.
Specifically, in the embodiment of the present invention, the natural days of month 1 of the next year is 31 days, and the predicted photovoltaic power generation amount of month 1 is x (t) ═ PG·31＝31·k·PNSin α (1- ξ). And a second predicted monthly power usage of month 1 of the next year is
Figure BDA0003259497230000151
And S409, calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Optionally, fig. 5 is a flowchart of a method for predicting residential electricity consumption according to another embodiment of the present invention. On the basis of the above embodiment, referring to fig. 5, the method for predicting the electricity consumption of the residents according to the embodiment of the present invention includes:
s501, acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area;
s502, establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data;
s503, according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents and photovoltaic power generation amount of a preset area are predicted;
and S504, subtracting the predicted monthly electricity consumption of the residents from the photovoltaic power generation amount to obtain the actual electricity consumption of the residents in the preset area.
Specifically, since photovoltaic power generation is easily affected by the atmospheric environment, the photovoltaic power generation amount is generally smaller than the actual monthly power consumption of residents in the preset area, and therefore, a power supply department is required to cooperate to provide a part which is not enough for photovoltaic power generation, that is, the electric quantity W actually consumed by the residents in the preset area, so that the electric quantity W actually consumed by the residents in the preset area is expressed by the following formula:
Figure BDA0003259497230000152
wherein X (t) is the actual natural days of the ith month of the next year.
Specifically, in the embodiment of the present invention, the amount of electricity actually consumed by the residents in month 1 of the next year in the preset area is
Figure BDA0003259497230000161
By predicting the actual consumed electric quantity of residents in the preset area in the next year, the more accurate electric energy load change trend of the area can be obtained, and the method has certain reference significance for energy supply of the capacity of a power supply department, so that energy conservation and emission reduction are better realized.
Optionally, fig. 6 is a schematic structural diagram of a residential electricity consumption prediction system according to an embodiment of the present invention. On the basis of the above embodiment, referring to fig. 6, the resident electricity consumption amount prediction system provided by the embodiment of the present invention includes:
the historical data acquisition module 10 is used for acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area;
the model establishing module 20 is used for establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to monthly historical power consumption and photovoltaic power generation historical data;
the electric quantity prediction module 30 is used for predicting monthly electric quantity of residents in a preset area and photovoltaic power generation quantity according to the electric quantity prediction model and the photovoltaic power generation prediction model;
and the calculating module 40 is used for calculating the electric quantity actually consumed by residents in the preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Alternatively, fig. 7 is a schematic structural diagram of a further residential electricity consumption prediction system according to an embodiment of the present invention. On the basis of the foregoing embodiment, referring to fig. 7, the system for predicting residential electricity consumption according to the embodiment of the present invention further includes:
a historical power consumption obtaining unit 101, configured to obtain monthly historical power consumption of the previous N years in a preset area;
the photovoltaic power generation historical data acquisition unit 102 is configured to acquire monthly photovoltaic power generation historical data of the previous N years in the preset area, historical temperature data and historical illumination intensity of the previous N years in the preset area, and weather prediction information in the preset area, where N is a positive integer greater than or equal to 1.
Alternatively, fig. 8 is a schematic structural diagram of a further residential electricity consumption prediction system according to an embodiment of the present invention. On the basis of the foregoing embodiment, referring to fig. 8, the system for predicting electricity consumption of residents according to the embodiment of the present invention further includes:
a power consumption prediction unit 201, configured to predict, according to monthly historical power consumption, a power consumption prediction model of residents in a preset area of the next year by using a time series prediction method;
the photovoltaic power generation prediction unit 202 is used for establishing a photovoltaic power generation prediction model based on a time sequence according to the photovoltaic power generation historical data and meteorological prediction information in a preset region; the weather prediction information in the preset area comprises the rated power of the solar panel, the inclination angle of the solar panel and the temperature coefficient.
Alternatively, fig. 9 is a schematic structural diagram of a further residential electricity consumption prediction system according to an embodiment of the present invention. On the basis of the foregoing embodiment, referring to fig. 9, the system for predicting residential electricity consumption according to the embodiment of the present invention further includes:
the sequence calculation subunit 2011 is configured to calculate a moving average sequence of monthly electricity consumption once in N years before the history according to the monthly historical electricity consumption of the preset area;
the sequence maker subunit 2012 is used for obtaining a constant value sequence by using the ratio of the monthly historical electricity consumption to the N monthly once moving average value sequences before the history;
the index correction subunit 2013 is used for calculating the monthly index according to the constant value sequence and correcting the monthly index to obtain a corrected monthly index;
the data fitting subunit 2014 is used for fitting the monthly historical power consumption data by adopting a linear regression model to obtain a fitting value sequence and a predicted value sequence;
the model building subunit 2015 is configured to predict a monthly electricity consumption prediction model of the next year according to the predicted value sequence and the corrected monthly index.
Alternatively, fig. 10 is a schematic structural diagram of a residential electricity consumption prediction apparatus according to an embodiment of the present invention. On the basis of the above-described embodiment, referring to fig. 10, the embodiment of the invention provides a readable storage medium 51 having stored thereon a software program, which, when instructions in the readable storage medium 51 are executed by the processor 50 in the residential electricity consumption amount prediction apparatus, enables the residential electricity consumption amount prediction apparatus to execute the residential electricity consumption amount prediction method according to any of the above-described embodiments. The method comprises the following steps: acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area; establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data; according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents and photovoltaic power generation amount of a preset area are predicted; and calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
Of course, the storage medium containing the computer-executable instructions provided by the embodiment of the present invention is not limited to the operation of the residential power consumption prediction method described above, and may also perform the relevant operations in the residential power consumption prediction method provided by any embodiment of the present invention, and has corresponding functions and advantages.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute the method for predicting the electricity consumption of the residents according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A resident electricity consumption prediction method is characterized by comprising the following steps:
acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area;
establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data;
according to the power consumption prediction model and the photovoltaic power generation prediction model, monthly power consumption of residents and photovoltaic power generation amount of a preset area are predicted;
and calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
2. The residential power consumption prediction method according to claim 1, wherein the acquiring monthly historical power consumption and photovoltaic power generation historical data of the preset area comprises:
acquiring monthly historical power consumption of the previous N years in a preset area;
acquiring monthly photovoltaic power generation historical data of the previous N years in a preset area, historical temperature data and historical illumination intensity of the previous N years in the preset area and weather prediction information in the preset area, wherein N is a positive integer greater than or equal to 1.
3. The residential power consumption prediction method according to claim 2, wherein the building of a power consumption prediction model and a photovoltaic power generation prediction model based on time series according to the monthly historical power consumption and the photovoltaic power generation historical data comprises:
predicting a power consumption prediction model of residents in the preset area in the next year by adopting a time series prediction method according to the monthly historical power consumption;
establishing a photovoltaic power generation prediction model based on a time sequence according to the photovoltaic power generation historical data and the meteorological prediction information in the preset area;
the weather prediction information in the preset area comprises rated power of a solar panel, an inclination angle of the solar panel and a temperature coefficient.
4. The resident electricity consumption prediction method according to claim 3, wherein the prediction model for predicting the electricity consumption of the residents in the preset area in the next year by using a time series prediction method based on the monthly historical electricity consumption comprises:
calculating a once-moving average sequence of monthly electricity consumption in N years before the history according to the historical monthly electricity consumption in a preset area;
obtaining a constant value sequence by the ratio of the monthly historical electricity consumption to the once moving average value sequence of the monthly electricity consumption N years before the calendar;
calculating a monthly index according to the constant value sequence, and correcting the monthly index to obtain a corrected monthly index;
fitting the monthly historical power consumption data by adopting a linear regression model to obtain a fitting value sequence and a predicted value sequence;
and predicting the monthly electricity consumption prediction model of the next year according to the predicted value sequence and the corrected monthly index.
5. The resident power consumption amount prediction method according to claim 4,
the monthly historical power consumption Y of the previous N years in the preset area is calculated by adopting the following formula:
Y＝[y1,y2,…,y12N]
wherein N represents year, that is, N is a positive integer of 1 or more and 12 or less, y1Indicating the monthly historical power usage, y, of the previous month 112NIndicating the monthly historical power usage of the previous 12N months.
6. The resident power consumption amount prediction method according to claim 5,
the power consumption once moving average sequence of N years and months before the calendar is expressed by the following formula:
Figure FDA0003259497220000021
where i denotes each month, i.e., 1,2,3, …, 12N;
the constant value sequence is expressed by the following formula:
Figure FDA0003259497220000031
wherein i is 1,2,3, …, 12N;
the monthly index is expressed by the following formula:
Figure FDA0003259497220000032
wherein R isiThe index of each month is expressed, j represents the year, and j is more than or equal to 1 and less than or equal to N;
the corrected monthly index is expressed by the following formula:
Figure FDA0003259497220000033
wherein R'iRepresents a monthly correction index;
the fitting value sequence is expressed by the following formula:
Figure FDA0003259497220000034
the sequence of the predicted values is expressed by the following formula:
Figure FDA0003259497220000035
monthly power consumption prediction model for next year
Figure FDA0003259497220000036
The following formula is adopted:
Figure FDA0003259497220000037
7. the resident power consumption amount prediction method according to claim 3,
the photovoltaic power generation prediction model is expressed by the following formula:
PG＝k·PN·sinα·(1-ξ)
wherein, PGPredicting the electrical power for photovoltaic power generation, k being the correction factor, PNα is the incident angle of sunlight, where sin α ═ sin (180- β -Ag ═ sin (β + Ag), β denotes the solar altitude, and β denotes the angle of inclination of the solar panel;
xi is a temperature coefficient and is expressed by the following formula:
Figure FDA0003259497220000041
wherein, P303KRepresents the power loss, T, of the solar panel at 303KhighRepresenting the ambient temperature at the highest point of the sun;
the predicted photovoltaic power generation amount is expressed by the following formula:
X(t)＝PG·F(t)＝k·PN·sinα·(1-ξ)·F(t)
wherein F (t) represents the natural number of days per month.
8. The resident electricity consumption prediction method according to claim 1, wherein the calculating of the amount of electricity actually consumed by the residents in the preset area based on the predicted monthly electricity consumption of the residents and the photovoltaic power generation amount comprises:
and subtracting the predicted monthly electricity consumption of the residents from the photovoltaic power generation amount to obtain the actual electricity consumption of the residents in the preset area.
9. A resident electricity consumption amount prediction system characterized by comprising:
the historical data acquisition module is used for acquiring monthly historical power consumption and photovoltaic power generation historical data of a preset area;
the model establishing module is used for establishing a power consumption prediction model and a photovoltaic power generation prediction model based on a time sequence according to the monthly historical power consumption and the photovoltaic power generation historical data;
the electric quantity prediction module is used for predicting monthly electric quantity of residents and photovoltaic power generation quantity of a preset area according to the electric quantity prediction model and the photovoltaic power generation prediction model;
and the calculation module is used for calculating the electric quantity actually consumed by residents in a preset area according to the predicted monthly electricity consumption of the residents and the photovoltaic power generation quantity.
10. A readable storage medium, wherein instructions in the readable storage medium, when executed by a processor of a residential power consumption amount prediction system, enable the residential power consumption amount prediction system to perform the residential power consumption amount prediction method according to any one of claims 1 to 8.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115603321A (en) * 2022-11-11 2023-01-13 浙江浙能能源服务有限公司（Cn） Power load prediction system and method based on power consumption data
CN115829134A (en) * 2022-12-08 2023-03-21 国网山东省电力公司临沂供电公司 Power supply scheduling method and system oriented to source network load uncertainty
CN116632930A (en) * 2023-02-24 2023-08-22 广东永光新能源设计咨询有限公司 Intelligent control method, system, medium and equipment for renewable energy and commercial power
CN117056848A (en) * 2023-10-10 2023-11-14 湖北华中电力科技开发有限责任公司 Monitoring data quality analysis method for improving power system decision
CN117892982A (en) * 2024-03-14 2024-04-16 国家电投集团云南国际电力投资有限公司 Power consumption scheduling method, device, equipment and storage medium for light Chu Zhi flexible building

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140297051A1 (en) * 2013-03-26 2014-10-02 Northeastern University Energy resource-grid-load automatic control system of smart microgrid and control methods thereof
CN105844365A (en) * 2016-04-08 2016-08-10 北方工业大学 Optimization method and device of household energy management system
CN106845669A (en) * 2016-12-12 2017-06-13 国网上海市电力公司 Method based on exponential smoothing prediction power network year continuous loading
US20170262007A1 (en) * 2016-03-10 2017-09-14 Macau University Of Science And Technology Multi-agent oriented method for forecasting-based control with load priority of microgrid in island mode
CN109767033A (en) * 2018-12-25 2019-05-17 深圳供电局有限公司 Dispatching method, device, computer equipment and the storage medium of photovoltaic electric
US20200209430A1 (en) * 2018-12-28 2020-07-02 Utopus Insights, Inc. Systems and methods distributed-solar power forecasting using parameter regularization
CN112366708A (en) * 2020-11-30 2021-02-12 深圳市汇拓新邦科技有限公司 Charging management method, device, equipment and storage medium
CN112634077A (en) * 2020-12-18 2021-04-09 四川大汇大数据服务有限公司 Medium-and-long-term power supply and demand situation analysis method
CN113159427A (en) * 2021-04-25 2021-07-23 西安交通大学 Regional power grid load prediction method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140297051A1 (en) * 2013-03-26 2014-10-02 Northeastern University Energy resource-grid-load automatic control system of smart microgrid and control methods thereof
US20170262007A1 (en) * 2016-03-10 2017-09-14 Macau University Of Science And Technology Multi-agent oriented method for forecasting-based control with load priority of microgrid in island mode
CN105844365A (en) * 2016-04-08 2016-08-10 北方工业大学 Optimization method and device of household energy management system
CN106845669A (en) * 2016-12-12 2017-06-13 国网上海市电力公司 Method based on exponential smoothing prediction power network year continuous loading
CN109767033A (en) * 2018-12-25 2019-05-17 深圳供电局有限公司 Dispatching method, device, computer equipment and the storage medium of photovoltaic electric
US20200209430A1 (en) * 2018-12-28 2020-07-02 Utopus Insights, Inc. Systems and methods distributed-solar power forecasting using parameter regularization
CN112366708A (en) * 2020-11-30 2021-02-12 深圳市汇拓新邦科技有限公司 Charging management method, device, equipment and storage medium
CN112634077A (en) * 2020-12-18 2021-04-09 四川大汇大数据服务有限公司 Medium-and-long-term power supply and demand situation analysis method
CN113159427A (en) * 2021-04-25 2021-07-23 西安交通大学 Regional power grid load prediction method

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115603321A (en) * 2022-11-11 2023-01-13 浙江浙能能源服务有限公司（Cn） Power load prediction system and method based on power consumption data
CN115829134A (en) * 2022-12-08 2023-03-21 国网山东省电力公司临沂供电公司 Power supply scheduling method and system oriented to source network load uncertainty
CN115829134B (en) * 2022-12-08 2023-10-10 国网山东省电力公司临沂供电公司 Power supply scheduling method and system for uncertainty of source network load
CN116632930A (en) * 2023-02-24 2023-08-22 广东永光新能源设计咨询有限公司 Intelligent control method, system, medium and equipment for renewable energy and commercial power
CN116632930B (en) * 2023-02-24 2024-01-26 广东永光新能源设计咨询有限公司 Intelligent control method, system, medium and equipment for renewable energy and commercial power
CN117056848A (en) * 2023-10-10 2023-11-14 湖北华中电力科技开发有限责任公司 Monitoring data quality analysis method for improving power system decision
CN117056848B (en) * 2023-10-10 2023-12-29 湖北华中电力科技开发有限责任公司 Monitoring data quality analysis method for improving power system decision
CN117892982A (en) * 2024-03-14 2024-04-16 国家电投集团云南国际电力投资有限公司 Power consumption scheduling method, device, equipment and storage medium for light Chu Zhi flexible building

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