CN117134322A - Method, system, equipment and medium for predicting power consumption preference of low-voltage transformer area user - Google Patents
Method, system, equipment and medium for predicting power consumption preference of low-voltage transformer area user Download PDFInfo
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- H—ELECTRICITY
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Abstract
The application relates to a low-voltage station user electricity consumption preference prediction method, which comprises the following steps: constructing a low-voltage transformer area user electricity preference model, wherein the low-voltage transformer area user electricity preference model comprises an external influence factor analysis module and an internal change rule analysis module; the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation; the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation; and acquiring power consumption data of users in the transformer area, and predicting power consumption preference of the corresponding users based on the constructed power consumption preference model of the users in the low-voltage transformer area.
Description
Technical Field
The application relates to a method, a system, equipment and a medium for predicting electricity consumption preference of a low-voltage station user, and belongs to the technical field of electric power measurement.
Background
The power system load has strong periodicity and randomness, and has extremely complex and tight relation with factors such as industrial structure, economic level, politics, weather and the like of the region. The load of the low-voltage station area changes regularly according to a certain trend in time and space on one hand; on the other hand, the load of residents is mainly influenced severely by a plurality of factors such as weather conditions, electricity utilization habits, economic level and the like, and obvious nonlinear characteristics and random characteristics exist. Therefore, the load characteristics of the low-voltage transformer area are comprehensively analyzed, the regularity and the fluctuation of the low-voltage transformer area are mastered, and the low-voltage transformer area is always the key point of low-voltage distribution network planning research. Many factors affecting the power load characteristics are often measured and analyzed by qualitative and quantitative analysis; the trend of the power system load in the time dimension is often measured and analyzed through load characteristic indexes and curves. In the aspect of analyzing the load characteristics of the low-voltage transformer area users, the number of indexes related to the electricity consumption behaviors of the users is large, and the randomness of the index change is large, so that the main difficulties are as follows:
(1) In the prior art, most of methods adopted by modeling of the user electricity preference of the low-voltage area are user questionnaires, namely, the use time of each electric appliance is determined by inquiring the time range of each electric appliance allowed to be planned by the user, and the method makes the scale and customization of the modeling of the user preference difficult to realize.
(2) The factors influencing the load of the platform area are many, on one hand, the action trend of the influencing factors needs to be qualitatively analyzed, and on the other hand, the action degree of the influencing factors needs to be quantitatively analyzed, and the factors which have the greatest influence on the load change are analyzed to separate the primary and secondary.
(3) In recent years, load characteristics of regional total load have been studied extensively, but the degree of knowledge of load characteristics of distribution areas is insufficient, and thus, the actual situation of load characteristics of users has been ignored based on whether users for power use are managed in actual operation or past industry classification.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides a method, a system, equipment and a medium for predicting the electricity consumption preference of a low-voltage station user.
The technical scheme of the application is as follows:
on one hand, the application provides a low-voltage station user electricity consumption preference prediction method, which comprises the following steps:
constructing a low-voltage transformer area user electricity preference model, wherein the low-voltage transformer area user electricity preference model comprises an external influence factor analysis module and an internal change rule analysis module;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
and acquiring power consumption data of users in the transformer area, and predicting power consumption preference of the corresponding users based on the constructed power consumption preference model of the users in the low-voltage transformer area.
As a preferred embodiment, the external influence factors include climate factors, economic factors, time factors, electricity price factors, and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover;
the economic factors include regional GDP and average income index;
the time factors include seasonal period, workday period, and holiday period;
the electricity price factors comprise step electricity price and time-of-use electricity price;
the random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above.
As a preferred embodiment, the electric power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-to-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load, and a monthly average daily load rate.
As a preferred embodiment, the electrical load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
On the other hand, the application also provides a low-voltage station user electricity consumption preference prediction system, which comprises:
the model construction module is used for constructing a low-voltage transformer area user electricity preference model and comprises an external influence factor analysis module and an internal change rule analysis module;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
and the prediction module is used for acquiring the electricity consumption data of the users in the transformer area and predicting the electricity consumption preference of the corresponding users based on the constructed low-voltage transformer area user electricity consumption preference model.
As a preferred embodiment, the external influence factors include climate factors, economic factors, time factors, electricity price factors, and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover;
the economic factors include regional GDP and average income index;
the time factors include seasonal period, workday period, and holiday period;
the electricity price factors comprise step electricity price and time-of-use electricity price;
the random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above.
As a preferred embodiment, the electric power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-to-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load, and a monthly average daily load rate.
As a preferred embodiment, the electrical load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
In still another aspect, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the low voltage station user electricity consumption preference prediction method according to any of the embodiments of the present application when the program is executed by the processor.
In yet another aspect, the present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a low voltage station user electricity consumption preference prediction method according to any of the embodiments of the present application.
The application has the following beneficial effects:
according to the application, the external influence factors of the load are summarized and generalized and analyzed by utilizing data analysis and data mining technology, the relevance of different weather, economy, time and electricity price on load fluctuation is compared by considering the load characteristic index, and the relevance between multiple external influence factors and the load and the relevance between the load internal self-power load characteristic index and the load are found. The method can well capture the electricity consumption behavior habit of the user and factors influencing the electricity consumption preference of the user, and complete modeling of the load characteristic of the low-voltage transformer area and prediction of the user preference.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed.
It is to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Embodiment one:
referring to fig. 1, the embodiment provides a low-voltage station user electricity consumption preference prediction method, which includes the following steps:
s100, constructing a low-voltage transformer area user electricity preference model, wherein the low-voltage transformer area user electricity preference model comprises an external influence factor analysis module and an internal change rule analysis module;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
and S200, acquiring power consumption data of users in the transformer area, and predicting power consumption preference of the corresponding users based on the constructed power consumption preference model of the users in the low-voltage transformer area.
As a preferred embodiment of the present embodiment, the external influence factors include climate factors, economic factors, time factors, electricity price factors, and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover; along with the development of social economy and the improvement of living standard of people, the total amount of cooling and heating loads is high and higher in the load of the platform area, so that electricity consumption peaks are often caused in summer and winter, and the load of the platform area changes when the cooling and heating loads are left and right. The cooling and heating load is mainly influenced by meteorological factors, so that the influence of the meteorological on the load of the platform area is necessary to be studied. The cooling and heating loads of the platform area mainly comprise air conditioning loads and electric heater loads, and the proportion of the cooling and heating loads in the total load of the same season is determined by the weather variation differences of different seasons, namely the weather influences on the loads are different in different seasons. As known from common knowledge, the human body comfort index of weather conditions in spring and autumn is high, peak load is generally not formed, peak-valley difference is small, and the response degree of load to weather factors is low; the human comfort index of the weather conditions in summer and winter is low, peak load is often formed, peak-valley difference is large, and the response degree of the load to weather factors is high. Meanwhile, the response degree of the areas with high urban areas to the meteorological conditions is higher than that of the areas with low urban areas. Therefore, research on the influence of meteorological factors on the load of the platform area is focused on areas with high urban areas and two seasons in summer and winter. And the average air temperature, the lowest air temperature in winter, the highest air temperature in summer and the sunshine hours are selected to analyze the correlation of meteorological factors and loads.
The economic factors include regional GDP and average income index; the economic development level is the first driving force for the increase of the power demand and is a macroscopic factor affecting the load characteristics for a long time, generally, the higher the economic development level is, the higher the electrification degree is, the higher the average life power consumption of residents is, the load factor level tends to be reduced, and the peak-valley difference of a power grid is larger. GDP is commonly used internationally to represent the level of economic development. The proportion of the load of the platform area in the total load can be regarded as unchanged in a short time, so that the approximate relation between the load of the platform area and the GDP can be obtained from the relation between the social electricity consumption and the GDP, and in conclusion, the economic factors are in direct proportion to the load of the platform area, namely, the higher the economic level is, the higher the electrification degree is, and the higher the electricity consumption of people per capita is, the higher the load of the platform area is.
The time factors include seasonal period, workday period, and holiday period; the time factor is another important factor which affects the load of the platform area except weather and economic factors, and mainly reflects the influence of different seasons on the load of the platform area and the characteristic that the load is naturally increased. The long-term natural growth of the load is mainly due to the development of national economy and the improvement of living standard of people, the load is faster when the economy is developed, and the influence of different seasons on the load of a platform area can be analyzed from the sensitivity of different seasons to temperature, main energy consumption equipment of different seasons and the like.
The electricity price factors comprise step electricity price and time-of-use electricity price; the intelligent electricity utilization is one of six links of constructing the intelligent power grid, and a novel electricity supply and utilization relation of the power grid and real-time interaction of the user energy flow, the information flow and the service flow is constructed, so that comprehensive service level and energy utilization efficiency are comprehensively improved. The demand response is developed rapidly in the present year, and the benefit win-win of each party is realized by changing the inherent habit power consumption mode after receiving the direct compensation notification of the induced load reduction or the power price rising signal sent by the power supply party. Demand response research based on electricity price is one of two types of research in demand response, and research on demand side (user side) in China mainly focuses on two aspects: the design theory of the time-of-use electricity price is researched and how to increase the income and cost by using the time-of-use electricity price is analyzed from the perspective of users.
The random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above. In addition to the above factors, random factors and different load composition ratios also affect the load of the cell. Some major political activities or popular television programs cause peak load segments and increase peak-to-valley differences of load; or power system faults or open-gate limits, etc., lead to underestimation of the load segment. In addition, the bay covers a certain area, and the difference in load characteristics may be caused by the difference in load proportions of residents and businesses of the area.
As a preferred embodiment of the present embodiment, the electric power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load rate, wherein:
daily maximum load P d,max : the point of record with the largest value in the daily load data statistics recordsThe method comprises the following steps:
f 1 ＝P d,max ＝max{P 1 ,P 2 ,…,P n }；
where n is the number of records per day.
Daily minimum load P d,min : the record point with the smallest value in the daily load data statistics records is:
f 2 ＝P d,min ＝min{P 1 ,P 2 ,…,P n }；
daily average load P d,av : the average daily recorded load can be approximated by using the average of the daily composite values, namely:
daily load rate γ: the average daily load factor is the ratio of average daily load to maximum daily load, namely:
daily minimum load factor β:
daily peak-valley difference: the difference between the values of the daily maximum load and the daily minimum load, namely:
f 6 ＝P d,max -P d,min ；
daily peak Gu Chalv:
daily load curve: daily load changes over time are plotted as curves.
Maximum load for month: the maximum value of the data record of all loads in each month is the maximum value of the maximum loads in all days in the month.
Month minimum load: the minimum of the data records for all loads per month, i.e. the minimum of the daily minimum loads for that month.
Average daily load per month: the average of the monthly load, that is, the average of the daily monthly load.
Average daily load rate per month: average daily average load rate per month.
As an example, typical daily load rates and minimum load rates are shown in the following table:
TABLE 1 typical daily and minimum load rates
As an example, typical summer and winter peak-to-valley rates and daily peak-to-valley rates are shown in the following table:
TABLE 2 typical daily peak to valley and daily peak to valley rates
As a preferred implementation of the present embodiment, the power load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
Embodiment two:
the embodiment provides a low-voltage station user electricity consumption preference prediction system, which comprises:
the model construction module is used for constructing a low-voltage transformer area user electricity preference model and comprises an external influence factor analysis module and an internal change rule analysis module; the module is used for implementing the function of step S100 in the first embodiment, and will not be described here again;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
the prediction module is used for obtaining the electricity consumption data of the users in the transformer area, and performing electricity consumption preference prediction of the corresponding users based on the built low-voltage transformer area user electricity consumption preference model, and the module is used for realizing the function of step S200 in the first embodiment, and is not described herein.
As a preferred embodiment of the present embodiment, the external influence factors include climate factors, economic factors, time factors, electricity price factors, and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover;
the economic factors include regional GDP and average income index;
the time factors include seasonal period, workday period, and holiday period;
the electricity price factors comprise step electricity price and time-of-use electricity price;
the random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above.
As a preferred embodiment of the present embodiment, the electric power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load factor, a daily minimum load factor, a daily peak-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load, and a monthly average daily load factor.
As a preferred implementation of the present embodiment, the power load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
Embodiment III:
the embodiment provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the low-voltage area user electricity consumption preference prediction method according to any embodiment of the application when executing the program.
Embodiment four:
the present embodiment proposes a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a low-voltage district user electricity consumption preference prediction method according to any of the embodiments of the present application.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent: a, b, c, a and b, a and c, b and c or a and b and c, wherein a, b and c can be single or multiple.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (hereinafter referred to as ROM), a random access Memory (Random Access Memory) and various media capable of storing program codes such as a magnetic disk or an optical disk.
The foregoing description is only illustrative of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. The low-voltage station user electricity consumption preference prediction method is characterized by comprising the following steps of:
constructing a low-voltage transformer area user electricity preference model, wherein the low-voltage transformer area user electricity preference model comprises an external influence factor analysis module and an internal change rule analysis module;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
and acquiring power consumption data of users in the transformer area, and predicting power consumption preference of the corresponding users based on the constructed power consumption preference model of the users in the low-voltage transformer area.
2. The method for predicting the electricity consumption preference of a low-voltage station user according to claim 1, wherein the method comprises the following steps:
the external influence factors comprise climate factors, economic factors, time factors, electricity price factors and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover;
the economic factors include regional GDP and average income index;
the time factors include seasonal period, workday period, and holiday period;
the electricity price factors comprise step electricity price and time-of-use electricity price;
the random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above.
3. The method for predicting the electricity consumption preference of a low-voltage station user according to claim 1, wherein the method comprises the following steps:
the power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-to-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load, and a monthly average daily load rate.
4. A method for predicting a user power consumption preference of a low voltage transformer area according to claim 3, wherein: the power load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
5. A low voltage utility power consumption preference prediction system, comprising:
the model construction module is used for constructing a low-voltage transformer area user electricity preference model and comprises an external influence factor analysis module and an internal change rule analysis module;
the external influence factor analysis module is used for selecting a plurality of external influence factors, carrying out characteristic analysis on the power load based on the selected external influence factors, and acquiring the relevance between the external influence factors and the power load fluctuation;
the internal change rule analysis module is used for selecting a plurality of power load characteristic indexes, classifying and analyzing the power load characteristic indexes to acquire the relevance of the power load characteristic indexes and power load fluctuation;
and the prediction module is used for acquiring the electricity consumption data of the users in the transformer area and predicting the electricity consumption preference of the corresponding users based on the constructed low-voltage transformer area user electricity consumption preference model.
6. The low voltage district subscriber electricity preference prediction system according to claim 5 wherein:
the external influence factors comprise climate factors, economic factors, time factors, electricity price factors and random factors;
the climate factors comprise temperature, humidity, wind speed, rainfall and cloud cover;
the economic factors include regional GDP and average income index;
the time factors include seasonal period, workday period, and holiday period;
the electricity price factors comprise step electricity price and time-of-use electricity price;
the random factors include other random events that can cause compliance with the fluctuations and do not fall under the four influencing factors described above.
7. The low voltage district subscriber electricity preference prediction system according to claim 5 wherein:
the power load characteristic index includes a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-to-valley difference, a daily peak Gu Chalv, a daily load curve, a monthly maximum load, a monthly minimum load, a monthly average daily load, and a monthly average daily load rate.
8. The low voltage district subscriber electricity preference prediction system according to claim 7 wherein: the power load characteristic index is specifically classified into a description class, a comparison class, and a curve class.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the low voltage zone user electricity consumption preference prediction method of any of claims 1 to 4 when the program is executed by the processor.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a low voltage district user electricity usage preference prediction method as claimed in any one of claims 1 to 4.
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| CN202310899978.5A CN117134322A (en) | 2023-07-20 | 2023-07-20 | Method, system, equipment and medium for predicting power consumption preference of low-voltage transformer area user |
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