CN116151509A - Power information management method and system based on data fusion - Google Patents
Power information management method and system based on data fusion Download PDFInfo
- Publication number
- CN116151509A CN116151509A CN202310106784.5A CN202310106784A CN116151509A CN 116151509 A CN116151509 A CN 116151509A CN 202310106784 A CN202310106784 A CN 202310106784A CN 116151509 A CN116151509 A CN 116151509A
- Authority
- CN
- China
- Prior art keywords
- data
- power supply
- information
- target area
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a power information management method and a system based on data fusion, which are applied to the technical field of data processing, wherein the method comprises the following steps: historical electricity consumption information of a target area is collected. And acquiring the environmental data of the target area, acquiring an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation of the environmental data set and the historical electricity consumption information. And acquiring real-time environment data, and carrying out power consumption data estimation according to the mapping relation to obtain estimated required power consumption. The region priority coefficient of the target region is obtained. And reading the power supply planning information of the area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient. And carrying out power supply management of the target area through the power supply data. The technical problem that in the prior art, the power information management method is low in intellectualization, and power equipment is damaged due to frequent overload operation of the power equipment in the power utilization peak period is solved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a power information management method and system based on data fusion.
Background
The data fusion refers to analyzing and fusing observation information acquired according to time sequence by using a computer according to a preset criterion so as to complete required decision and evaluation tasks. In the prior art, data fusion is rarely applied to the power information management method, so that the power information management method is low in intellectualization, overload operation of power equipment is often caused in a power consumption peak period, and further the power equipment is damaged, and the power consumption safety of a user is affected.
Therefore, the power information management method in the prior art is low in intellectualization, and the power equipment is often operated in overload in the power consumption peak period, so that the technical problem of damage to the power equipment is solved.
Disclosure of Invention
The power information management method and the system based on data fusion solve the technical problems that in the prior art, the power information management method is low in intellectualization, power equipment is frequently overloaded in the power consumption peak period, and the power equipment is damaged.
The application provides a power information management method based on data fusion, which comprises the following steps: acquiring historical electricity consumption information of a target area; acquiring environmental data of the target area to obtain an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption; collecting real-time environment data of the target area, and carrying out power consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required power consumption; obtaining a region priority coefficient of the target region; reading power supply planning information of an area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient; and carrying out power supply management of the target area through the power supply data.
The application also provides a power information management system based on data fusion, which comprises: the power consumption information acquisition module is used for acquiring historical power consumption information of the target area; the mapping relation acquisition module is used for acquiring environmental data of the target area, acquiring an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption; the estimated required electricity consumption acquisition module is used for acquiring real-time environment data of the target area, and carrying out electricity consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required electricity consumption; a priority coefficient obtaining module, configured to obtain a region priority coefficient of the target region; the power supply data acquisition module is used for reading power supply planning information of an area and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient; and the power supply management module is used for carrying out power supply management on the target area through the power supply data.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the power information management method based on data fusion when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements a power information management method based on data fusion.
The power information management method and the system based on data fusion solve the technical problems that in the prior art, the power information management method is low in intellectualization, power equipment is frequently overloaded in the power consumption peak period, and the power equipment is damaged. The method has the advantages that the electricity consumption in each electricity consumption area is timely adjusted according to actual environment data, the intellectualization of the electric power information management method is improved, overload operation of electric power equipment is avoided, and electricity consumption safety of users is guaranteed.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a power information management method based on data fusion according to an embodiment of the present application;
fig. 2 is a schematic flow chart of acquiring an environmental data set by using a power information management method based on data fusion according to an embodiment of the present application;
fig. 3 is a schematic flow chart of obtaining an evaluation result of adjusting a power supply load according to the power information management method structure based on data fusion provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of a power information management system based on data fusion according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device of a power information management system based on data fusion according to an embodiment of the present invention.
Reference numerals illustrate: the system comprises a power consumption information acquisition module 11, a mapping relation acquisition module 12, an estimated required power consumption acquisition module 13, a priority coefficient acquisition module 14, a power supply data acquisition module 15, a power supply management module 16, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
Example 1
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in this application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a power information management method based on data fusion, where the method includes:
s10: acquiring historical electricity consumption information of a target area;
s20: acquiring environmental data of the target area to obtain an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption;
s30: collecting real-time environment data of the target area, and carrying out power consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required power consumption;
specifically, the historical electricity consumption of the target area is collected in detail, wherein the target area is a corresponding area for carrying out power supply management. And then, acquiring environment data of a target area, acquiring an environment data set, wherein the environment data set comprises various historical environment information in the target area, carrying out data fusion on the environment data set and the historical electricity consumption information, constructing a mapping relation between the environment data and the electricity consumption, and carrying out statistics on the electricity consumption data under each environment and obtaining the average value of the electricity consumption levels under the corresponding environments when the electricity consumption mapping relation is acquired, so that the corresponding electricity consumption data is mapped under each environment. The power consumption situation can be estimated according to the environmental data by carrying out data fusion on the environmental data set and the historical power consumption information and constructing a power consumption mapping relation, so that the power consumption situation in the target area can be conveniently and accurately acquired, and the regulation and the planning of the power consumption are realized. And acquiring real-time environment data of a target area, and carrying out power consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required power consumption.
As shown in fig. 2, the method S20 provided in the embodiment of the present application further includes:
s21: acquiring environmental temperature data of the target area to obtain an environmental temperature data acquisition result;
s22: acquiring environmental humidity data of the target area to obtain an environmental humidity data acquisition result;
s23: forming a natural environment data set according to the environmental temperature data acquisition result and the environmental humidity data acquisition result;
s24: the set of environmental data is obtained based on the set of natural environmental data.
Specifically, the step of collecting the environmental data of the target area specifically includes collecting the historical environmental temperature data in the target area by collecting the environmental temperature data of the target area, and obtaining an environmental temperature data collection result. And acquiring the environmental humidity data of the target area, and acquiring historical humidity temperature data in the target area to obtain an environmental humidity data acquisition result. And then, the environmental temperature data acquisition result and the environmental humidity data acquisition result form a natural environment data set. And obtaining the set of environmental data based on the set of natural environmental data. The specific mapping relation between the environment data and the electricity consumption is conveniently and further constructed later by acquiring the environment data set, so that the electricity consumption condition in the target area can be conveniently and rapidly acquired according to the environment data.
The method S20 provided in the embodiment of the present application further includes:
s25: carrying out enterprise change information statistics on the target area to obtain an enterprise change information statistics result;
s26: obtaining each enterprise information in the enterprise change information statistical result, and generating enterprise change electricity utilization data according to the enterprise information and the enterprise change information statistical result;
s27: performing residential user change statistics on the target area, and generating residential change electricity utilization data according to residential user change statistics results;
s28: constructing an unnatural environment data set according to the enterprise changing electricity data and the residence changing electricity data;
s29: and obtaining the environment data set according to the natural environment data set and the non-natural environment data set.
Specifically, enterprise change information statistics is carried out on a target area, enterprise change data in the target area are obtained, then each enterprise information in enterprise change information statistics results is obtained, enterprise change electricity consumption data are generated according to the enterprise information and the enterprise change information statistics results, and after the enterprise information in the target area is changed, the electricity consumption condition of an enterprise is changed, and the electricity consumption condition of the enterprise after the enterprise is changed in a preset time is obtained, so that accurate obtaining of the electricity consumption condition in the target area is facilitated. And then, carrying out residential user change statistics on the target area, generating residential change electricity consumption data according to residential user change statistics results, acquiring residential user change statistics results because electricity consumption habits after residential user change may be different from previous electricity consumption habits, and monitoring electricity consumption conditions of the users after change within a preset time so as to be convenient for accurately acquiring electricity consumption conditions in the target area. And constructing an unnatural environment data set by changing the power consumption data of enterprises and the power consumption data of residences. And finally, obtaining the environment data set according to the natural environment data set and the non-natural environment data set. The environment data set is obtained by the natural environment data set and the non-natural environment data set, so that the environment data set can more clearly reflect the electricity utilization environment change condition, and the more accurate mapping relation between the environment data and the electricity utilization data can be conveniently constructed later.
The method S30 provided in the embodiment of the present application further includes:
s31: obtaining a time identifier of the historical electricity consumption information;
s32: performing time feature aggregation according to the historical electricity consumption information and the time mark to obtain an aggregation result;
s33: and adding the aggregation result to the matching feature of the mapping relation.
Specifically, a time identifier of the historical electricity consumption information is obtained, then time feature aggregation is carried out according to the historical electricity consumption information and the corresponding time identifier, the electricity consumption information of different time period features is aggregated, and an aggregation result is obtained. Namely, separating time marks in the historical electricity consumption information, and carrying out characteristic classification on the electricity consumption information by taking the time marks as characteristics, wherein each time mark can be specifically daily or hourly, for example, the electricity consumption information from monday to sunday is respectively subjected to characteristic classification, an aggregation result is obtained, and each time mark characteristic corresponds to the electricity consumption information of a plurality of times in the aggregation result. And finally, adding the acquired aggregation result to the matching feature of the mapping relation, namely before building the mapping relation, classifying the mapping relation according to time aggregation and environment data, wherein each mapping group contains corresponding environment data and time data.
The method S30 provided in the embodiment of the present application further includes:
s34: constructing a power consumption estimation model according to the mapping relation and the aggregation result;
s35: acquiring date information;
s36: and inputting the real-time environment data and the date information into the electricity consumption prediction model, and outputting the predicted required electricity consumption.
Specifically, a power consumption estimation model is constructed according to the mapping relation and the aggregation result, and each mapping group contains corresponding environment data and time data. By means of average value calculation of the data in each mapping group, the mapping relation obtained at the moment not only contains specific environmental characteristics, but also has corresponding date information, so that the obtained mapping relation is more accurate, and the construction of the electricity consumption estimation model is completed. The electricity consumption estimation model comprises electricity consumption conditions corresponding to the environmental data and the time characteristics. Then, the date information is acquired, the date information acquired at this time corresponds to the time identifier, and if the time identifier is monday to sunday, the corresponding date information is monday to sunday. And finally, inputting the real-time environment data and the date information into the electricity consumption prediction model, and outputting the predicted required electricity consumption, so that the predicted required electricity consumption is more accurate.
S40: obtaining a region priority coefficient of the target region;
s50: reading power supply planning information of an area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient;
s60: and carrying out power supply management of the target area through the power supply data.
Specifically, the regional priority coefficient of the target region is obtained, the priority coefficient can be divided by different target regions, the priority coefficients with the same or different grades exist in different target regions, and the higher the priority coefficient is, the higher the corresponding electric energy distribution proportion is. And then, reading power supply planning information of an area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient, wherein the power supply data is specifically an actual power supply distribution condition of a certain area, if the power distribution condition of a certain large area is obtained in total and is n kilowatt-hours, the large area comprises A, B two small areas, the priority coefficient of an area A is 4, the priority coefficient of an area B is 6, when the power is distributed, the n kilowatt-hours of the power distribution of a certain large area are distributed according to the corresponding priority coefficient, and if the priority coefficient of 5 is marked, the priority coefficient 4 is the power for reducing the corresponding proportion of the area A, and the power is distributed to the area B. The power supply of the area is planned to be power supply plan data of the corresponding area in the current time period, and the power supply data of the target area obtained through the power supply plan data, the pre-estimated required power consumption information and the area priority coefficient is the maximum power supply data of the current area. And finally, carrying out power supply management on the target area through the power supply data, so that the power consumption in each power consumption area can be timely adjusted according to the actual environment data, the intellectualization of the power information management method is improved, overload operation of power equipment is avoided, and the power consumption safety of a user is ensured.
As shown in fig. 3, the method S60 provided in the embodiment of the present application further includes:
s61: obtaining power supply line information of the target area;
s62: carrying out power supply load evaluation of each circuit in the target area according to the power supply data and the power supply circuit information, and generating a power supply load evaluation result;
s63: generating a fluctuation coefficient according to the ratio of the estimated required electricity consumption to the power supply data;
s64: carrying out fluctuation adjustment on the power supply load evaluation result through the fluctuation coefficient to obtain an adjusted power supply load evaluation result;
s65: and carrying out power supply management according to the power supply load adjustment evaluation result.
Specifically, power supply line information of a target area is obtained, power supply load evaluation of each line in the target area is performed according to power supply data and the power supply line information, whether each area can meet corresponding electric loads under the influence of corresponding power supply data is evaluated, and a power supply load evaluation result is generated, wherein the power supply load evaluation result represents the ratio of each power supply data to the power supply load of the line information. And generating a fluctuation coefficient according to the ratio of the estimated required power consumption to the power supply data, wherein when the fluctuation coefficient is larger than 1, the power supply data cannot meet the corresponding estimated required power consumption, and when the fluctuation coefficient is smaller than or equal to 1, the power supply data can meet the corresponding estimated required power consumption. And then, carrying out fluctuation adjustment on the power supply load evaluation result through the fluctuation coefficient, namely adjusting the estimated power supply load evaluation result according to the fluctuation coefficient, if the power supply load evaluation result is overload at a certain moment, and the corresponding fluctuation coefficient is less than or equal to 1 at the moment, after the power supply load evaluation result is adjusted according to the fluctuation coefficient, the power supply load evaluation result is possibly corrected to be non-overload, so that the optional power supply load evaluation result is more accurate. And finally, carrying out power supply management according to the evaluation result of the power supply load.
The method S65 provided in the embodiment of the present application further includes:
s651: setting a load early warning constraint threshold;
s652: judging whether the power supply load evaluation result meets the load early warning constraint threshold;
s653: when the power supply load evaluation result can meet the load early warning constraint threshold, generating first early warning grade information;
s654: when the power supply load evaluation result cannot meet the load early warning constraint threshold, judging whether the adjusted power supply load evaluation result meets the load early warning constraint threshold;
s655: when the adjusted power supply load evaluation result meets the load early warning constraint threshold, generating second early warning grade information;
s656: and carrying out early warning management on the power supply line of the target area according to the first early warning level information or the second early warning level information.
Specifically, a load early warning constraint threshold is set, and whether the power supply load evaluation result meets the load early warning constraint threshold is judged, wherein the load early warning constraint threshold is an early warning constraint threshold of the duty ratio of the power supply line electric load. And when the power supply load evaluation result can meet the load early warning constraint threshold, generating first early warning grade information when the power supply electric load exceeds the load early warning. And when the power supply load evaluation result cannot meet the load early warning constraint threshold, judging whether the power supply load adjustment evaluation result meets the load early warning constraint threshold. And when the adjusted power supply load evaluation result meets the load early warning constraint threshold, generating second early warning grade information. And carrying out power supply line early warning management of the target area according to the first early warning level information or the second early warning level information, so that overload operation of the power supply line is further avoided, and the electricity utilization safety of a user is ensured.
According to the technical scheme provided by the embodiment of the invention, the historical electricity consumption information of the target area is acquired. And acquiring environmental data of the target area to obtain an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption. And acquiring real-time environment data of the target area, and carrying out power consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required power consumption. And obtaining the region priority coefficient of the target region. And reading power supply planning information of the area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient. And carrying out power supply management of the target area through the power supply data. The technical problem that in the prior art, the power information management method is low in intellectualization, and power equipment is damaged due to frequent overload operation of the power equipment in the power utilization peak period is solved. The method has the advantages that the electricity consumption in each electricity consumption area is timely adjusted according to actual environment data, the intellectualization of the electric power information management method is improved, overload operation of electric power equipment is avoided, and electricity consumption safety of users is guaranteed.
Example two
Based on the same inventive concept as the power information management method based on data fusion in the foregoing embodiments, the present invention also provides a power information management system based on data fusion, where the system may be implemented by hardware and/or software, and may generally be integrated in an electronic device, for executing the method provided by any embodiment of the present invention. As shown in fig. 4, the system includes:
the electricity consumption information acquisition module 11 is used for acquiring historical electricity consumption information of the target area;
the mapping relation acquisition module 12 is configured to perform environmental data acquisition on the target area to obtain an environmental data set, perform data fusion on the environmental data set and the historical electricity consumption information, and construct a mapping relation between environmental data and electricity consumption;
the estimated required electricity consumption acquisition module 13 is used for acquiring real-time environment data of the target area, and estimating electricity consumption data according to the real-time environment data and the mapping relation to obtain estimated required electricity consumption;
a priority coefficient obtaining module 14, configured to obtain a region priority coefficient of the target region;
the power supply data acquisition module 15 is configured to read power supply planning information of an area, and generate power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient;
and the power supply management module 16 is used for carrying out power supply management of the target area through the power supply data.
Further, the mapping relationship obtaining module 12 is further configured to:
acquiring environmental temperature data of the target area to obtain an environmental temperature data acquisition result;
acquiring environmental humidity data of the target area to obtain an environmental humidity data acquisition result;
forming a natural environment data set according to the environmental temperature data acquisition result and the environmental humidity data acquisition result;
the set of environmental data is obtained based on the set of natural environmental data.
Further, the mapping relationship obtaining module 12 is further configured to:
carrying out enterprise change information statistics on the target area to obtain an enterprise change information statistics result;
obtaining each enterprise information in the enterprise change information statistical result, and generating enterprise change electricity utilization data according to the enterprise information and the enterprise change information statistical result;
performing residential user change statistics on the target area, and generating residential change electricity utilization data according to residential user change statistics results;
constructing an unnatural environment data set according to the enterprise changing electricity data and the residence changing electricity data;
and obtaining the environment data set according to the natural environment data set and the non-natural environment data set.
Further, the estimated required power consumption obtaining module 13 is further configured to:
obtaining power supply line information of the target area;
carrying out power supply load evaluation of each circuit in the target area according to the power supply data and the power supply circuit information, and generating a power supply load evaluation result;
generating a fluctuation coefficient according to the ratio of the estimated required electricity consumption to the power supply data;
carrying out fluctuation adjustment on the power supply load evaluation result through the fluctuation coefficient to obtain an adjusted power supply load evaluation result;
and carrying out power supply management according to the power supply load adjustment evaluation result.
Further, the estimated required power consumption obtaining module 13 is further configured to:
setting a load early warning constraint threshold;
judging whether the power supply load evaluation result meets the load early warning constraint threshold;
when the power supply load evaluation result can meet the load early warning constraint threshold, generating first early warning grade information;
when the power supply load evaluation result cannot meet the load early warning constraint threshold, judging whether the adjusted power supply load evaluation result meets the load early warning constraint threshold;
when the adjusted power supply load evaluation result meets the load early warning constraint threshold, generating second early warning grade information;
and carrying out early warning management on the power supply line of the target area according to the first early warning level information or the second early warning level information.
Further, the power management module 16 is further configured to:
obtaining a time identifier of the historical electricity consumption information;
performing time feature aggregation according to the historical electricity consumption information and the time mark to obtain an aggregation result;
and adding the aggregation result to the matching feature of the mapping relation.
Further, the power management module 16 is further configured to:
constructing a power consumption estimation model according to the mapping relation and the aggregation result;
acquiring date information;
and inputting the real-time environment data and the date information into the electricity consumption prediction model, and outputting the predicted required electricity consumption.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to a power information management method based on data fusion in an embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e., implements a data fusion-based power information management method as described above.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. 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, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method for managing power information based on data fusion, the method comprising:
acquiring historical electricity consumption information of a target area;
acquiring environmental data of the target area to obtain an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption;
collecting real-time environment data of the target area, and carrying out power consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required power consumption;
obtaining a region priority coefficient of the target region;
reading power supply planning information of an area, and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient;
and carrying out power supply management of the target area through the power supply data.
2. The method of claim 1, wherein the method comprises:
acquiring environmental temperature data of the target area to obtain an environmental temperature data acquisition result;
acquiring environmental humidity data of the target area to obtain an environmental humidity data acquisition result;
forming a natural environment data set according to the environmental temperature data acquisition result and the environmental humidity data acquisition result;
the set of environmental data is obtained based on the set of natural environmental data.
3. The method according to claim 2, wherein the method comprises:
carrying out enterprise change information statistics on the target area to obtain an enterprise change information statistics result;
obtaining each enterprise information in the enterprise change information statistical result, and generating enterprise change electricity utilization data according to the enterprise information and the enterprise change information statistical result;
performing residential user change statistics on the target area, and generating residential change electricity utilization data according to residential user change statistics results;
constructing an unnatural environment data set according to the enterprise changing electricity data and the residence changing electricity data;
and obtaining the environment data set according to the natural environment data set and the non-natural environment data set.
4. The method of claim 1, wherein the method comprises:
obtaining power supply line information of the target area;
carrying out power supply load evaluation of each circuit in the target area according to the power supply data and the power supply circuit information, and generating a power supply load evaluation result;
generating a fluctuation coefficient according to the ratio of the estimated required electricity consumption to the power supply data;
carrying out fluctuation adjustment on the power supply load evaluation result through the fluctuation coefficient to obtain an adjusted power supply load evaluation result;
and carrying out power supply management according to the power supply load adjustment evaluation result.
5. The method of claim 4, wherein the method comprises:
setting a load early warning constraint threshold;
judging whether the power supply load evaluation result meets the load early warning constraint threshold;
when the power supply load evaluation result can meet the load early warning constraint threshold, generating first early warning grade information;
when the power supply load evaluation result cannot meet the load early warning constraint threshold, judging whether the adjusted power supply load evaluation result meets the load early warning constraint threshold;
when the adjusted power supply load evaluation result meets the load early warning constraint threshold, generating second early warning grade information;
and carrying out early warning management on the power supply line of the target area according to the first early warning level information or the second early warning level information.
6. The method of claim 1, wherein the method comprises:
obtaining a time identifier of the historical electricity consumption information;
performing time feature aggregation according to the historical electricity consumption information and the time mark to obtain an aggregation result;
and adding the aggregation result to the matching feature of the mapping relation.
7. The method of claim 6, wherein the method comprises:
constructing a power consumption estimation model according to the mapping relation and the aggregation result;
acquiring date information;
and inputting the real-time environment data and the date information into the electricity consumption prediction model, and outputting the predicted required electricity consumption.
8. A data fusion-based power information management system, the system comprising:
the power consumption information acquisition module is used for acquiring historical power consumption information of the target area;
the mapping relation acquisition module is used for acquiring environmental data of the target area, acquiring an environmental data set, carrying out data fusion on the environmental data set and the historical electricity consumption information, and constructing a mapping relation between the environmental data and the electricity consumption;
the estimated required electricity consumption acquisition module is used for acquiring real-time environment data of the target area, and carrying out electricity consumption data estimation according to the real-time environment data and the mapping relation to obtain estimated required electricity consumption;
a priority coefficient obtaining module, configured to obtain a region priority coefficient of the target region;
the power supply data acquisition module is used for reading power supply planning information of an area and generating power supply data of the target area according to the power supply planning information, the estimated required power consumption information and the area priority coefficient;
and the power supply management module is used for carrying out power supply management on the target area through the power supply data.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a data fusion based power information management method according to any one of claims 1 to 7 when executing executable instructions stored in said memory.
10. A computer readable medium having stored thereon a computer program, which when executed by a processor implements a data fusion based power information management method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310106784.5A CN116151509A (en) | 2023-02-13 | 2023-02-13 | Power information management method and system based on data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310106784.5A CN116151509A (en) | 2023-02-13 | 2023-02-13 | Power information management method and system based on data fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116151509A true CN116151509A (en) | 2023-05-23 |
Family
ID=86361495
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310106784.5A Pending CN116151509A (en) | 2023-02-13 | 2023-02-13 | Power information management method and system based on data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116151509A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631545A (en) * | 2015-12-25 | 2016-06-01 | 河海大学常州校区 | Photovoltaic power station generation capacity prediction method based on similar day analysis and prediction system thereof |
| CN106529747A (en) * | 2017-01-04 | 2017-03-22 | 成都四方伟业软件股份有限公司 | Power load predicting method and system based on large data |
| CN106651200A (en) * | 2016-12-29 | 2017-05-10 | 中国西电电气股份有限公司 | Electrical load management method and system for industrial enterprise aggregate user |
| CN110850199A (en) * | 2019-10-28 | 2020-02-28 | 国网福建省电力有限公司 | Power distribution network line heavy overload monitoring method |
| CN112508306A (en) * | 2020-12-28 | 2021-03-16 | 深圳供电局有限公司 | Self-adaptive method and system for power production configuration |
| CN113361776A (en) * | 2021-06-08 | 2021-09-07 | 国网上海市电力公司 | Power load probability prediction method based on user power consumption behavior clustering |
| CN114693006A (en) * | 2022-06-01 | 2022-07-01 | 天津市普迅电力信息技术有限公司 | Power distribution and energy storage management system and method for novel power system |
| CN115186944A (en) * | 2022-09-15 | 2022-10-14 | 合肥优晟电力科技有限公司 | Urban power distribution network planning method and system |
| CN115511656A (en) * | 2022-05-30 | 2022-12-23 | 国网辽宁省电力有限公司电力科学研究院 | Demand planning auxiliary decision system based on mining power grid data value |
-
2023
- 2023-02-13 CN CN202310106784.5A patent/CN116151509A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631545A (en) * | 2015-12-25 | 2016-06-01 | 河海大学常州校区 | Photovoltaic power station generation capacity prediction method based on similar day analysis and prediction system thereof |
| CN106651200A (en) * | 2016-12-29 | 2017-05-10 | 中国西电电气股份有限公司 | Electrical load management method and system for industrial enterprise aggregate user |
| CN106529747A (en) * | 2017-01-04 | 2017-03-22 | 成都四方伟业软件股份有限公司 | Power load predicting method and system based on large data |
| CN110850199A (en) * | 2019-10-28 | 2020-02-28 | 国网福建省电力有限公司 | Power distribution network line heavy overload monitoring method |
| CN112508306A (en) * | 2020-12-28 | 2021-03-16 | 深圳供电局有限公司 | Self-adaptive method and system for power production configuration |
| CN113361776A (en) * | 2021-06-08 | 2021-09-07 | 国网上海市电力公司 | Power load probability prediction method based on user power consumption behavior clustering |
| CN115511656A (en) * | 2022-05-30 | 2022-12-23 | 国网辽宁省电力有限公司电力科学研究院 | Demand planning auxiliary decision system based on mining power grid data value |
| CN114693006A (en) * | 2022-06-01 | 2022-07-01 | 天津市普迅电力信息技术有限公司 | Power distribution and energy storage management system and method for novel power system |
| CN115186944A (en) * | 2022-09-15 | 2022-10-14 | 合肥优晟电力科技有限公司 | Urban power distribution network planning method and system |
Non-Patent Citations (1)
| Title |
|---|
| 赵嘉承;林仁;王黎明;: "基于大数据融合的电力安全智能监测与预警平台应用研究", 电脑知识与技术, no. 33 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system | |
| Zhou et al. | Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach | |
| US10223167B2 (en) | Discrete resource management | |
| CN111680841B (en) | Short-term load prediction method, system and terminal equipment based on principal component analysis | |
| CN102982489A (en) | Power customer online grouping method based on mass measurement data | |
| Tang et al. | Efficient scenario generation of multiple renewable power plants considering spatial and temporal correlations | |
| CN109035067A (en) | Building energy consumption processing method and processing device based on RF and ARMA algorithm | |
| CN116227757B (en) | Comprehensive energy management and control method and system based on intelligent cloud gateway | |
| CN108346009B (en) | Power production configuration method and device based on user model self-learning | |
| CN111260505B (en) | Big data analysis method and device based on power Internet of things and computer equipment | |
| CN115730749B (en) | Power dispatching risk early warning method and device based on fusion power data | |
| CN112508306A (en) | Self-adaptive method and system for power production configuration | |
| CN111738529A (en) | Comprehensive energy system demand response method, system and equipment based on reinforcement learning | |
| Zhang et al. | Multi-objective interval prediction of wind power based on conditional copula function | |
| Lizondo et al. | Multiagent model for distributed peak shaving system with demand-side management approach | |
| Rajanarayan Prusty et al. | Uncertainty modeling steps for probabilistic steady-state analysis | |
| CN116151509A (en) | Power information management method and system based on data fusion | |
| Nesti et al. | Temperature overloads in power grids under uncertainty: A large deviations approach | |
| CN112036634A (en) | Photovoltaic power generation power determination method, prediction system and readable storage medium | |
| CN116470491A (en) | Photovoltaic power probability prediction method and system based on copula function | |
| CN112488416B (en) | Power grid operation situation monitoring method and system | |
| CN115130764A (en) | Power distribution network situation prediction method and system based on state evaluation | |
| CN112330017A (en) | Power load prediction method, power load prediction device, electronic device, and storage medium | |
| Kumar et al. | Wireless sensor network using control communication and monitoring of smart grid | |
| CN118017554A (en) | Capacity expansion method, device and equipment of distribution transformer and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |