CN114021294A - Energy operation load prediction and early warning method - Google Patents
Energy operation load prediction and early warning method Download PDFInfo
- Publication number
- CN114021294A CN114021294A CN202111283650.8A CN202111283650A CN114021294A CN 114021294 A CN114021294 A CN 114021294A CN 202111283650 A CN202111283650 A CN 202111283650A CN 114021294 A CN114021294 A CN 114021294A
- Authority
- CN
- China
- Prior art keywords
- water
- load
- pipe network
- data
- model
- 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
- 238000000034 method Methods 0.000 title claims abstract description 36
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 207
- 239000008235 industrial water Substances 0.000 claims abstract description 11
- 238000005265 energy consumption Methods 0.000 claims abstract description 7
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 230000000007 visual effect Effects 0.000 claims description 34
- 238000009826 distribution Methods 0.000 claims description 18
- 238000011161 development Methods 0.000 claims description 15
- 238000010586 diagram Methods 0.000 claims description 10
- 238000012800 visualization Methods 0.000 claims description 10
- 239000008400 supply water Substances 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 8
- 230000004927 fusion Effects 0.000 claims description 8
- 238000007726 management method Methods 0.000 claims description 8
- IQLZWWDXNXZGPK-UHFFFAOYSA-N methylsulfonyloxymethyl methanesulfonate Chemical compound CS(=O)(=O)OCOS(C)(=O)=O IQLZWWDXNXZGPK-UHFFFAOYSA-N 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 6
- 238000013499 data model Methods 0.000 claims description 6
- 239000002131 composite material Substances 0.000 claims description 5
- 239000013589 supplement Substances 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims description 3
- 239000000969 carrier Substances 0.000 claims description 2
- 230000005855 radiation Effects 0.000 claims description 2
- 238000009966 trimming Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000012512 characterization method Methods 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 230000006872 improvement Effects 0.000 description 9
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 230000002650 habitual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
-
- 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
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an energy operation load prediction and early warning method, which relates to the technical field of energy prediction and comprises the following steps: collecting data, dividing regions, judging water use load value variables, analyzing water energy demand variables, constructing a model, optimizing the model, predicting the load, monitoring variables and carrying out abnormity early warning; the water supply network is divided into a first network area and a second network area according to the difference of residential water and commercial industrial water, a water load value variable is obtained through calculation by collecting population, social fixed asset investment amount, per-capita water consumption expenditure, residential consumption price index, commercial industrial unit water consumption expenditure and characterization area commercial industrial investment factors, the water load value variable is obtained through analysis by matching with GDP, urbanization rate and water energy consumption proportion data, the variable is assigned to each area in the model, the range of future load values is determined, the load historical data is fitted to obtain the predicted value of the future water supply load, and planning is convenient.
Description
Technical Field
The invention relates to the technical field of energy prediction, in particular to an energy operation load prediction and early warning method.
Background
The water source is a general term of water source and existence form regions, the water source is a life source, an important medium for material, information and energy transfer and is an irreplaceable resource for living organisms on the surface of the earth, the water source mainly exists in oceans, rivers and lakes, glacier snow mountains and other regions, the water source is updated through atmospheric movement and other forms, and the water source is a precious resource which can not be separated by human beings in production and living activities and is also one of energy sources on which the human beings live;
with the continuous expansion of urban scale, a large amount of investment is used for the construction of a water supply network, in water conservancy and water utilities, water supply load judgment is important work in water utilities and is an important basis for formulating water supply development plans, and the prediction accuracy directly influences the quality of urban water supply development.
Disclosure of Invention
Aiming at the problems, the invention provides an energy operation load prediction and early warning method, which is convenient for systematically predicting the future water supply load and planning early, so that the development of an urban water supply system follows the rhythm of the urban development.
In order to realize the purpose of the invention, the invention is realized by the following technical scheme: an energy operation load prediction and early warning method comprises the following steps:
the method comprises the following steps: collecting data
Acquiring distribution diagram data of a water supply pipeline in a municipal system, recording detailed coordinates, and processing the distribution diagram data into effective data;
step two: dividing regions
Dividing a water supply pipeline into a first pipe network area and a second pipe network area, wherein the first pipe network area is divided into residential water for residents to meet the resident load, and the second pipe network area is set into commercial industrial water to meet the commercial industrial water;
step three: determining water load value variable
Collecting actual values of the highest daily water load in one year in a first pipe network area and a second pipe network area and the natural growth rate of the annual water load in 2-3 years, collecting regional development and resident life variables as supplements to determine the influence of high-consumption resident users and newly-increased unit users on the water load in the first pipe network area and the second pipe network area, calculating and predicting the annual water load value, and judging the water load value variable;
step four: analyzing hydraulic energy demand variables
Collecting consumption and urbanization rate data of the first pipe network area and the second pipe network area within 2-3 years, obtaining variables of the consumption and urbanization rate data, and analyzing the variables and the water load value variables to obtain water energy demand variables;
step five: building models
Building a data model, inputting the effective data in the step one into the model, associating coordinate information and distribution map attributes, and building a water supply network overall model;
step six: model optimization
Fine trimming and reconstructing a first pipe network area model and a second pipe network area model in the water supply pipe network overall model to complete the ground feature elements, including the regional water supply meters, and then cutting the model to obtain a visual model;
step seven: load prediction
Assigning values to each region in the visual model according to regional development and resident life variables collected in the third step and consumption and urbanization rate data collected in the fourth step and variables of annual water use load values, predicting each variable according to the values, and obtaining a predicted value of future water supply load according to a fitting method;
step eight: variable monitoring
Collecting the flow of the water supply water meters in the first pipe network area and the second pipe network area, constructing a water consumption variable line graph of each water supply water meter in each time period by taking time as a variable, determining the area time flow range and the area conventional water time range of the radiation area of the water supply water meters, and inputting the ranges into a visualization model;
step nine: anomaly early warning
And (3) accessing the water meters in the first pipe network area and the second pipe network area into each water meter element in the visual model, and comparing the detection value data of each water meter with the time flow range of each area and the time range of the water used in each area respectively to remind abnormality.
The further improvement lies in that: in the first step, the process of profile data processing is as follows: and (3) inputting the distribution diagram data of the water supply pipeline into a metadata management system MDMS, describing a data set in the metadata management system MDMS through a rule protocol, and taking out the processed effective data.
The further improvement lies in that: in the third step, collecting regional development and resident life variables as supplements, wherein collecting the specific variables of the first pipe network region is as follows: the investment amount of population and social fixed assets, the per-capita water expenditure and the consumption price index of residents; the specific variables for collecting the second pipe network area are as follows: and the water expenditure of the commercial industrial unit characterizes the investment factor of the regional commercial industry.
The further improvement lies in that: in the third step, after determining the influence of the high-consumption resident users and the newly added unit users in the first pipe network area and the second pipe network area on the water load, establishing a load prediction formula according to the data:
predicting the load value of a certain year, namely the actual value of the highest daily load of the last year, x (1+ natural annual load growth rate) + the newly increased user load;
the annual water load value is calculated and predicted according to the water consumption value, and the water load value variable is judged.
The further improvement lies in that: and in the fourth step, the consumption and urbanization rate data comprise GDP, urbanization rate and water energy consumption proportion data, and in the fourth step, after the water energy demand variable is obtained, the water energy load variable of 3-5 year-old years is predicted, and the predicted value is compared with the actual value to judge the calculated reliability.
The further improvement lies in that: in the fifth step, the concrete process of constructing the water supply network overall model is as follows: based on an EXIF principle of exchangeable image files, digital images are used as carriers to fuse spatial confidence and general form attributes to construct a data model, effective data in the first step are input into the model, the model embeds associated coordinate information and distribution map attributes into a physical structure of the digital images, then through a multi-sequencing improved SNM algorithm, similar repeated data in the effective data are cleaned to obtain accurate, clear and visual data fusion, then the data fusion is led into GML to realize visualization of coordinate space data, meanwhile, SVG is used for carrying out space-time data vectorization to form points, lines and planes and form specific data coordinates, and a water supply network overall model is constructed.
The further improvement lies in that: in the sixth step, the overall model is divided and cut according to the division of the first pipe network area and the second pipe network area, and each internal element is individualized to obtain the visual model.
The further improvement lies in that: in the seventh step, the fitting method specifically comprises the following steps: determining the ranges of future load values of the first pipe network area and the second pipe network area by taking the growth rate of each variable as a reference and the influence of the growth rate of each variable on the composite variation of water supply, and fitting the historical load data of each area to obtain the predicted value of the future water supply load
The further improvement lies in that: and seventhly, after the predicted value of the future water supply load is obtained, inputting the predicted value into the visual model in different areas, visualizing each area according to different load values, and then networking the visual model for inquiring the predicted value of the future water supply load of each area.
The further improvement lies in that: and step nine, accessing a remote communication module in each water meter of the first pipe network area and the second pipe network area, accessing the remote communication module into each water meter element in the visual model, synchronously receiving detection value data of each water meter by using each water meter element, comparing the detection value data with the time flow range of each area and the time range of the water used in each area respectively, and when the numerical value is abnormal, carrying out highlight reminding on the area in the visual model and displaying corresponding coordinates.
The invention has the beneficial effects that:
1. the invention obtains the distribution map data of the water supply pipeline, divides a first pipe network area into a first pipe network area and a second pipe network area according to the difference of residential water and commercial industrial water, respectively predicts to improve the pertinence of the prediction, calculates and obtains water load value variables by collecting population, social fixed asset investment amount, per capita water expenditure, residential consumption price index, commercial industrial unit water expenditure and characterization area commercial industrial investment factors, analyzes and obtains water energy demand variables by matching GDP, urbanization rate and water energy consumption proportion data within 2-3 years, simultaneously constructs a visual model, assigns values to each area in the model by each variable obtained by analysis and calculation, uses the growth rate of the previous annual variable as reference, determines the future load value range of the first pipe network area and the second pipe network area by the influence of the growth rate of each variable on the water supply composite variable, and then, the historical load data of each area is fitted to obtain the predicted value of the future water supply load, so that the future water supply load can be predicted systematically, planning can be performed early and the development of the urban water supply system can keep pace with the development of the urban water supply system.
2. According to the method, after the predicted value of the future water supply load is obtained through analysis, the predicted value is input into the visualization model in a regional mode, each region is visualized according to different load values, the visualization model is networked and used for inquiring the predicted value of the future water supply load of each region, and a worker can conveniently check and plan the water supply load in the future.
3. The method collects the flow of the water supply meters, takes time as a variable, constructs a water consumption variable reflection graph of each water supply meter in each time period, determines the regional time flow range and the regional water use time range of the water supply meter, accesses the data of each water meter into each water meter element in the visual model, compares the data with the regional time flow range and the regional water use time range respectively, and carries out highlight reminding and displays corresponding coordinates on the region in the visual model when numerical value abnormity occurs, thereby facilitating real-time monitoring and quickly positioning abnormity.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
Example one
As shown in fig. 1, the embodiment provides an energy operation load prediction and early warning method, which includes the following steps:
the method comprises the following steps: collecting data
Acquiring distribution diagram data of a water supply pipeline in a municipal system, recording detailed coordinates, inputting the distribution diagram data of the water supply pipeline into a metadata management system MDMS, describing a data set in the metadata management system MDMS through a rule protocol, and taking out processed effective data;
step two: dividing regions
Dividing a water supply pipeline into a first pipe network area and a second pipe network area, wherein the first pipe network area is divided into residential water for residents to meet the resident load, and the second pipe network area is set into commercial industrial water to meet the commercial industrial water;
step three: determining water load value variable
Collecting actual values of the annual highest daily water load in a first pipe network area and a second pipe network area and the natural growth rate of the annual water load in 2-3 years, collecting regional development and resident life variables as supplements, wherein the specific variables of the first pipe network area are collected as follows: the investment amount of population and social fixed assets, the per-capita water expenditure and the consumption price index of residents; the specific variables for collecting the second pipe network area are as follows: the method comprises the following steps of determining the influence of high-consumption resident users and newly-added unit users on water load in a first pipe network area and a second pipe network area by the water consumption expenditure of a commercial industrial unit and the representation of regional commercial industrial investment factors, and then establishing a load prediction formula according to the data:
predicting the load value of a certain year, namely the actual value of the highest daily load of the last year, x (1+ natural annual load growth rate) + the newly increased user load;
calculating and predicting annual water load value and judging water load value variable;
step four: analyzing hydraulic energy demand variables
Collecting GDP, urbanization rate and water energy consumption proportion data in 2-3 years of a first pipe network area and a second pipe network area, obtaining the variables of GDP, urbanization rate and water energy consumption proportion, analyzing the variables and water load value variables to obtain water energy demand variables, predicting the water energy load variables of 3-5 year calendar history years, and comparing predicted values with actual values to judge the calculated reliability;
step five: building models
Based on an EXIF principle of exchangeable image files, a digital image is used as a carrier to fuse spatial confidence and general form attributes to construct a data model, effective data in the first step are input into the model, the model embeds associated coordinate information and distribution map attributes into a physical structure of the digital image, then an SNM algorithm is improved through multiple sequencing to clean similar repeated data in the effective data to obtain accurate, clear and visual data fusion, then the data fusion is led into a GML to realize visualization of coordinate space data, and simultaneously SVG is used for carrying out space-time data vectorization to form points, lines and planes and form specific data coordinates to construct a water supply network overall model;
step six: model optimization
The method comprises the steps of carrying out fine modification and reconstruction on a first pipe network region model and a second pipe network region model in a water supply pipe network overall model to enable ground feature elements to be complete and include regional water supply meters, dividing and cutting the overall model according to division of the first pipe network region and the second pipe network region, and realizing singleization of all internal elements to obtain a visual model;
step seven: load prediction
The population, the investment amount of the social fixed assets, the per capita water expenditure, the resident consumption price index, the water expenditure of the commercial industry unit and the characterization area commercial industry investment factors collected in the third step; and fourthly, assigning values to each region in the visual model by matching with the variables of the annual water load value, predicting each variable, taking the previous annual variable growth rate as a reference, determining the range of the future load values of the first pipe network region and the second pipe network region according to the influence of each variable growth rate on the water supply composite variable quantity, fitting the load historical data of each region to obtain the predicted value of the future water supply load, inputting the predicted value into the visual model in different regions after obtaining the predicted value of the future water supply load, visualizing each region according to different load values, and networking the visual model for inquiring the predicted value of the future water supply load of each region.
Example two
As shown in fig. 1, the embodiment provides an energy operation load prediction and early warning method, which includes the following steps:
the method comprises the following steps: collecting data
Acquiring distribution diagram data of a water supply pipeline in a municipal system, recording detailed coordinates, inputting the distribution diagram data of the water supply pipeline into a metadata management system MDMS, describing a data set in the metadata management system MDMS through a rule protocol, and taking out processed effective data;
step two: dividing regions
Dividing a water supply pipeline into a first pipe network area and a second pipe network area, wherein the first pipe network area is divided into residential water for residents to meet the resident load, and the second pipe network area is set into commercial industrial water to meet the commercial industrial water;
step three: building models
Based on an EXIF principle of exchangeable image files, a digital image is used as a carrier to fuse spatial confidence and general form attributes to construct a data model, effective data in the first step are input into the model, the model embeds associated coordinate information and distribution map attributes into a physical structure of the digital image, then an SNM algorithm is improved through multiple sequencing to clean similar repeated data in the effective data to obtain accurate, clear and visual data fusion, then the data fusion is led into a GML to realize visualization of coordinate space data, and simultaneously SVG is used for carrying out space-time data vectorization to form points, lines and planes and form specific data coordinates to construct a water supply network overall model;
step four: model optimization
The method comprises the steps of carrying out fine modification and reconstruction on a first pipe network region model and a second pipe network region model in a water supply pipe network overall model to enable ground feature elements to be complete and include regional water supply meters, dividing and cutting the overall model according to division of the first pipe network region and the second pipe network region, and realizing singleization of all internal elements to obtain a visual model;
step five: variable monitoring
Collecting the flow of the water supply water meters in the first pipe network area and the second pipe network area, constructing a water consumption variable reflection graph of each water supply water meter in each time period by taking time as a variable, determining the area time flow range and the area conventional water time range of the water supply water meter, and inputting the ranges into a visual model;
step six: anomaly early warning
The method comprises the steps of accessing a remote communication module in each water meter of a first pipe network area and a second pipe network area, accessing the remote communication module into each water meter element in a visual model, synchronously receiving detection value data of each water meter by using each water meter element, comparing the detection value data with a time flow range of each area and a time range of water used in each area respectively, and when numerical values are abnormal, carrying out highlight reminding on the areas in the visual model and displaying corresponding coordinates.
The invention obtains the distribution map data of the water supply pipeline, divides a first pipe network area into a first pipe network area and a second pipe network area according to the difference of residential water and commercial industrial water, respectively predicts to improve the pertinence of the prediction, calculates and obtains water load value variables by collecting population, social fixed asset investment amount, per capita water expenditure, residential consumption price index, commercial industrial unit water expenditure and characterization area commercial industrial investment factors, analyzes and obtains water energy demand variables by matching GDP, urbanization rate and water energy consumption proportion data within 2-3 years, simultaneously constructs a visual model, assigns values to each area in the model by each variable obtained by analysis and calculation, uses the growth rate of the previous annual variable as reference, determines the future load value range of the first pipe network area and the second pipe network area by the influence of the growth rate of each variable on the water supply composite variable, and then, the historical load data of each area is fitted to obtain the predicted value of the future water supply load, so that the future water supply load can be predicted systematically, planning can be performed early and the development of the urban water supply system can keep pace with the development of the urban water supply system. In addition, after the predicted value of the future water supply load is obtained through analysis, the predicted value is input into the visualization model in different areas, each area is visualized according to different load values, and the visualization model is networked and used for inquiring the predicted value of the future water supply load of each area, so that a worker can conveniently check and plan the water supply load in the future. Finally, the flow of the water supply meters is collected, the time is used as a variable, a water use variable reflection graph of each water supply meter in each time period is constructed, the regional time flow range and the regional habitual water time range of the water supply meter are determined, the data of each water meter is accessed into each water meter element in the visual model and is compared with the regional time flow range and the regional habitual water time range respectively, when numerical value abnormality occurs, the regional data is highlighted and displayed with corresponding coordinates in the visual model, real-time monitoring is facilitated, and the abnormality is quickly located.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. An energy operation load prediction and early warning method is characterized by comprising the following steps:
the method comprises the following steps: collecting data
Acquiring distribution diagram data of a water supply pipeline in a municipal system, recording detailed coordinates, and processing the distribution diagram data into effective data;
step two: dividing regions
Dividing a water supply pipeline into a first pipe network area and a second pipe network area, wherein the first pipe network area is divided into residential water for residents to meet the resident load, and the second pipe network area is set into commercial industrial water to meet the commercial industrial water;
step three: determining water load value variable
Collecting actual values of the highest daily water load in one year in a first pipe network area and a second pipe network area and the natural growth rate of the annual water load in 2-3 years, collecting regional development and resident life variables as supplements to determine the influence of high-consumption resident users and newly-increased unit users on the water load in the first pipe network area and the second pipe network area, calculating and predicting the annual water load value, and judging the water load value variable;
step four: analyzing hydraulic energy demand variables
Collecting consumption and urbanization rate data of the first pipe network area and the second pipe network area within 2-3 years, obtaining variables of the consumption and urbanization rate data, and analyzing the variables and the water load value variables to obtain water energy demand variables;
step five: building models
Building a data model, inputting the effective data in the step one into the model, associating coordinate information and distribution map attributes, and building a water supply network overall model;
step six: model optimization
Fine trimming and reconstructing a first pipe network area model and a second pipe network area model in the water supply pipe network overall model to complete the ground feature elements, including the regional water supply meters, and then cutting the model to obtain a visual model;
step seven: load prediction
Assigning values to each region in the visual model according to regional development and resident life variables collected in the third step and consumption and urbanization rate data collected in the fourth step and variables of annual water use load values, predicting each variable according to the values, and obtaining a predicted value of future water supply load according to a fitting method;
step eight: variable monitoring
Collecting the flow of the water supply water meters in the first pipe network area and the second pipe network area, constructing a water consumption variable line graph of each water supply water meter in each time period by taking time as a variable, determining the area time flow range and the area conventional water time range of the radiation area of the water supply water meters, and inputting the ranges into a visualization model;
step nine: anomaly early warning
And (3) accessing the water meters in the first pipe network area and the second pipe network area into each water meter element in the visual model, and comparing the detection value data of each water meter with the time flow range of each area and the time range of the water used in each area respectively to remind abnormality.
2. The energy operation load prediction and early warning method according to claim 1, characterized in that: in the first step, the process of profile data processing is as follows: and (3) inputting the distribution diagram data of the water supply pipeline into a metadata management system MDMS, describing a data set in the metadata management system MDMS through a rule protocol, and taking out the processed effective data.
3. The energy operation load prediction and early warning method according to claim 1, characterized in that: in the third step, collecting regional development and resident life variables as supplements, wherein collecting the specific variables of the first pipe network region is as follows: the investment amount of population and social fixed assets, the per-capita water expenditure and the consumption price index of residents; the specific variables for collecting the second pipe network area are as follows: and the water expenditure of the commercial industrial unit characterizes the investment factor of the regional commercial industry.
4. The energy operation load prediction and early warning method according to claim 3, characterized in that: in the third step, after determining the influence of the high-consumption resident users and the newly added unit users in the first pipe network area and the second pipe network area on the water load, establishing a load prediction formula according to the data:
predicting the load value of a certain year, namely the actual value of the highest daily load of the last year, x (1+ natural annual load growth rate) + the newly increased user load;
the annual water load value is calculated and predicted according to the water consumption value, and the water load value variable is judged.
5. The energy operation load prediction and early warning method according to claim 1, characterized in that: and in the fourth step, the consumption and urbanization rate data comprise GDP, urbanization rate and water energy consumption proportion data, and in the fourth step, after the water energy demand variable is obtained, the water energy load variable of 3-5 year-old years is predicted, and the predicted value is compared with the actual value to judge the calculated reliability.
6. The energy operation load prediction and early warning method according to claim 1, characterized in that: in the fifth step, the concrete process of constructing the water supply network overall model is as follows: based on an EXIF principle of exchangeable image files, digital images are used as carriers to fuse spatial confidence and general form attributes to construct a data model, effective data in the first step are input into the model, the model embeds associated coordinate information and distribution map attributes into a physical structure of the digital images, then through a multi-sequencing improved SNM algorithm, similar repeated data in the effective data are cleaned to obtain accurate, clear and visual data fusion, then the data fusion is led into GML to realize visualization of coordinate space data, meanwhile, SVG is used for carrying out space-time data vectorization to form points, lines and planes and form specific data coordinates, and a water supply network overall model is constructed.
7. The energy operation load prediction and early warning method according to claim 1, characterized in that: in the sixth step, the overall model is divided and cut according to the division of the first pipe network area and the second pipe network area, and each internal element is individualized to obtain the visual model.
8. The energy operation load prediction and early warning method according to claim 1, characterized in that: in the seventh step, the fitting method specifically comprises the following steps: and determining the ranges of future load values of the first pipe network area and the second pipe network area by taking the annual variable growth rate as a reference and the influence of each variable growth rate on the water supply composite variable quantity, and then fitting the load historical data of each area to obtain a predicted value of the future water supply load.
9. The energy operation load prediction and early warning method according to claim 8, characterized in that: and seventhly, after the predicted value of the future water supply load is obtained, inputting the predicted value into the visual model in different areas, visualizing each area according to different load values, and then networking the visual model for inquiring the predicted value of the future water supply load of each area.
10. The energy operation load prediction and early warning method according to claim 1, characterized in that: and step nine, accessing a remote communication module in each water meter of the first pipe network area and the second pipe network area, accessing the remote communication module into each water meter element in the visual model, synchronously receiving detection value data of each water meter by using each water meter element, comparing the detection value data with the time flow range of each area and the time range of the water used in each area respectively, and when the numerical value is abnormal, carrying out highlight reminding on the area in the visual model and displaying corresponding coordinates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111283650.8A CN114021294A (en) | 2021-11-01 | 2021-11-01 | Energy operation load prediction and early warning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111283650.8A CN114021294A (en) | 2021-11-01 | 2021-11-01 | Energy operation load prediction and early warning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114021294A true CN114021294A (en) | 2022-02-08 |
Family
ID=80059259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111283650.8A Pending CN114021294A (en) | 2021-11-01 | 2021-11-01 | Energy operation load prediction and early warning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114021294A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060093079A (en) * | 2006-06-20 | 2006-08-23 | 주식회사 우암닷컴 | Long-run water demand forecast method and system by co-integrating regression with regional time varying coefficients |
| CN103218536A (en) * | 2013-04-26 | 2013-07-24 | 珠江水利委员会珠江水利科学研究院 | Great-leap-forward development area water demand prediction method |
| CN104200277A (en) * | 2014-08-12 | 2014-12-10 | 南方电网科学研究院有限责任公司 | Modeling method for medium and long term power load forecasting |
| CN106127360A (en) * | 2016-06-06 | 2016-11-16 | 国网天津市电力公司 | A kind of multi-model load forecasting method analyzed based on user personality |
| CN107590568A (en) * | 2017-09-20 | 2018-01-16 | 上海合泽电力工程设计咨询有限公司 | A kind of load forecasting method that becomes more meticulous based on space subdivision |
| CN108491969A (en) * | 2018-03-16 | 2018-09-04 | 国家电网公司 | Spatial Load Forecasting model building method based on big data |
| US20190260204A1 (en) * | 2018-02-17 | 2019-08-22 | Electro Industries/Gauge Tech | Devices, systems and methods for the collection of meter data in a common, globally accessible, group of servers, to provide simpler configuration, collection, viewing, and analysis of the meter data |
| CN110580264A (en) * | 2019-08-22 | 2019-12-17 | 华东师范大学 | Multi-source heterogeneous space-time data and vector credibility construction method thereof |
| CN111210058A (en) * | 2019-12-26 | 2020-05-29 | 深圳供电局有限公司 | Grid-based power distribution network top-down load prediction information method |
| CN112199402A (en) * | 2020-09-02 | 2021-01-08 | 国网山东省电力公司临沂供电公司 | Energy big data based supervision system and method |
| CN112526888A (en) * | 2021-02-07 | 2021-03-19 | 广州大学城能源发展有限公司 | Regional concentrated tail end load prediction method |
| CN112991659A (en) * | 2021-03-18 | 2021-06-18 | 湖州星创生态科技有限公司 | Big data security monitoring management method with early warning processing function |
-
2021
- 2021-11-01 CN CN202111283650.8A patent/CN114021294A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060093079A (en) * | 2006-06-20 | 2006-08-23 | 주식회사 우암닷컴 | Long-run water demand forecast method and system by co-integrating regression with regional time varying coefficients |
| CN103218536A (en) * | 2013-04-26 | 2013-07-24 | 珠江水利委员会珠江水利科学研究院 | Great-leap-forward development area water demand prediction method |
| CN104200277A (en) * | 2014-08-12 | 2014-12-10 | 南方电网科学研究院有限责任公司 | Modeling method for medium and long term power load forecasting |
| CN106127360A (en) * | 2016-06-06 | 2016-11-16 | 国网天津市电力公司 | A kind of multi-model load forecasting method analyzed based on user personality |
| CN107590568A (en) * | 2017-09-20 | 2018-01-16 | 上海合泽电力工程设计咨询有限公司 | A kind of load forecasting method that becomes more meticulous based on space subdivision |
| US20190260204A1 (en) * | 2018-02-17 | 2019-08-22 | Electro Industries/Gauge Tech | Devices, systems and methods for the collection of meter data in a common, globally accessible, group of servers, to provide simpler configuration, collection, viewing, and analysis of the meter data |
| CN108491969A (en) * | 2018-03-16 | 2018-09-04 | 国家电网公司 | Spatial Load Forecasting model building method based on big data |
| CN110580264A (en) * | 2019-08-22 | 2019-12-17 | 华东师范大学 | Multi-source heterogeneous space-time data and vector credibility construction method thereof |
| CN111210058A (en) * | 2019-12-26 | 2020-05-29 | 深圳供电局有限公司 | Grid-based power distribution network top-down load prediction information method |
| CN112199402A (en) * | 2020-09-02 | 2021-01-08 | 国网山东省电力公司临沂供电公司 | Energy big data based supervision system and method |
| CN112526888A (en) * | 2021-02-07 | 2021-03-19 | 广州大学城能源发展有限公司 | Regional concentrated tail end load prediction method |
| CN112991659A (en) * | 2021-03-18 | 2021-06-18 | 湖州星创生态科技有限公司 | Big data security monitoring management method with early warning processing function |
Non-Patent Citations (1)
| Title |
|---|
| 黄恒熙;: "惠州市应急需水预测及合理性分析", 广东水利水电, no. 07, 25 July 2012 (2012-07-25) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105184455A (en) | High dimension visualized analysis method facing urban electric power data analysis | |
| CN104657788B (en) | Key industry electricity demand forecasting method based on Industrial Cycle index | |
| CN111308039A (en) | Gridding water quality real-time monitoring method and system | |
| Rashid | AMI smart meter big data analytics for time series of electricity consumption | |
| US20130069951A1 (en) | System and method for generating, processing and displaying data relating to consumption data with an application | |
| CN107679634A (en) | A kind of method that power supply trouble based on data visualization reports analysis and prediction for repairment | |
| CN102135531A (en) | Method for forecasting blue-green algae water bloom in large-scale shallow lake within 72 hours | |
| Xu et al. | Temporal and spatial variations of urban climate and derivation of an urban climate map for Xi'an, China | |
| Popescu et al. | Assessing residual hydropower potential of the La Plata Basin accounting for future user demands | |
| CN113762618B (en) | Lake water level forecasting method based on multi-factor similarity analysis | |
| CN109933637A (en) | A kind of flood risk Dynamic Display and analysis system | |
| CN112330197B (en) | Meteorological hydrologic data quality control and evaluation method | |
| CN117236199B (en) | Method and system for improving water quality and guaranteeing water safety of river and lake in urban water network area | |
| CN110084439A (en) | A kind of software cost measure and cloud system based on the estimation of NESMA function point | |
| CN108764527B (en) | Screening method for soil organic carbon library time-space dynamic prediction optimal environment variables | |
| CN112985713A (en) | Pipe network leakage monitoring method and system based on edge calculation | |
| CN104715156A (en) | Building energy-saving potential dynamic assessment method | |
| CN115994685A (en) | Method for evaluating current situation of homeland space planning | |
| Xu et al. | Detecting spatial patterns of rivermouth processes using a geostatistical framework for near-real-time analysis | |
| CN110298765A (en) | A kind of power distribution network electric power method for detecting abnormality based on objective correlative factor | |
| Joshi et al. | Rainfall prediction using data visualisation techniques | |
| CN114021294A (en) | Energy operation load prediction and early warning method | |
| CN116203352A (en) | Fault early warning method, device, equipment and medium for power distribution network | |
| CN112699287B (en) | Configurable model data automatic preprocessing and distributing method and system | |
| CN116861371A (en) | Precipitation product precision improving method, system, equipment, medium and terminal |
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 |