CN115081671A - Method for analyzing and displaying correlation between air temperature and power consumption - Google Patents
Method for analyzing and displaying correlation between air temperature and power consumption Download PDFInfo
- Publication number
- CN115081671A CN115081671A CN202210444069.8A CN202210444069A CN115081671A CN 115081671 A CN115081671 A CN 115081671A CN 202210444069 A CN202210444069 A CN 202210444069A CN 115081671 A CN115081671 A CN 115081671A
- Authority
- CN
- China
- Prior art keywords
- air temperature
- data
- electricity
- electricity consumption
- daily
- 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 13
- 230000005611 electricity Effects 0.000 claims abstract description 47
- 238000004458 analytical method Methods 0.000 claims abstract description 6
- 230000005612 types of electricity Effects 0.000 claims abstract description 6
- 238000012937 correction Methods 0.000 claims abstract description 3
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000013480 data collection Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000013079 data visualisation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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
- G06Q10/04—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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2428—Query predicate definition using graphical user interfaces, including menus and forms
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Data Mining & Analysis (AREA)
- Water Supply & Treatment (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for analyzing and displaying the correlation between air temperature and power consumption, which comprises the steps of collecting and storing basic data of air temperature and power consumption of various places by a big data platform; then, the big data platform performs correlation decision analysis on the daily electricity consumption and the daily air temperature data to form a correlation trend graph of the electricity consumption and the air temperature; calculating future daily electricity consumption data of different types of electricity consumer customers in each region on an electricity consumption prediction model based on air temperature; and correcting the prediction coefficient of the relation between the air temperature and the electricity consumption according to the deviation between the predicted value and the actual value by using a prediction model correction mechanism and newly recorded air temperature data of each region and electricity consumption data of different types of electricity consumers of each region. The invention can utilize big data technology, combine air temperature and electric quantity data, predict future electricity utilization situation according to air temperature change of future date, and improve data reference for electricity utilization customers.
Description
Technical Field
The invention relates to the technical field of analysis of air temperature and electricity consumption, in particular to an analysis and display method for correlation between air temperature and electricity consumption.
Background
At present, most users clearly know that the electricity consumption is higher in summer and winter than in spring and autumn, but the most electricity saving is not clear at what temperature. Although the platform has the useful electric quantity data and the gas temperature data, the useful electric quantity data and the gas temperature data are dispersed on different platforms to form respective data isolated islands, and the value of the data is exerted.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for analyzing and displaying the correlation between air temperature and power consumption.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for analyzing and displaying correlation between air temperature and power consumption comprises the following steps:
step 1: the big data platform collects and stores the basic data of the gas temperature and the power consumer in each place;
step 2: the big data platform performs correlation decision analysis on the daily electricity consumption and the daily air temperature data to form a correlation trend graph of the electricity consumption and the air temperature;
and step 3: calculating future daily electricity consumption data of different types of electricity consumer customers in various regions based on an electricity consumption prediction model of the air temperature;
and 4, step 4: a prediction model correction mechanism; and correcting the prediction coefficient of the relation between the air temperature and the electricity consumption according to the deviation amount of the predicted value and the actual value.
Preferably, the collection of the basic data comprises temperature data collection and power consumer data collection, wherein
Collecting air temperature data: acquiring historical daily lowest air temperature and highest air temperature data of each place, the historical daily lowest air temperature and the historical daily highest air temperature of the future date from a meteorological platform through air temperature APi and storing the data; the meteorological platform is used for cleaning, filtering and screening collected air temperature data of different regions, and storing the air temperature data into the hbase according to the region codes and the day and hour;
collecting data of electric users: acquiring and storing data such as basic archive information of electricity consumers in various places, historical daily electricity consumption and the like from the metering platform through the electricity consumer information APi; the metering platform collects the power consumption data of users in different areas, and the data are cleaned, filtered and screened, and are stored in the hbase by day according to the area codes.
The invention has the following beneficial effects:
the invention can utilize big data technology, combine air temperature and electric quantity data, predict future electricity utilization situation according to air temperature change of future date, and improve data reference for electricity utilization customers.
Drawings
FIG. 1 is a flow chart of a method for analyzing and displaying correlation between air temperature and power consumption according to the present invention;
FIG. 2 is a data visualization diagram of a temperature and power consumption data display example provided by the invention;
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Referring to fig. 1-2, one embodiment of the present invention is provided: a method for analyzing and displaying correlation between air temperature and power consumption comprises the following steps:
1. collecting and storing basic data required by a big data platform:
a) collecting air temperature data: acquiring historical daily lowest air temperature and highest air temperature data of each place, the historical daily lowest air temperature and the historical daily highest air temperature of the future date from a meteorological platform through air temperature APi and storing the data; the meteorological platform is used for cleaning, filtering and screening collected air temperature data of different regions, and storing the air temperature data into the hbase according to the region codes and the day and hour;
b) collecting data of power consumers: acquiring and storing data such as basic archive information of all the electric consumers and historical daily electric consumption from the metering platform through the electric consumer information APi; the metering platform is used for cleaning, filtering and screening collected power consumption data of users in different areas, and storing the data into hbase by day according to area codes;
and the big data platform reads mass daily electricity consumption and daily air temperature data from the hbase to perform correlation decision analysis, and forms a correlation trend graph of electricity consumption and air temperature.
2. Calculating a relation coefficient between the electricity consumption of electricity customers in various regions and the local air temperature, and calculating an incidence relation coefficient between the air temperature in various regions and the electricity consumption of different types of electricity consumers through a data analysis technology according to data such as the daily lowest air temperature and the highest air temperature data, the local electricity consumer category, the daily electricity consumption and the like of the history in various regions;
3. the method comprises the steps that based on a power consumption prediction model of air temperature, according to data of the lowest air temperature and the highest air temperature of future dates of various regions, future daily power consumption data of different types of power consumer customers in various regions are calculated through the prediction model;
4. a prediction model modification mechanism; and correcting the prediction coefficient of the relation between the air temperature and the electricity consumption according to the deviation amount of the predicted value and the actual value.
The following is further illustrated by a specific temperature and power consumption data display example:
acquiring historical air temperature data and future air temperature prediction data through an air temperature and electricity consumption data API; the historical daily electricity consumption of the user, future electricity consumption data form a data visualization chart for displaying, as shown in fig. 2:
the data presentation rules are as follows:
1) date: the date is the same as the month of the electricity consumption calendar and changes along with the change of the month of the electricity consumption calendar;
2) electricity consumption: displaying the power consumption corresponding to each day according to the selected month, and not displaying the power consumption line graphs on the current day and after the current day; if no corresponding electricity consumption quantity exists on other dates, the default is '0', and the daily electricity consumption quantity is the electricity consumption quantity on the corresponding date in the electricity consumption calendar;
3) temperature: displaying the temperature corresponding to each day according to the selected month, and not displaying the temperature line graph on the current day and after the current day; if no corresponding temperature exists on other dates, defaulting to be 0, and defaulting to display the range values of the lowest temperature and the highest temperature on the same day by the daily temperature;
4) the Y-axis (electricity consumption) scale is displayed in a floating mode and is averagely divided into 7 sections according to the maximum value and the minimum value;
5) the Y-axis (temperature) scale is displayed in a floating mode and is averagely divided into 7 sections according to the maximum value and the minimum value;
6) the X axis (date) shows the current month date by default, the current month date is evenly divided into 6 sections for display, and the last day of the sixth section is default.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (2)
1. A method for analyzing and displaying correlation between air temperature and power consumption is characterized in that: the method comprises the following steps:
step 1: the big data platform collects and stores the basic data of the gas temperature and the power consumer in each place;
step 2: the big data platform performs correlation decision analysis on the daily electricity consumption and the daily air temperature data to form a correlation trend graph of the electricity consumption and the air temperature;
and step 3: calculating future daily electricity consumption data of different types of electricity consumer customers in various regions based on an electricity consumption prediction model of the air temperature;
and 4, step 4: a prediction model correction mechanism; and correcting the relation prediction coefficient of the air temperature and the electricity consumption according to the deviation amount of the predicted value and the actual value of newly recorded air temperature data of each region and electricity consumption data of different types of electricity consumers of each region.
2. The method for analyzing and displaying the correlation between the air temperature and the power consumption as claimed in claim 1, wherein the method comprises the following steps: the collection of the basic data comprises temperature data collection and power consumer data collection, wherein
Collecting air temperature data: acquiring historical daily lowest air temperature and highest air temperature data of each place, the historical daily lowest air temperature and the historical daily highest air temperature of the future date from a meteorological platform through air temperature APi and storing the data; the meteorological platform is used for cleaning, filtering and screening collected air temperature data of different regions, and storing the air temperature data into the hbase according to the region codes and the day and hour;
collecting data of power consumers: acquiring and storing data such as basic archive information of all the electric consumers and historical daily electric consumption from the metering platform through the electric consumer information APi; the metering platform collects the power consumption data of users in different areas, and the data are cleaned, filtered and screened, and are stored in the hbase by day according to the area codes.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210444069.8A CN115081671A (en) | 2022-04-25 | 2022-04-25 | Method for analyzing and displaying correlation between air temperature and power consumption |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210444069.8A CN115081671A (en) | 2022-04-25 | 2022-04-25 | Method for analyzing and displaying correlation between air temperature and power consumption |
Publications (1)
Publication Number | Publication Date |
---|---|
CN115081671A true CN115081671A (en) | 2022-09-20 |
Family
ID=83247187
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210444069.8A Pending CN115081671A (en) | 2022-04-25 | 2022-04-25 | Method for analyzing and displaying correlation between air temperature and power consumption |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115081671A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116701481A (en) * | 2023-04-21 | 2023-09-05 | 国网宁夏电力有限公司 | Electric heating electricity consumption fluctuation analysis system and method |
CN116823075A (en) * | 2023-08-29 | 2023-09-29 | 小象飞羊(北京)科技有限公司 | City data construction model, electronic equipment and storage medium |
-
2022
- 2022-04-25 CN CN202210444069.8A patent/CN115081671A/en active Pending
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116701481A (en) * | 2023-04-21 | 2023-09-05 | 国网宁夏电力有限公司 | Electric heating electricity consumption fluctuation analysis system and method |
CN116823075A (en) * | 2023-08-29 | 2023-09-29 | 小象飞羊(北京)科技有限公司 | City data construction model, electronic equipment and storage medium |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN115081671A (en) | Method for analyzing and displaying correlation between air temperature and power consumption | |
JP3783929B2 (en) | Energy saving effect estimation method and apparatus | |
US7451017B2 (en) | Energy and cost savings calculation system | |
US7392115B2 (en) | Characterization of utility demand using utility demand footprint | |
CN104412291B (en) | The method and system of unusual usage amount report | |
US6785620B2 (en) | Energy efficiency measuring system and reporting methods | |
US20040225625A1 (en) | Method and system for calculating and distributing utility costs | |
US7647137B2 (en) | Utility demand forecasting using utility demand matrix | |
JP6079215B2 (en) | Power demand forecasting device, program | |
US20090006279A1 (en) | Automatic utility usage rate analysis methodology | |
CN109165763A (en) | A kind of potential complained appraisal procedure and device of 95598 customer service work order | |
CN111553516A (en) | Short-term electric quantity load accurate prediction method | |
CN110097220B (en) | Method for predicting monthly electric quantity of wind power generation | |
CN115809237B (en) | Method and system for supplementing missing data of user water meter | |
JP2006011715A (en) | Estimation method for resource consumption, and device | |
CN116683452B (en) | Method and system for repairing solar heat lost electric quantity | |
CN106875058B (en) | Intelligent judgment method for expanded open capacity of power industry | |
KR101865924B1 (en) | Appratus and method for estimation of weekly power load to improve processing time using neural network and revision factor | |
JP2000270473A (en) | Power demand estimating method | |
Bakhshiloevich et al. | Development of a combined method for forecasting electricity consumption of an industrial enterprise llc evrosnar | |
JP6103323B1 (en) | Electricity price information prediction system | |
US5924076A (en) | Coin operated device collection scheduler | |
CN116957842A (en) | Power data analysis processing method, system and storage medium | |
CN113378102A (en) | Data missing preprocessing method, medium and application for short-term load prediction | |
CN111582517A (en) | Data processing method and device based on full life cycle cost |
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 |