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
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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.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210444069.8A CN115081671A (en) | 2022-04-25 | 2022-04-25 | Method for analyzing and displaying correlation between air temperature and power consumption |
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| CN202210444069.8A CN115081671A (en) | 2022-04-25 | 2022-04-25 | Method for analyzing and displaying correlation between air temperature and power consumption |
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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 |
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