Multi-meteorological-factor quantification method and device for daily power consumption increase and medium

Abstract

The invention provides a multi-meteorological-factor quantification method and device for daily electricity consumption increase and a medium, and the method comprises the steps: obtaining initial meteorological data in a preset time period, and obtaining meteorological index data through the completion operation of the initial meteorological data; dividing the meteorological index data into an initial meteorological index and a synthetic meteorological index; performing correlation analysis on the meteorological index, the synthetic meteorological index, the auxiliary meteorological factor and the electricity consumption to obtain an index with the maximum correlation coefficient; and based on the index with the maximum correlation coefficient, adopting an intelligent identification temperature threshold for modeling to obtain meteorological electric quantity and economic electric quantity. According to the technical scheme provided by the invention, the auxiliary meteorological factors can be considered as explanatory variables, and the influence of various meteorological factors on the electric quantity is fully reflected.

Description

Technical Field

[0001] This document relates to the technical field of meteorological factor quantification, and in particular to a method, device and medium for quantifying multiple meteorological factors of daily electricity consumption growth. Background Art

[0002] There is a certain correlation between weather and electricity consumption. Rising temperatures require people to use cooling equipment such as air conditioners, which increases electricity consumption; falling temperatures require people to use heating equipment to keep the room warm, which increases electricity consumption. In recent years, extreme weather events have increased, people have become more sensitive to meteorological factors, and the impact of meteorological changes on electricity consumption has become increasingly significant. The proportion of summer cooling load and winter heating load in the total social load has continued to increase. Meteorological electricity refers to meteorological factors, which mainly consider the electricity consumption caused by temperature factors. It is necessary to calculate heating and cooling electricity. The most commonly used method is the basic load comparison method, such as Figure 2 Schematic diagram of the traditional base load comparison method.

[0003] The main idea of ​​heating load calculation is as follows: assume that the heating load of typical days in autumn and spring is basically not released, and the actual load can be regarded as the basic load and power caused by economic factors alone; consider that the basic load increases linearly over time from autumn this year to spring next year, and the daily basic load and power in winter can be obtained; subtract the corresponding basic load and power from the actual daily load and power in winter to obtain the daily heating load and power in winter. It is worth noting that factors such as the Spring Festival holiday will reduce the basic load, and the basic load data before and after the Spring Festival should be scaled according to the actual load ratio.

[0004] The main idea of ​​cooling load calculation is as follows: The main idea is to assume that the cooling load and power are basically not released during the specific period of spring and autumn, and the load and power can be regarded as the basic load and power caused by economic factors alone; on the basis of calculating the typical daily load in spring and autumn, the corresponding basic load and power are subtracted from the daily load and power in summer to obtain the cooling load and power. The above basic load comparison method has the following shortcomings:

[0005] (1) Meteorological factors are not considered fully and comprehensively. In reality, although electricity consumption behaviors such as cooling and heating are mainly affected by temperature factors, other meteorological factors such as humidity, wind speed, and precipitation also have an impact on electricity consumption behaviors. There are relatively few qualitative and quantitative studies in this regard, and only a few studies have applied meteorological variables such as humidity and human comfort index.

[0006] (2) The nonlinear relationship between electricity consumption and temperature is too subjective. From the scatter plots of electricity load, electricity consumption and temperature over the years, we can find the nonlinear relationship between electricity load, electricity consumption and temperature. However, the existing research mainly relies on human judgment to divide the temperature stages.

[0007] (3) The commonly used basic load comparison method has obvious shortcomings. On the one hand, it is assumed that there is no meteorological electricity in the spring and autumn months, and only cooling / heating electricity in the summer and winter. In fact, people are increasingly sensitive to weather, and there is also meteorological electricity in the spring and autumn months, such as April when heating has ended and October when heating has not yet started. On the other hand, this method has negative cooling and heating loads on some dates, which is inconsistent with common sense. Summary of the invention

[0008] The present invention provides a method, device and medium for quantifying multiple meteorological factors of daily electricity consumption growth, aiming to solve the above-mentioned problem.

[0009] According to an embodiment of the present invention, a method for quantifying daily electricity consumption growth based on multiple meteorological factors is provided, including:

[0010] According to an embodiment of the present invention, there is provided an electronic device, including:

[0011] processor; and,

[0012] A memory arranged to store computer executable instructions, wherein when the computer executable instructions are executed, the processor executes the steps of the multi-meteorological factor quantification method for daily electricity consumption growth as described above.

[0013] According to an embodiment of the present invention, a storage medium is provided for storing computer executable instructions, which, when executed, implement the steps of the multi-meteorological factor quantification method for daily electricity consumption growth as described above.

[0014] The embodiment of the present invention proposes a method for quantifying daily electricity consumption growth based on multiple meteorological factors, taking holidays and air conditioning growth trends into account as explanatory variables, fully reflecting the impact of multiple meteorological factors on electricity, and making the analysis more detailed. The method uses intelligent identification of temperature threshold modeling when calculating meteorological electricity, and conducts comparative analysis on different variable selections. Through the dimensions of model fit goodness, calculated meteorological electricity change trend, and year-on-year growth rate of economic electricity, a suitable model is selected to obtain meteorological electricity and economic electricity. Compared with the assumption that load linearly increases when calculating meteorological electricity by the basic load comparison method, this method takes into account the nonlinear characteristics of the impact of meteorology on electricity, and obtains the impact of electricity at different temperature levels. The threshold selection uses intelligent identification rather than manual setting, and the meteorological electricity calculation result obtained is more objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 It is a flow chart of a method for quantifying daily electricity consumption growth using multiple meteorological factors according to an embodiment of the present invention;

[0017] Figure 2 It is a schematic diagram of the traditional base load comparison method;

[0018] Figure 3 This is a schematic diagram of the mechanism of the impact of climate temperature on electricity consumption growth according to an embodiment of the present invention;

[0019] Figure 4 It is a specific implementation flow chart of the multi-meteorological factor quantification method for daily electricity consumption growth according to an embodiment of the present invention;

[0020] Figure 5 Schematic diagram of the monthly year-on-year growth rate of electricity consumption in the tertiary industry in City A since 2023 under the intelligent identification temperature threshold modeling. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0022] Method Embodiment

[0023] According to an embodiment of the present invention, a method for quantifying daily electricity consumption growth based on multiple meteorological factors is provided. Figure 1 Flow chart of the multi-meteorological factor quantification method for daily electricity consumption growth according to an embodiment of the present invention. Figure 1 As shown, the multi-meteorological factor quantification method for daily electricity consumption growth in an embodiment of the present invention specifically includes:

[0024] S1. Acquire initial meteorological data within a preset time period, and acquire meteorological index data by completing the initial meteorological data; in the embodiment of the present invention, the initial meteorological data is completed by using an average or an arithmetic progression method.

[0025] S2. Divide the meteorological index data into initial meteorological indexes and synthetic meteorological indexes; the initial meteorological indexes include: temperature, humidity, wind speed and precipitation; and the synthetic meteorological indexes include: perceived temperature and comfort.

[0026] Figure 3 This is a schematic diagram of the mechanism of the impact of climate temperature on electricity consumption growth in an embodiment of the present invention. Climate temperature factors mainly affect people's cooling and heating needs, thereby affecting electricity consumption growth. In summer, when the temperature rises to hot, people's cooling needs in production and life will increase, and the electricity consumption of air conditioners, electric fans and other equipment for cooling will increase, thereby increasing electricity consumption; in winter, when the temperature drops to cold, people's heating needs in production and life will increase, and the electricity consumption of electric heating and other equipment for heating will increase, thereby increasing electricity consumption. In addition, with the improvement of people's living standards and the advancement of science and technology, people's purchase and use of various cooling and heating equipment will continue to increase, and under the same meteorological conditions, the amount of cooling and heating electricity will also increase.

[0027] In the embodiment of the present invention, holidays and air conditioning growth trends are further taken into account as explanatory variables, where the commercial building area and the number of air conditioners per 100 households represent the growth trends of air conditioning in the service industry and residents, respectively, and are selected and used according to the model simulation effect. Since this patent studies high-frequency (daily) electricity consumption, economic activity levels, population changes, technological progress and other variables, which generally remain unchanged in the short term and have no high-frequency data, they are not analyzed in the model.

[0028] S3, performing correlation analysis on the meteorological index, the synthetic meteorological index, the auxiliary meteorological factors and the power consumption to obtain the index with the largest correlation coefficient;

[0029] S4. Based on the index with the largest correlation coefficient, intelligent identification temperature threshold modeling is adopted to obtain meteorological power and economic power.

[0030] Intelligent identification temperature threshold modeling is mainly based on the threshold model. When the economic system undergoes a systematic transformation, or there are systematic differences within the economic system, it is difficult for general linear models to reasonably characterize the nonlinear relationship between such economic variables. The threshold regression model is a typical model that can characterize changes or differences in economic structure. The threshold effect refers to the phenomenon (structural mutation) that when a representative observed variable (economic parameter) reaches a specific value, another economic parameter suddenly turns to other development situations. The critical value of the cause phenomenon is called the threshold value. Mainly by selecting representative observed variables (economic parameters), the threshold regression model estimates its unknown threshold value and the regression coefficient of the explanatory variable of the segmented zone system linear regression. The threshold regression model can study multiple thresholds and multiple zone systems. Taking a single threshold and two zone systems as an example, the regression model is as follows:

[0031]

[0032] q t is the threshold variable, the average temperature is used as the threshold variable, γ1 is the threshold value to be estimated, X, Z, ε are the exogenous explanatory variables and disturbance terms, B represents the coefficient of the explanatory variable X, δ1 represents the coefficient of the explanatory variable Z, δ2 represents the coefficient of the explanatory variable Z, ε 1t represents the disturbance term, ε 2t represents the disturbance term, X t ' represents the exogenous explanatory variable that does not change with the threshold, Z t ' represents an exogenous explanatory variable whose coefficient changes with the threshold.

[0033] In this patent, holidays and air conditioning growth trends are used as exogenous explanatory variables with variable coefficients. In order to reduce the fluctuations between the calculation results of models in different temperature sections, the lagged variables of electricity consumption and temperature are set as constant coefficient explanatory variables, that is, the coefficients of the lagged variables in each temperature section remain consistent.

[0034] If there are multiple thresholds, an indicative function I(·) can be introduced, that is, when the expression is true, the value is 1, otherwise it is 0. The specific regression model can be expressed as:

[0035]

[0036] By determining the above regression equation, the least squares method can be used to regress the residual sum of squares minimization SSR(B,δ,γ). The actual calculation is carried out in two steps. First, the value γ of the threshold variable q is selected. Since the characteristic function I(q,r) exists, SSR(γ) is a step function of γ, and γ is selected on q. By selecting a finite number of γ on q, piecewise least squares regression is performed, and SSR and γ of different selections are compared to determine γe. Then, based on the given value of the threshold variable γe, the regression equation can be converted into a linear model, and the least squares method is used to estimate the regression coefficient of the explanatory variable.

[0037] The meteorological variables in the estimation results are multiplied by the corresponding threshold coefficients to obtain the meteorological electricity, and the total electricity minus the meteorological electricity is used to obtain the economic electricity.

[0038] The method further includes: adjusting parameters of intelligent identification temperature threshold modeling based on meteorological power and economic power using model fitting goodness, meteorological power change trend and year-on-year growth rate of economic power.

[0039] Figure 4 This is a specific implementation flow chart of the multi-meteorological factor quantification method for daily electricity consumption growth according to an embodiment of the present invention. Figure 4 A specific implementation of the embodiment of the present invention is given:

[0040] Taking the electricity consumption of the tertiary industry as an example, the analysis results of the above model are shown. Based on the classification of meteorological indicators, two types of model simulation and debugging are carried out: one is a model that adds meteorological variables such as minimum temperature, temperature difference, interaction terms between relative humidity and average temperature, and wind speed; the other is a model that only adds temperature difference and perceived temperature, because temperature, wind speed, and relative humidity are all included in the calculation formula of the perceived temperature index. Due to the lack of daily economic data, non-meteorological factors are mainly represented by the lagged terms of electricity consumption in the tertiary industry and holidays. Considering separating economic factors from meteorological factors in the model, there is no intercept term in the model equation because the intercept term may contain both economic and meteorological content.

[0041] The use of intelligent identification of temperature threshold modeling and analysis is to use model tools to divide the stages according to the characteristics of the data itself, which has a certain objectivity. In the model regression, the threshold variable selects the average temperature (Avgtemp). In the first type of meteorological indicator model, the temperature is divided into four intervals: below 1.8℃, between 1.8℃ and 20.1℃, between 20.1℃ and 26.1℃, and above 26.1℃; in the second type of meteorological indicator model, the temperature is divided into five intervals: below 1.8℃, between 1.8℃ and 12.4℃, between 12.4℃ and 20.1℃, between 20.1℃ and 24.4℃, and above 24.4℃. The temperature interval division is more refined. Considering the reduction of fluctuations between the results of the models in different temperature ranges, the lag variables are set as constant explanatory variables, that is, the coefficients of the lag variables in each temperature range are kept consistent. In the first type of meteorological indicator model, the estimated coefficient of the lag variable of the tertiary industry electricity consumption is 0.7, which is significant at the 5% significance level, while the estimated coefficient of the lag variable of the temperature is -7.2, which is not significant enough; in the second type of meteorological indicator model, the estimated coefficients of the lag variable of the tertiary industry electricity consumption and the lag variable of the temperature are 0.7 and 32.5 respectively, both of which are significant at the 5% significance level. The goodness of fit of the threshold regression under the two types of meteorological indicators is relatively high and close, both close to 0.95; the regression of each temperature range has differences in the significance and estimated coefficients of individual variables.

[0042] Table 1 Analysis results of temperature threshold modeling for intelligent identification of electricity consumption in the tertiary industry (first category of meteorological indicators)

[0043]

[0044]

[0045] Note: The t-statistics of the estimated parameters of the explanatory variables are in brackets in the table; *** , ** It means that the parameter estimate is significant at the 5%, 1%, and 10% significance levels, the same below.

[0046] Table 2 Analysis results of temperature threshold modeling for intelligent identification of electricity consumption in the tertiary industry (second type of meteorological indicators) Based on the regression results of intelligent identification temperature threshold modeling, the year-on-year growth rate of economic electricity in City A is calculated for each month since the beginning of this year. Figure 5 It can be seen that its changing trend is roughly similar to the first three models. The growth rates in May and June are still relatively high, but they are more reasonable than the original statistics (such as a growth rate of nearly 50% in June). This is related to the peak in May of the previous year and stricter control.

[0047] Finally, considering the differences in the impact of meteorology on electricity consumption, the existence of meteorological electricity in non-summer and winter months, and the periodic changes in the relationship between temperature and electricity consumption, we recommend the modeling of intelligent identification of temperature thresholds and its regression analysis results. In addition, for ease of application, it is recommended to perform relevant calculations based on the model of the first type of meteorological indicators.

[0048] The embodiment of the present invention proposes a method for quantifying daily electricity consumption growth based on multiple meteorological factors, taking holidays and air conditioning growth trends into account as explanatory variables, fully reflecting the impact of multiple meteorological factors on electricity, and making the analysis more detailed. The method uses intelligent identification of temperature threshold modeling when calculating meteorological electricity, and conducts comparative analysis on different variable selections. Through the dimensions of model fit goodness, calculated meteorological electricity change trend, and year-on-year growth rate of economic electricity, a suitable model is selected to obtain meteorological electricity and economic electricity. Compared with the assumption that load linearly increases when calculating meteorological electricity by the basic load comparison method, this method takes into account the nonlinear characteristics of the impact of meteorology on electricity, and obtains the impact of electricity at different temperature levels. The threshold selection uses intelligent identification rather than manual setting, and the meteorological electricity calculation result obtained is more objective.

[0049] Device Example 1

[0050] According to an embodiment of the present invention, there is provided an electronic device, including:

[0051] processor; and,

[0052] A memory is arranged to store computer executable instructions, which when executed cause the processor to perform the steps of the above method embodiments.

[0053] Device Example 2

[0054] According to an embodiment of the present invention, a storage medium is provided for storing computer executable instructions, wherein the computer executable instructions implement the steps of the above method embodiment when executed.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantifying daily electricity consumption growth based on multiple meteorological factors, characterized in that include: S1. Acquire initial meteorological data within a preset time period, and acquire meteorological index data by completing the initial meteorological data; S2, dividing the meteorological index data into initial meteorological indexes and synthetic meteorological indexes; S3, performing correlation analysis on the meteorological index, the synthetic meteorological index, the auxiliary meteorological factors and the power consumption to obtain the index with the largest correlation coefficient; S4. Based on the index with the largest correlation coefficient, intelligent identification temperature threshold modeling is adopted to obtain meteorological power and economic power.

2. The method according to claim 1, characterized in that The initial meteorological data is completed using an average or an arithmetic progression method.

3. The method according to claim 1, characterized in that The initial meteorological indicators include: temperature, humidity, wind speed and precipitation.

4. The method according to claim 1, characterized in that: The synthetic meteorological index includes: perceived temperature and comfort level.

5. The method according to claim 1, characterized in that The method according to claim 1, characterized in that the auxiliary meteorological factors include: Holiday factors, air conditioning growth data and commercial building area data.

6. The method according to claim 1, characterized in that The method according to claim 1 is characterized in that the intelligent identification temperature threshold modeling obtains a threshold regression model through formula 1: qt is the threshold variable, the average temperature is used as the threshold variable, γ1 is the threshold value to be estimated, X, Z, ε are the exogenous explanatory variables and disturbance terms, B is the coefficient of the explanatory variable X, δ1 is the coefficient of the explanatory variable Z, δ2 is the coefficient of the explanatory variable Z, ε 1t represents the disturbance term, ε 2t represents the disturbance term, X t ' represents the exogenous explanatory variable that does not change with the threshold, Z t ' represents an exogenous explanatory variable whose coefficient changes with the threshold.

7. The method according to claim 6, characterized in that The method according to claim 1 is characterized in that if there are multiple threshold values ​​to be estimated, an indicative function I() is introduced, and the threshold regression model is expressed in the form of formula 2:

8. The method according to claim 1, characterized in that: The method according to claim 1 is characterized in that the method further comprises: adjusting parameters of intelligent identification temperature threshold modeling based on meteorological power and economic power using model fitting goodness, meteorological power change trend and economic power year-on-year growth rate.

9. An electronic device, comprising: processor; as well as, A memory arranged to store computer executable instructions, wherein when the computer executable instructions are executed, the processor executes the steps of the multi-meteorological factor quantification method for daily electricity consumption growth as claimed in any one of claims 1 to 8.

10. A storage medium for storing computer executable instructions, wherein the computer executable instructions, when executed, implement the steps of the multi-meteorological factor quantification method for daily electricity consumption growth as described in any one of claims 1 to 8.

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