CN112149309A - Temperature-adjusting load curve fitting method considering temperature-load correlation - Google Patents
Temperature-adjusting load curve fitting method considering temperature-load correlation Download PDFInfo
- Publication number
- CN112149309A CN112149309A CN202011060090.5A CN202011060090A CN112149309A CN 112149309 A CN112149309 A CN 112149309A CN 202011060090 A CN202011060090 A CN 202011060090A CN 112149309 A CN112149309 A CN 112149309A
- Authority
- CN
- China
- Prior art keywords
- temperature
- load
- fitting
- model
- maximum
- 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.)
- Granted
Links
Images
Classifications
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a temperature-regulating load curve fitting method considering temperature-load correlation, which comprises the steps of constructing a data sample, adopting a reference load comparison strategy to approximate an annual temperature-regulating load curve based on the climate characteristics of Guizhou province, and further obtaining annual temperature-regulating load data and maximum temperature-regulating load; calculating the correlation degree by combining the data sample and the annual temperature regulation load data, and further analyzing the correlation degree of the temperature and the temperature regulation load and the cumulative effect of the temperature on the temperature regulation load; combining the correlation degree of the temperature and the temperature regulation load, constructing a unitary quadratic fit model and a unitary cubic fit model to fit the current day temperature and the maximum temperature regulation load, and constructing a binary quadratic fit model to perform multivariate nonlinear regression fitting on the current day temperature, the previous day temperature and the maximum temperature regulation load; and quantifying the influence degree of the temperature on the maximum temperature regulation load by calculating the sensitivity of the fitting curve. The method has important significance for load prediction in the dispatching operation of the power system.
Description
Technical Field
The invention relates to the technical field of power temperature regulation loads, in particular to a temperature regulation load curve fitting method considering temperature-load correlation.
Background
The load characteristic analysis and load prediction of the power system are the basis of power grid planning and power grid dispatching operation control, and the power load is influenced by various factors such as economy, climate, industrial structure and the like. With the promotion of comprehensive health of China, the living standard of residents is continuously improved, the proportion of air conditioner loads such as heating, refrigeration and the like to the total power utilization load is in a trend of increasing year by year, and the influence rule of temperature on the power grid load is analyzed, so that the method has important significance for more accurately predicting the power grid load.
Because of more factors influencing the load of the power system, most of the existing methods analyze the relevance between the air temperature and the maximum load more, establish a plurality of regression models to fit a relation curve between the air temperature (or the corrected air temperature after considering the temperature accumulation effect) and the load at the same day, and more emphasize on analyzing the influence of the temperature on the cooling load in summer. If the occupancy of residential and commercial loads in a regional power load structure is low and the occupancy of a large industrial load is high, the influence of the large industry and the like on the load cannot be well eliminated by the existing analysis method, and further the influence of the temperature on the load cannot be well evaluated.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides the temperature-regulating load curve fitting method considering the temperature-load correlation, which can effectively eliminate the influence of the large industry on the load and further more accurately evaluate the influence degree of the temperature on the temperature-regulating load.
In order to solve the technical problems, the invention provides the following technical scheme: constructing a data sample, and adopting a reference load comparison strategy to approximate an annual temperature regulation load curve based on the climate characteristics of Guizhou province so as to obtain annual temperature regulation load data and a maximum temperature regulation load; calculating the correlation degree by combining the data sample and the annual temperature regulation load data, and further analyzing the correlation degree of the temperature and the temperature regulation load and the cumulative effect of the temperature on the temperature regulation load; combining the correlation degree of the temperature and the temperature regulation load, constructing a unitary quadratic fit model and a unitary cubic fit model to fit the current day temperature and the maximum temperature regulation load, and constructing a binary quadratic fit model to perform multivariate nonlinear regression fitting on the current day temperature, the previous day temperature and the maximum temperature regulation load; and quantifying the influence degree of the temperature on the maximum temperature regulation load by calculating the sensitivity of the fitting curve.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the construction data sample comprises that the average daily temperature data of 2015 to 2019 years of Guizhou province is collected through a historical weather query system of the China weather bureau to construct a temperature data sample; and collecting the active load data of the Guizhou province from 2015 to 2019 through a power grid dispatching management system, and constructing a load data sample.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: defining the sampling step length of the temperature data sample as 1 day to obtain a daily average temperature data sample sequence; and defining the sampling step length of the load data sample as 15 minutes, and obtaining an active load data sample sequence.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the annual temperature regulation load curve comprises the following steps of selecting daily average load curves of 4 months and 9 months as daily basic active load curves, namely:
wherein k is the total days of 4 months and 9 months, t is the daily sampling time, PB(t) daily basis active load; p (k, t) represents the active load at the kth day t; further obtaining the annual temperature regulation load curve PT(d，t)：
PT(d，t)＝P(d，t)-PB(t)
Wherein P (d, t) is the active load data sample sequence.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the correlation includes a correlation r defining a random sequence X, Yxy：
Wherein the content of the first and second substances,
respectively, the mean, X, of the random sequence X, Yi、YiTo followElements in machine sequence X, Y, N representing the number of elements in sequence X, Y; based on the definition, respectively calculating the correlation degree between the current day average temperature data sample sequence T and the temperature regulation load sequence, and the previous day average temperature data sample sequence T-1Correlation degree with the temperature regulation load sequence, and the average temperature data sample sequence T of the previous two days-2Correlation with the tempering load sequence.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the unary quadratic fitting model comprises the following steps of constructing the unary quadratic fitting model based on an unary quadratic function:
PTmax(T)＝a1T2+a2T+a3
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day is obtained through fitting of the unitary quadratic fitting model, a1、a2、a3And fitting the undetermined coefficient of the model for the unary quadratic fit.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the unitary cubic fitting model comprises the following steps of constructing the unitary cubic fitting model based on a unitary cubic function:
PT1max(T)＝b1T3+b2T2+b3T+b4
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day is obtained through fitting of the unitary cubic fitting model, b1、b2、b3、b4And fitting the undetermined coefficient of the model for the unary three times.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the binary quadratic fitting model comprises the following steps of constructing the binary quadratic fitting model based on a binary quadratic function:
PT2max(T，T-1)＝c1T2+c2TT-1+c3T-1 2+c4T+c5T-1+c6
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the current day and the temperature of the previous day is obtained through fitting of the binary quadratic fitting model, c1、c2、c3、c4、c5、c6And fitting undetermined coefficients for the binary quadratic function.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: the sensitivity comprises obtaining the temperature-maximum tempering load sensitivity by taking the derivative of the temperature-maximum tempering load curve:
wherein, KTThe current day temperature-maximum tempering load sensitivity; kT-1The previous day temperature-maximum tempering load sensitivity.
As a preferable aspect of the temperature regulation load curve fitting method considering the temperature-load dependency according to the present invention, wherein: also comprises the following steps of (1) preparing,
defining the temperature variation value Delta T of the current day and the temperature variation value Delta T of the previous day-1Respectively calculating the temperature-maximum temperature regulation load sensitivity corresponding to the unitary quadratic fitting model, the unitary cubic fitting model and the binary quadratic fitting model; the above-mentionedThe corresponding temperature-maximum temperature regulation load sensitivity of the unary quadratic fitting model is as follows:
KT＝2a1ΔT+a2
the corresponding temperature-maximum temperature regulation load sensitivity of the unitary cubic fitting model is as follows:
KT＝3b1ΔT2+2b2ΔT+b3
the corresponding temperature-maximum temperature regulation load sensitivity of the binary quadratic fitting model is as follows:
KT＝2c1ΔT+c2ΔT×T-1+c4
the invention has the beneficial effects that: temperature-temperature regulation load curve fitting is carried out by constructing a unitary quadratic fitting model and a unitary cubic fitting model, and a temperature regulation curve is fitted by constructing a binary quadratic fitting model so as to analyze the condition that the heating electric load is influenced by gas temperature after the accumulative effect of the temperature is considered; then, the influence degree of the temperature on the temperature regulation load is further quantized by calculating the temperature-temperature regulation load sensitivity; the method has important significance for load characteristic analysis and load prediction in power grid planning and power system dispatching operation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a schematic flow chart of a temperature regulation load curve fitting method considering temperature-load dependence according to a first embodiment of the present invention;
fig. 2 is a diagram illustrating a fit curve of temperature-maximum tempering load on the day of 2015 of a tempering load curve fitting method considering temperature-load dependency according to a first embodiment of the invention;
fig. 3 is a 2017 current day temperature-maximum tempering load fitting curve diagram of a tempering load curve fitting method considering temperature-load correlation according to a first embodiment of the invention;
fig. 4 is a 2018 current day temperature-maximum tempering load fitting curve diagram of a tempering load curve fitting method considering temperature-load correlation according to a first embodiment of the present invention;
fig. 5 is a 2019 current day temperature-maximum tempering load fitting curve diagram of a tempering load curve fitting method considering temperature-load correlation according to a first embodiment of the present invention;
fig. 6 is a graph showing a fit curve of the temperature of the day of the year 2015, the temperature of the previous day-maximum tempering load according to the tempering load curve fitting method considering the temperature-load dependency according to the first embodiment of the invention;
fig. 7 is a fitting curve diagram of the current day temperature, the previous day temperature and the maximum tempering load in 2017 of a tempering load curve fitting method considering temperature-load correlation according to the first embodiment of the invention;
fig. 8 is a fitting curve diagram of the current day temperature, the previous day temperature and the maximum tempering load in 2018 of a tempering load curve fitting method considering temperature-load correlation according to the first embodiment of the invention;
fig. 9 is a fitting curve diagram of the current day temperature, the previous day temperature and the maximum tempering load in 2019 of a tempering load curve fitting method considering temperature-load correlation according to the first embodiment of the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 9, a first embodiment of the present invention provides a temperature-controlled load curve fitting method considering temperature-load dependency, including:
s1: and constructing a data sample, and adopting a reference load comparison strategy to approximate an annual temperature regulation load curve based on the climate characteristics of the Guizhou province so as to obtain annual temperature regulation load data and the maximum temperature regulation load.
The method comprises the steps of acquiring 2015-2019-year daily average temperature data of Guizhou province by a historical weather query system of the China weather bureau, wherein the sampling step length is 1 day, obtaining 2015-2019-year daily average temperature data sample sequences of the Guizhou province, wherein 5 groups of daily average temperature data sample sequences are obtained, and 365 temperature data samples in each group are obtained.
Active load time sequence data of Guizhou province are collected through a power grid dispatching management system, the sampling step length (adopting the minimum time interval of data points) is 15 minutes, namely the collected data are calculated by utilizing 5 years multiplied by 365 days multiplied by 24 hours multiplied by 4, 5 groups of active load data sample sequences of Guizhou province in 2015-2019 years in each year are obtained, and 35040 load data samples in each group are obtained.
Furthermore, as the climate characteristics of Guizhou province are cool in summer and wet and cold in winter, the coldest months in the whole year are 12 months and 1 month, and the climate is comfortable in spring and autumn, especially 4 months and 9 months, temperature adjusting equipment such as an air conditioner and a heater is rarely started; therefore, the average daily load curves of 4 months and 9 months are selected as daily basic active load curves, namely:
wherein k is the total days of 4 months and 9 months, t is the daily sampling time, PB(t) is the daily basic active load, and P (k, t) represents the active load at the kth sampling time t of 4 months and 9 months;
further, 2015-2019 year-per-year temperature-adjusting load yeastLine PT(d，t)：
PT(d，t)＝P(d，t)-PB(t)
Wherein, P (d, t) is the active load data sample sequence.
S2: and calculating the correlation degree by combining the data sample and the annual temperature regulation load data, and further analyzing the correlation degree of the temperature and the temperature regulation load and the cumulative effect of the temperature on the temperature regulation load.
It should be noted that the cumulative effect of temperature is defined as: the daily maximum load is abnormally increased to a certain extent due to continuous high-temperature or low-temperature weather for multiple days, namely, the temperature is not obviously increased or reduced under the same temperature level, but the daily maximum load is still larger than the daily maximum load of the high-temperature or low-temperature weather for a certain day; the cumulative effect of temperature is generated because the human sense has a process of adapting to the temperature change, and the cumulative effect directly influences the comfort level of the human body, thereby indirectly influencing the use condition of the temperature sensitive load.
Defining a degree of correlation r for a random sequence X, Yxy：
Wherein the content of the first and second substances,
respectively, the mean, X, of the random sequence X, Yi、YiIs an element in the random sequence X, Y, and N represents the number of elements in the sequence X, Y; a larger absolute value of the correlation indicates a stronger correlation.
In view of the fact that the heating in winter is relatively large in the Guizhou province over the years, and the temperature regulation load in the whole province is mainly the heating load in winter, the correlation between the average temperature data sample sequence T on the current day and the temperature regulation load sequence and the average temperature data sample sequence T on the previous day are calculated by using the annual temperature time series data and the annual temperature regulation load data respectively-1Correlation degree with temperature regulation load sequence, average temperature data sample sequence T of previous two days-2And the temperature regulating loadThe degree of correlation of the sequences; the larger the absolute value of the correlation degree is, the stronger the correlation between the temperature and the temperature regulation load curve is; the correlation between the winter temperature and the temperature adjusting load in the Guizhou province of 2015-2019 is calculated according to a correlation formula and is shown in table 1, and obviously, the correlation between the temperature adjusting load and the temperature of the current day and the temperature of the previous day is high.
Table 1: and a correlation contrast table of the temperature-temperature regulation load curve in winter of Guizhou province.
It should be noted that, in 2016, the power grid in Guizhou starts to execute power transmission and distribution prices, the randomness of putting and withdrawing of industrial users is strong, and industrial loads cannot be well deducted when the temperature regulation loads are approximately estimated by adopting a reference load comparison method, so that the correlation coefficient of the temperature regulation loads and the temperature is very low.
S3: and constructing a unitary quadratic fitting model and a unitary cubic fitting model to fit the current day temperature and the maximum temperature regulation load according to the correlation degree of the temperature and the temperature regulation load, and constructing a binary quadratic fitting model to perform multivariate nonlinear regression fitting on the current day temperature, the previous day temperature and the maximum temperature regulation load.
The calculation result of the correlation between the winter temperature and the temperature adjusting load of Guizhou province in the Guizhou province of 2015-2019 is shown, and the correlation between the temperature and the temperature adjusting load is low due to the fact that 2016 is influenced by the fact that the electricity price policy is started to be executed, so that curve fitting of the temperature adjusting load is not carried out in the year; therefore, fitting the temperature-adjusting load curve for 2015, 2017, 2018 and 2019 by using the following fitting model, and solving the confidence interval of the waiting coefficient of the fitting function according to 95% confidence.
Specifically, (1) constructing a unitary quadratic fitting model based on a unitary quadratic function:
PTmax(T)＝a1T2+a2T+a3
wherein the content of the first and second substances,
finger passing unary and secondary fitting dieThe maximum temperature regulating load corresponding to the temperature of the day, a, is obtained by the model fitting1、a2、a3And (4) fitting the undetermined coefficient of the model for unary quadratic fit.
(2) Constructing a unitary cubic fitting model based on a unitary cubic function:
PT1max(T)＝b1T3+b2T2+b3T+b4
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day is obtained through fitting by a unitary cubic fitting model, b1、b2、b3、b4And fitting the undetermined coefficient of the model for the unitary third time.
The confidence intervals of the fitting model and the undetermined coefficient are obtained by calculation and are shown in a table 2, and fitting curves are shown in figures 2-5 in detail; it can be seen that, in combination with the calculation result of the temperature-temperature regulation load correlation, the correlation in 2017 is low, while the correlation in 2015, 2018 and 2019 is 0.4-0.6, and the trend lines of the fitted curves in 2015, 2018 and 2019 in fig. 2, 4 and 5 are consistent.
Table 2: a winter temperature-maximum temperature regulation load fitting model and a parameter confidence interval table in Guizhou province.
(3) Constructing a binary quadratic fitting model based on a binary quadratic function:
PT2max(T，T-1)＝c1T2+c2TT-1+c3T-1 2+c4T+c5T-1+c6
it is understood from the table i that the temperature control load has a high correlation with the current day temperature and the previous day temperature, and therefore the current day temperature, the previous day temperature, and the corresponding temperature control load are fitted using a binary quadratic fit model.
Wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day and the temperature of the day before is obtained through fitting of a binary quadratic fitting model, c1、c2、c3、c4、c5、c6And fitting undetermined coefficients for the binary quadratic function.
The confidence intervals of the fitting function and the undetermined coefficient are obtained by calculation and are shown in a table 3, and the fitting curve is shown in figures 3-9 in detail; obviously, in 2015, 2018 and 2019, under the condition that the temperature is related to the temperature regulation load equivalently (the degree of correlation is in the range of 0.4-0.6), along with the development of the economic society, the increase of temperature regulation equipment, the improvement of the consumption capacity of residents and the influence of the temperature on the temperature regulation load are more and more obvious, for example, an undetermined coefficient constant term (c) of a binary quadratic fit function in 20156) 4771, and 2019 the parameter has increased to 6881.
Table 3: a winter current day temperature-maximum temperature regulation load fitting function and a parameter confidence interval table in Guizhou province.
S4: and the influence degree of the temperature on the maximum temperature regulation load is quantified by calculating the sensitivity of the fitting curve.
Based on the selection of the temperature-maximum temperature regulation load curve fitting model, the sensitivity of the temperature-maximum temperature regulation load can be obtained by solving the derivative (partial derivative) of the temperature-maximum temperature regulation load curve, and the sensitivity parameter reflects the change of the temperature variation corresponding to the maximum temperature regulation load in engineering; the expression for temperature-maximum tempering load sensitivity is as follows:
wherein, KTThe current day temperature-maximum tempering load sensitivity; kT-1The previous day temperature-maximum tempering load sensitivity.
Based on the sensitivity expression, defining the temperature change value Delta T of the current day and the temperature change value Delta T of the previous day-1Respectively calculating the temperature-maximum temperature regulation load sensitivity corresponding to the unitary quadratic fitting model, the unitary cubic fitting model and the binary quadratic fitting model:
(1) the corresponding temperature-maximum temperature regulation load sensitivity of the unary quadratic fitting model is as follows:
KT＝2a1ΔT+a2
(2) the corresponding temperature-maximum temperature regulation load sensitivity of the unitary cubic fitting model is as follows:
KT＝3b1ΔT2+2b2ΔT+b3
(3) the corresponding temperature-maximum temperature regulation load sensitivity of the binary quadratic fitting model is as follows:
KT＝2c1ΔT+c2ΔT×T-1+c4
the variation of the temperature-adjusting load corresponding to 1 ℃ of the temperature change is analyzed, and the sensitivity of the temperature-maximum temperature-adjusting load curve is calculated and obtained, as shown in table 4:
table 4: temperature-maximum tempering load sensitivity table in 2015, 2017, 2018 and 2019.
Preferably, the sensitivities of the temperature regulation load affected by the temperature when the unitary quadratic fit model is adopted for fitting are-126.56, -216.2 and-150.5 respectively, and the sensitivities of the temperature regulation load affected by the temperature when the unitary cubic fit model is adopted for fitting are-159.6, -230.13 and-306 respectively, namely, the maximum temperature regulation load in Guizhou winter is increased by about 126-306 MW when the temperature is reduced by 1 ℃, so that the unitary quadratic fit model and the unitary cubic fit model can better fit the current day temperature-maximum temperature regulation load curve.
Example 2
In order to verify and explain the technical effects adopted in the method, the embodiment selects a unitary linear function fitting method and adopts the method to perform comparison test, and compares test results by means of scientific demonstration to verify the real effect of the method.
The traditional unitary linear function fitting method has low precision on the fitting of the temperature-maximum daily load curve relationship in the same day, cannot well eliminate the influence of the large industry and the like on the load, and cannot better evaluate the influence of the temperature on the load.
In order to verify that the method has a higher goodness of fit compared with the conventional method, in this embodiment, the conventional one-dimensional linear function fitting method and the method are respectively used to calculate the goodness of fit, and the goodness of fit calculation formula is as follows:
wherein the content of the first and second substances,
respectively, the mean of a random sequence (actual value) Y, YiBeing elements in a random sequence (actual value) Y,
are each YiFitting value of (by fitting)Calculated from a function), R2For goodness of fit, the goodness of fit range is (0, 1), with closer to 1 indicating a better fit of the fitting function to the random sequence.
And respectively carrying out goodness-of-fit calculation on the fitted temperature-adjusting load curves of the unary primary function, the unary quadratic fitting model and the unary cubic fitting model so as to analyze the fitting precision condition of the fitting function, which is detailed in table 5:
table 5: and fitting precision comparison table.
| Goodness of fit | 2015 years | 2017 | 2018 years old | 2019 |
| Unitary linear function (common engineering) | 0.976286 | 0.815592 | 0.966216 | 0.962523 |
| Unary quadratic fitting model (method) | 0.976292 | 0.827133 | 0.966222 | 0.962527 |
| Three-dimensional fitting model (method) | 0.976426 | 0.827669 | 0.966289 | 0.962916 |
Obviously, compared with a unary first-order fitting function commonly used in traditional engineering, the unary second-order fitting model and the unary third-order fitting model provided by the method have higher fitting accuracy.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A temperature-adjusting load curve fitting method considering temperature-load correlation is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing a data sample, and adopting a reference load comparison strategy to approximate an annual temperature regulation load curve based on the climate characteristics of Guizhou province so as to obtain annual temperature regulation load data and a maximum temperature regulation load;
calculating the correlation degree by combining the data sample and the annual temperature regulation load data, and further analyzing the correlation degree of the temperature and the temperature regulation load and the cumulative effect of the temperature on the temperature regulation load;
combining the correlation degree of the temperature and the temperature regulation load, constructing a unitary quadratic fit model and a unitary cubic fit model to fit the current day temperature and the maximum temperature regulation load, and constructing a binary quadratic fit model to perform multivariate nonlinear regression fitting on the current day temperature, the previous day temperature and the maximum temperature regulation load;
and quantifying the influence degree of the temperature on the maximum temperature regulation load by calculating the sensitivity of the fitting curve.
2. A tempering load curve fitting method taking into account temperature-load dependency according to claim 1 wherein: the constructing of the data sample includes constructing the data sample,
collecting daily average temperature data of 2015-2019 years of Guizhou province by a historical weather query system of the China meteorological office to construct a temperature data sample;
and collecting the active load data of the Guizhou province from 2015 to 2019 through a power grid dispatching management system, and constructing a load data sample.
3. A tempering load curve fitting method taking into account temperature-load dependency according to claim 1 or 2, characterized in that: also comprises the following steps of (1) preparing,
defining the sampling step length of the temperature data sample as 1 day to obtain a daily average temperature data sample sequence;
and defining the sampling step length of the load data sample as 15 minutes, and obtaining an active load data sample sequence.
4. A tempering load curve fitting method taking into account temperature-load dependency according to claim 3 wherein: the annual tempering load curve includes the following,
selecting daily average load curves of 4 months and 9 months as daily basic active load curves, namely:
wherein k is the total days of 4 months and 9 months, t is the daily sampling time, PB(t) is the daily base active load, P (k, t) represents the active load at the kth day t;
further obtaining the annual temperature regulation load curve PT(d，t)：
PT(d，t)＝P(d，t)-PB(t)
Wherein P (d, t) is the active load data sample sequence.
5. A tempering load curve fitting method taking into account temperature-load dependency according to any of claims 1, 2, 4 wherein: the degree of correlation includes a degree of correlation,
defining a degree of correlation r for a random sequence X, Yxy：
Wherein the content of the first and second substances,
respectively, the mean, X, of the random sequence X, Yi、YiIs an element in the random sequence X, Y, and N represents the number of elements in the sequence X, Y;
based on the definition, respectively calculating the correlation degree between the current day average temperature data sample sequence T and the temperature regulation load sequence, and the previous day average temperature data sample sequence T-1Correlation degree with the temperature regulation load sequence, and the average temperature data sample sequence T of the previous two days-2Correlation with the tempering load sequence.
6. A tempering load curve fitting method taking into account temperature-load dependency according to claim 5 wherein: the unary quadratic fit model includes a model of,
constructing the unary quadratic fitting model based on the unary quadratic function:
PTmax(T)＝a1T2+a2T+a3
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day is obtained through fitting of the unitary quadratic fitting model, a1、a2、a3And fitting the undetermined coefficient of the model for the unary quadratic fit.
7. A tempering load curve fitting method taking into account temperature-load dependency according to claim 1 or 6 wherein: the unitary cubic fit model includes a model of,
constructing the unitary cubic fitting model based on a unitary cubic function:
PT1max(T)＝b1T3+b2T2+b3T+b4
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the day is obtained through fitting of the unitary cubic fitting model, b1、b2、b3、b4And fitting the undetermined coefficient of the model for the unary three times.
8. A tempering load curve fitting method taking into account temperature-load dependency according to claim 7 wherein: the binary quadratic fit model comprises a model of a quadratic fit,
constructing the binary quadratic fitting model based on a binary quadratic function:
PT2max(T，T-1)＝c1T2+c2TT-1+c3T-1 2+c4T+c5T-1+c6
wherein the content of the first and second substances,
the maximum temperature regulating load corresponding to the temperature of the current day and the temperature of the previous day is obtained through fitting of the binary quadratic fitting model, c1、c2、c3、c4、c5、c6And fitting undetermined coefficients for the binary quadratic function.
9. A tempering load curve fitting method taking into account temperature-load dependency according to any of claims 1, 2, 6, 8 wherein: the sensitivity may include one or more of,
obtaining the temperature-maximum tempering load sensitivity by taking the derivative of the temperature-maximum tempering load curve:
wherein, KTThe current day temperature-maximum tempering load sensitivity; kT-1The previous day temperature-maximum tempering load sensitivity.
10. A tempering load curve fitting method taking into account temperature-load dependency according to claim 9 wherein: the method also comprises the following steps of,
defining the temperature variation value Delta T of the current day and the temperature variation value Delta T of the previous day-1Respectively calculating the temperature-maximum temperature regulation load sensitivity corresponding to the unitary quadratic fitting model, the unitary cubic fitting model and the binary quadratic fitting model;
the corresponding temperature-maximum temperature regulation load sensitivity of the unary quadratic fitting model is as follows:
KT＝2a1ΔT+a2
the corresponding temperature-maximum temperature regulation load sensitivity of the unitary cubic fitting model is as follows:
KT＝3b1ΔT2+2b2ΔT+b3
the corresponding temperature-maximum temperature regulation load sensitivity of the binary quadratic fitting model is as follows:
KT＝2c1ΔT+c2ΔT×T-1+c4
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011060090.5A CN112149309B (en) | 2020-09-30 | 2020-09-30 | Temperature-regulating load curve fitting method considering temperature-load correlation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011060090.5A CN112149309B (en) | 2020-09-30 | 2020-09-30 | Temperature-regulating load curve fitting method considering temperature-load correlation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112149309A true CN112149309A (en) | 2020-12-29 |
| CN112149309B CN112149309B (en) | 2023-07-21 |
Family
ID=73951265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011060090.5A Active CN112149309B (en) | 2020-09-30 | 2020-09-30 | Temperature-regulating load curve fitting method considering temperature-load correlation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112149309B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113819581A (en) * | 2021-08-23 | 2021-12-21 | 广西电网有限责任公司电力科学研究院 | Summer air conditioner load capacity and temperature sensitivity analysis method thereof |
| CN115618747A (en) * | 2022-11-23 | 2023-01-17 | 广东电网有限责任公司中山供电局 | Cable hot spot state evaluation method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090295397A1 (en) * | 2008-05-28 | 2009-12-03 | Texas Instruments Incorporated | Systems and Methods for Determining Battery Parameters Following Active Operation of the Battery |
| CN103413253A (en) * | 2013-09-04 | 2013-11-27 | 国家电网公司 | Method for performing classification prediction on annual maximum load based on economical and meteorological factors |
| CN104156783A (en) * | 2014-07-29 | 2014-11-19 | 广西电网有限责任公司 | Maximum daily load prediction system and method of electric system considering meteorological accumulative effect |
| CN106779129A (en) * | 2015-11-19 | 2017-05-31 | 华北电力大学（保定） | A kind of Short-Term Load Forecasting Method for considering meteorologic factor |
| CN108664009A (en) * | 2017-08-03 | 2018-10-16 | 湖州师范学院 | Divided stages based on correlation analysis and fault detection method |
| CN108830415A (en) * | 2018-06-13 | 2018-11-16 | 国网江苏省电力有限公司电力科学研究院 | A kind of weather sensitive load power estimating method based on non-linear correlation model |
| CN110224393A (en) * | 2019-05-24 | 2019-09-10 | 广东电网有限责任公司阳江供电局 | A kind of new energy consumption appraisal procedure based on minimum load shedding model |
-
2020
- 2020-09-30 CN CN202011060090.5A patent/CN112149309B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090295397A1 (en) * | 2008-05-28 | 2009-12-03 | Texas Instruments Incorporated | Systems and Methods for Determining Battery Parameters Following Active Operation of the Battery |
| CN103413253A (en) * | 2013-09-04 | 2013-11-27 | 国家电网公司 | Method for performing classification prediction on annual maximum load based on economical and meteorological factors |
| CN104156783A (en) * | 2014-07-29 | 2014-11-19 | 广西电网有限责任公司 | Maximum daily load prediction system and method of electric system considering meteorological accumulative effect |
| CN106779129A (en) * | 2015-11-19 | 2017-05-31 | 华北电力大学（保定） | A kind of Short-Term Load Forecasting Method for considering meteorologic factor |
| CN108664009A (en) * | 2017-08-03 | 2018-10-16 | 湖州师范学院 | Divided stages based on correlation analysis and fault detection method |
| CN108830415A (en) * | 2018-06-13 | 2018-11-16 | 国网江苏省电力有限公司电力科学研究院 | A kind of weather sensitive load power estimating method based on non-linear correlation model |
| CN110224393A (en) * | 2019-05-24 | 2019-09-10 | 广东电网有限责任公司阳江供电局 | A kind of new energy consumption appraisal procedure based on minimum load shedding model |
Non-Patent Citations (4)
| Title |
|---|
| CRISTIAN GHIAUS 等: "Equivalence between the load curve and the free-running temperature in energy estimating methods", 《ENERGY AND BUILDINGS》 * |
| M. BABITA JAIN 等: "Curve fitting and regression line method based seasonal short term load forecasting", 《2012 WORLD CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGIES》 * |
| 刘文霞 等: "考虑温度-负荷相关性的调温负荷曲线拟合方法研究", 《电网与清洁能源》 * |
| 贺春光: "夏季电网最大负荷与气温的相关性分析", 《河北电力技术》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113819581A (en) * | 2021-08-23 | 2021-12-21 | 广西电网有限责任公司电力科学研究院 | Summer air conditioner load capacity and temperature sensitivity analysis method thereof |
| CN113819581B (en) * | 2021-08-23 | 2022-11-18 | 广西电网有限责任公司电力科学研究院 | Summer air conditioner load capacity and air temperature sensitivity analysis method |
| CN115618747A (en) * | 2022-11-23 | 2023-01-17 | 广东电网有限责任公司中山供电局 | Cable hot spot state evaluation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112149309B (en) | 2023-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111860979B (en) | Short-term load prediction method based on TCN and IPSO-LSSVM combined model | |
| CN110070282B (en) | Low-voltage transformer area line loss influence factor analysis method based on comprehensive relevance | |
| CN112149309A (en) | Temperature-adjusting load curve fitting method considering temperature-load correlation | |
| CN104616079A (en) | Temperature change based power grid daily electricity consumption prediction method | |
| CN111486554B (en) | Air conditioner temperature non-sensitive control method based on online learning | |
| CN109193630B (en) | Flexible load adjustable interval prediction method and device | |
| CN110264107B (en) | Large data technology-based abnormal diagnosis method for line loss rate of transformer area | |
| CN113297799B (en) | Air conditioner cluster load demand response potential evaluation method based on data driving | |
| CN103971296A (en) | Power purchase method for mathematic model based on electrical loads and temperature | |
| CN112001554B (en) | Short-term load prediction method based on parameter self-adaptive similar daily method | |
| Lam et al. | An analysis of climatic influences on chiller plant electricity consumption | |
| CN112651543A (en) | Daily electric quantity prediction method based on VMD decomposition and LSTM network | |
| CN115411730A (en) | Air conditioner load multi-period adjustable potential evaluation method and related device | |
| CN111553568A (en) | Line loss management method based on data mining technology | |
| JP5658597B2 (en) | Energy-saving behavior evaluation system | |
| CN110533247B (en) | Monthly power consumption prediction method adopting temperature data anomaly point compensation | |
| CN111723085B (en) | Method and system for cleaning electric energy consumption data of public building air conditioner | |
| Yang et al. | Power system yearly peak load forecasting: a grey system modeling approach | |
| CN115392546A (en) | Method and device for predicting cooling electric quantity and managing demand side based on meteorological factors | |
| CN113516279B (en) | Comprehensive energy load prediction method based on energy consumption state transfer | |
| CN116862129A (en) | Zero-energy-consumption building data analysis method and system | |
| CN115271168A (en) | Method and device for predicting response potential of electrical load and storage medium | |
| CN114862229A (en) | Power quality evaluation method and device, computer equipment and storage medium | |
| Liu et al. | Recognition model of air conditioning electricity consumption behaviors for demand response based on k-means clustering | |
| CN111598349A (en) | Short-term power consumption prediction method and device and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |