CN116683452B - Method and system for repairing solar heat lost electric quantity - Google Patents
Method and system for repairing solar heat lost electric quantity Download PDFInfo
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Abstract
The invention discloses a method and a system for repairing solar heat lost electric quantity, and relates to the technical field of electric energy metering in electric power marketing work. The method comprises the following steps: the method comprises the steps of obtaining the electricity consumption of a user, and dividing the electricity consumption into non-time-sharing electricity consumption and time-sharing electricity consumption according to the obtaining mode of the electricity consumption; fitting the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain a missing electric quantity; according to the invention, by adopting a time-sharing analysis method for the time-sharing electric quantity and adopting a similar day algorithm for fitting the non-time-sharing electric quantity, more accurate identification and repair data of the solar clear missing electric quantity are obtained. The defect of complex solution and larger error in the prior art for processing the continuous data missing condition is overcome.
Description
Technical Field
The invention relates to the technical field of electric energy metering in electric power marketing work, in particular to a method and a system for repairing solar energy lost electric quantity.
Background
At present, the national network headquarters require the province company to uniformly adjust the meter reading time of the customers in the power supply district and to carry out the electricity fee settlement according to the natural month so as to realize the synchronization of the electricity fee meter reading settlement period and the customer production and life periods. Compared with the traditional service, the meter reading accounting is changed from 'moon reading moon knot' to 'sun clearing moon knot', and the meter reading accounting service in the present stage has the following characteristics:
the meter reading accounting business carries out the settlement of the electricity charge of once meter reading every month according to the meter reading example day. Because the spot market adopts the node electricity price mechanism to price, the day-ahead market and the real-time market form a time-sharing node electricity price as the market electricity price through a centralized optimization competition mode. The marketing side needs to perform meter reading calculation on the electric quantity of the user side every day, and pushes the electric quantity to a transaction center to serve as an electric quantity settlement basis for wholesale market users such as electricity selling companies, direct transaction users and the like. Therefore, after the spot transaction is carried out, the meter reading and accounting business of the marketing profession needs to be carried out every day and the electric quantity delivery calculation (eliminating the non-marketized electric quantity) is carried out, and the electric quantity and electric charge clearing is carried out in a month. The meter reading and accounting business is changed into continuity from stage to stage. As meter reading and accounting services become more frequent, economic and resource losses due to power loss are not negligible. Because errors and faults of a metering device or a data communication system are unavoidable, the situation that the user electric quantity data is lost cannot be completely eradicated, and therefore the high-precision fitting method of the lost electric quantity data is particularly important.
For the application of the power loss data fitting method, some attempts have been made in the power industry:
she Feng et al (2006) identify and repair abnormal electricity according to the mean and variance of the load at the same time by using a statistical method based on historical load data in the metering system; zhang Xiaoxing et al (2005) identify abnormal electricity data by neural network method, and adjust the abnormal data by scaling the shape of characteristic curve; liu Li et al (2011) take node load in the system as a study object, adopt a k-means clustering algorithm to extract a typical characteristic curve, and use the transverse similarity and the longitudinal similarity of the curve to perform data identification; mo Weiren et al (2003) have carried out repair of detected abnormal electricity data by means of short-term load prediction, which is effective only for correction of individual data, and which is not ideal for repair in case of continuous abnormality of data.
In summary, the current research is mainly focused on identifying abnormal power data, but less discussion is needed for repairing the data, and the adopted method is simpler, the principle of the traditional data repairing method is simple, the correlation between the defect point and surrounding data or the periodicity of historical data is mainly considered, and when a single point of missing is processed, the calculation time is short and the repairing effect is good; when processing continuous data missing, the solution becomes very complex, and the repaired data is quite different from the original data. Therefore, how to realize the restoration of the solar heat lost electric quantity by using an efficient data fitting method becomes a problem to be solved urgently in the prior art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method and a system for repairing the solar clear missing electric quantity, which are used for obtaining more accurate solar clear missing electric quantity identification and repair data by adopting a time-sharing analysis method for the time-sharing electric quantity and adopting a similar daily algorithm for fitting the non-time-sharing electric quantity.
In order to achieve the above object, the present invention is realized by the following technical scheme:
the first aspect of the invention provides a method for repairing solar heat lost electric quantity, which comprises the following steps:
the method comprises the steps of obtaining the electricity consumption of a user, and dividing the electricity consumption into non-time-sharing electricity consumption and time-sharing electricity consumption according to the obtaining mode of the electricity consumption;
fitting the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain a missing electric quantity;
the time division electric quantity is fitted by adopting a time-sharing analysis method, and the specific steps are as follows:
determining a period corresponding to the electric quantity when the electric quantity is missing;
fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity;
fitting the non-time-sharing electric quantity by adopting a similar day algorithm, wherein the specific steps are as follows:
adopting a correlation analysis method to analyze the correlation between the fitting day of the missing electric quantity and the history non-fitting day and external factors;
searching a date with highest similarity between the feature type and the fitting day of the missing electric quantity in the non-fitting days of the history according to the correlation as a similarity day;
and supplementing the missing electric quantity of the fitting day according to the electric quantity curve of the similar day.
Further, according to the electricity consumption acquisition mode, the electricity consumption obtained by the electricity consumption acquisition mode with the peak-valley section segmented acquisition function is referred to as time-sharing electricity consumption, and the electricity consumption obtained by the electricity consumption acquisition mode without the peak-valley section segmented acquisition function is referred to as non-time-sharing electricity consumption.
Still further, the peak-valley sections include peaks, peak sections, normal sections and valley sections.
Further, when the electric quantity is simulated according to the time interval algorithm corresponding to different time intervals, the electric quantity used by the user is respectively measured according to the peak, the normal time and the valley time, and specifically, the electric quantity used by each hour is obtained through decomposition by using an arithmetic average method; non-integral points of the peak-valley period are respectively added and then fitted through arithmetic average values according to the composition of the non-integral points; for the same type of discontinuous time periods, the power is combined for calculation.
Further, the external factors include three factors of weather, time and date type.
Further, the specific step of searching the date with the highest similarity between the feature type and the fitting date of the missing electric quantity in the non-fitting dates of the history according to the correlation as the similarity date is as follows:
calculating matching coefficients of three factors of weather, time and date types;
weighting the matching coefficients of the three factors of weather, time and date types to obtain final matching coefficients of the three factors respectively;
calculating comprehensive similarity according to the final matching coefficients of the three factors;
and finally, selecting the date with the highest comprehensive similarity as the similarity date.
Further, the specific steps of weighting the matching coefficients of the three factors of weather, time and date type to obtain the final matching coefficients of the three factors respectively are as follows:
calculating the matching coefficient of the meteorological factors, the matching coefficient of the time factors and the matching coefficient of the date type factors by using a matching coefficient calculation model;
measuring and calculating the weights of three factors of weather, time and date types through an objective weight algorithm;
and the final matching coefficients of the three factors are respectively obtained by multiplying the weight and the matching factor.
Further, according to the electric quantity curve of the similar day, the specific steps of supplementing the missing electric quantity of the fitting day are as follows:
after the similar day is determined, calculating the electric quantity at the missing time point of the fitting day by taking the electric quantity proportion of each period of the similar day as a reference according to the electric quantity curve of the similar day;
and supplementing the missing electric quantity on the fitting day by using the calculation result.
Further, after the missing electric quantity is obtained, the electric charge is fed back to the user according to the missing electric quantity, and the code metering condition of the last month is considered when the electric charge calculation is carried out in the next month, so that the code metering adjustment is carried out.
The second aspect of the present invention provides a system for repairing a solar heat loss electric quantity, comprising:
the power acquisition module is configured to acquire the power consumption of a user and is divided into non-time-sharing power and time-sharing power according to the power consumption acquisition mode;
the fitting analysis module is configured to fit the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain the missing electric quantity;
the fitting analysis module comprises a time-sharing electric quantity fitting module and a non-time-sharing electric quantity fitting module, wherein the time-sharing electric quantity fitting module is configured to fit the time-sharing electric quantity by adopting a time-sharing analysis method, and the specific steps are as follows:
determining a period corresponding to the electric quantity when the electric quantity is missing;
fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity;
the non-time-sharing electric quantity fitting module is configured to fit the non-time-sharing electric quantity by adopting a similar day algorithm, and comprises the following specific steps of:
adopting a correlation analysis method to analyze the correlation between the fitting day of the missing electric quantity and the history non-fitting day and external factors;
searching a date with highest similarity between the feature type and the fitting day of the missing electric quantity in the non-fitting days of the history according to the correlation as a similarity day;
and supplementing the missing electric quantity of the fitting day according to the electric quantity curve of the similar day.
The one or more of the above technical solutions have the following beneficial effects:
the invention discloses a method and a system for repairing solar clear missing electric quantity, which are used for distinguishing time-sharing electric quantity and non-time-sharing electric quantity according to the electric quantity acquisition function of whether a metering device of a power grid enterprise has a peak, a peak section, a flat section and a valley section, and calculating the missing electric quantity by adopting different modes on the time-sharing electric quantity and the non-time-sharing electric quantity so as to obtain an accurate solar clear missing electric quantity result more comprehensively. According to the method, different missing electric quantity fitting methods are determined according to different time periods, the missing electric quantity fitting is carried out by adopting a similar day algorithm aiming at the non-time-sharing electric quantity, the electric quantity in the continuous time period can be accurately calculated and repaired, and the defects of complex solution and larger error in the processing of the continuous data missing condition in the prior art are overcome.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a method for repairing non-time-sharing electric quantity in a daily clear electric quantity deficiency according to an embodiment of the invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof;
embodiment one:
the embodiment of the invention provides a method for repairing solar heat lost electric quantity, which specifically comprises the following steps:
step 1, acquiring the electricity consumption of a user, and dividing the electricity consumption into non-time-sharing electricity consumption and time-sharing electricity consumption according to an acquisition mode of the electricity consumption.
In a specific embodiment, according to the power consumption acquisition mode, the power consumption obtained by the power consumption acquisition mode with the peak-valley section segmented acquisition function is referred to as time-sharing power consumption, and the power consumption obtained by the power consumption acquisition mode without the peak-valley section segmented acquisition function is referred to as non-time-sharing power consumption. In this embodiment, the electric quantity acquisition mode is to acquire electric quantity by using the power grid enterprise metering device.
And 2, fitting the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain the missing electric quantity.
And 2.1, fitting the time division electric quantity by adopting a time-division analysis method.
In a specific embodiment, the time-sharing electric quantity fitting is specifically a time-sharing electric quantity fitting of marketized transaction electricity, and the time-sharing electric quantity fitting is suitable for marketized transaction users without electric energy time-sharing metering conditions.
The electricity marketing transaction refers to a general term of electricity trading activities performed between an electricity generation enterprise and an electricity selling company or a large electricity consumer in a marketing manner. The power market model is a systematic form of power market established by the power industry. The time-sharing electric quantity fitting is needed for the marketization transaction, because the electric quantity fitting is needed only when marketization users are daily clear at present, and common residents, large industries and other users directly purchase electricity daily from the power grid do not need to calculate the daily clear electric quantity, so that fitting is not needed.
The condition of not having electric energy metering time-sharing means that the metering device cannot meet the electric power user requirement of freezing (storing) the electric quantity at 24 hours of continuous time-sharing of spot transaction. The marketized transaction users with the metering device meeting the requirement of time-sharing electric quantity freezing (storage) provide the actual electric quantity used in each period of 24 hours per day for the electric power transaction mechanism by the power supply enterprises as the settlement basis, which is not suitable for the embodiment. And when the time-sharing metering data is missing, the marketized trading users with the electric energy time-sharing metering condition calculate the electric energy according to the electric energy data fitting method of the electricity consumption side of Shandong province electric power spot trading rules by the power grid enterprise.
The peak-valley sections referred to in this embodiment include a peak, a peak section, a normal section, and a valley section, and are determined according to the existing specification as:
spike period: 10:30-11:30, 19:00-21:00; peak time: 8:30-11:30, 16:00-19:00; low valley period: 23:00-7:00; the rest time period is a normal time period.
The embodiment relates to the meter reading electric quantity of the next month, wherein the peak, the peak section, the flat section and the valley section are natural months, and the actually executed electric quantity is the meter reading electric quantity of the next month.
And 2.1.1, determining a period corresponding to the electric quantity when the electric quantity is missing.
And 2.1.2, fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity.
The time-sharing electric quantity fitting is called in the embodiment, and the time-sharing electric quantity is generated by decomposing the electric quantity into each hour in the corresponding section on average according to the electric quantity of the peak, peak section, flat section and valley section of the power grid enterprise metering device.
Before fitting the electric quantity, firstly acquiring electric quantity data, wherein the electric quantity data is obtained by collecting meter reading, pushing a meter code to a meter reading and collecting department, calculating the meter reading electric quantity by the meter reading and collecting department, and then performing further time-sharing electric quantity fitting according to the electric quantity data condition:
in a specific embodiment, the time-sharing power fit follows the following principle:
1) And performing time-sharing electric quantity fitting according to the self-marketized electricity consumption condition of the user. The power supply is switched to, the power is not used in the market, and the like, and is not influenced by other power consumption behaviors.
2) When the electric quantity is simulated according to the time interval algorithm corresponding to different time intervals, the electric quantity used by the user is measured according to the peak, the normal time interval and the valley time interval respectively, and specifically, the electric quantity used by each hour is obtained through decomposition by using an arithmetic average method.
3) The peak-valley period is non-integral, and according to the composition of the non-integral period, the non-integral portions are respectively added and then fitted through arithmetic average.
4) For the same type of discontinuous time periods, the power is combined for calculation.
Wherein, the discontinuous same type period refers to a certain peak-to-valley period, and the corresponding 24-point period is not continuous but segmented. For example, peak time periods are 8:00-10:00, and corresponding time points are 12:00-14:00, and when the total electricity quantity of a peak time period of a certain day is calculated, the electricity quantity of the peak time periods is added.
5) Fitting electric quantity or settlement electric quantity (including variable-loss electric quantity and line-loss electric quantity) is distributed to each period of 24 hours, if the distributed electric quantity is uneven due to electric quantity rounding or time-sharing meter code, the power grid enterprise brings the uneven electric quantity difference value into the 23-point period electric quantity of the marketized electricity consumer.
According to the method for restoring the solar energy lost power in the embodiment, the power grid enterprise fits the time-sharing power, and the time-sharing power can be used as a settlement basis for marketized users to participate in the market. The monthly settlement electricity quantity in the spot market is settled by marketizing users to actually fit the time-sharing electricity quantity every day. And fitting deviation electricity quantity caused by time-sharing electricity consumption of settlement and retail package (contract) decomposition electricity quantity to serve as a deviation assessment electricity quantity foundation. When the monthly settlement is carried out and the accumulated daily clearing electric quantity and the total monthly electric quantity are unbalanced, the power grid enterprise settles the unbalanced electric quantity (including the variable electric quantity and the line-loss electric quantity) according to the weighted average value of the current market trading electricity price (package trading price, no deviation assessment and no default price).
In a specific embodiment, the specific algorithm for obtaining the missing electric quantity by fitting the electric quantity according to the time interval algorithm corresponding to different time intervals includes:
1) And calculating the total power consumption of the peak period according to the power consumption of the marketized users in the range of 10:30-11:30 and 19:00-21:00 per day, and dividing the total power consumption by the duration of the peak period for 3 hours to obtain the power consumption of the peak period per hour.
2) And calculating the total electricity consumption of the peak period according to the electricity consumption of the marketized users in the range of 8:30-10:30 and 16:00-19:00 per day, and dividing the total electricity consumption by the time length of 5 hours of the peak period to obtain the electricity consumption of the peak period per hour.
3) And calculating the total electricity consumption of the valley period according to the electricity consumption of the marketized users in the range of 0:00-7:00 and 23:00-24:00 each day, and dividing the total electricity consumption by the period of 8 hours of the valley period to obtain the electricity consumption of the valley period per hour.
4) And calculating the total electricity consumption of the flat period by dividing the total electricity consumption of the flat period by the time length of 8 hours of the flat period according to the electricity consumption of 7:00-8:30, 11:30-16:00 and 21:00-23:00 of the marketized users every day, so as to obtain the electricity consumption of the flat period per hour.
5) Daily 9: the power consumption in the 00 time period is calculated according to the addition of the half-hour power in the peak period and the half-hour power in the flat period, namely (Duan Dianliang + power in the peak period in the flat period) multiplied by 0.5.
6) 11 per day: and the power consumption in the 00 time period is calculated by adding the half-hour power of the peak section and the half-hour power of the peak section. I.e., (peak period charge + peak period charge) ×0.5.
7) 12 per day: and the power consumption in the 00 time period is calculated by adding the half-hour power of the flat section and the half-hour power of the peak section. I.e., (usual period power + peak period power) ×0.5.
In a specific embodiment, the time-sharing electric quantity fitting corresponding time period table is shown in table 1:
TABLE 1 time-sharing electric quantity fitting corresponding period
Fitting method under the condition of loss of peak-valley time-sharing power consumption determined by corresponding time periods in table 1:
1) When the peak-valley time-sharing collection electricity consumption is absent, if the total time period is absent, fitting the total time period electricity quantity according to the sum of the electricity quantities of all time-sharing time periods of the power grid enterprises.
For example: absent the total time period power, the total time period power=peak time period power+peak time period power+flat time Duan Dianliang +valley time period power.
2) When the peak-valley time-sharing collection power consumption is absent, if one time-sharing period is absent, the total time-sharing power consumption of the power grid enterprise is subtracted from the sum of the other time-sharing power consumption, and the power consumption is fitted to the missing time-sharing power consumption.
For example: absent peak period power, peak period power = total period power- (peak period power + flat Duan Dianliang + valley period power).
3) When the peak-valley time-sharing acquisition power consumption is absent, if two or more time-sharing time periods are absent, the power consumption of the corresponding time periods is synthesized by deducting the power consumption of the corresponding time periods according to the same time period proportion of the latest historical same time according to the time attribute. The deduction sequence is sequentially a valley period, a flat period, a peak period and a peak period. The time attribute is divided into workday, double holiday and holiday, and holiday is divided into big holiday and small holiday.
For example: and when the peak period and the valley period electric quantity are short in the period of 4 months and 10 days (working days) in 2020, the valley period electric quantity in the period of 4 months and 10 days is fitted first according to the valley period proportion in the period of 4 months and 9 days (working days). And fitting to generate peak segment electric quantity according to the lack of a time segment fitting algorithm.
For example: and when the peak period and the valley period are short of the electric quantity in the period of 5 months and 1 month (small and long false) in 2020, the electric quantity in the period of 1 month and 1 month in 2020 is fitted first according to the proportion of the valley period in the period of 5 months and 1 month in 2019. And fitting to generate peak segment electric quantity according to the lack of a time segment fitting algorithm.
For example: when the peak period and the valley period are short in the period of 1 (big and false) 10 months in 2020, the valley period electric quantity of 1 month of 10 months in 2020 is fitted first according to the valley period proportion of 1 month of 10 months in 2019. And fitting to generate peak segment electric quantity according to the lack of a time segment fitting algorithm.
For example: and when the peak period, the flat period and the valley period electric quantity are short in the period of 4 months and 10 days (working days) in 2020, the peak period, the flat period and the valley period electric quantity in 4 months and 10 days are fitted first according to the proportion of the valley period and the flat period in 4 months and 9 days (working days). And fitting to generate peak segment electric quantity according to a period-lacking fitting principle.
4) When the peak-valley time-sharing acquisition power consumption is absent, if the total time period is present but the time-sharing time period is absent, the time-sharing power consumption is fitted according to the same time-sharing proportion of the same time with the same time according to the latest historical time attribute.
5) When the peak-valley time-sharing acquisition power consumption is lost, if the acquisition of the meter codes of each time period fails, the on-site verification of acquisition (meter reading) personnel is needed, and the power fitting is not needed due to reasons such as failure power failure, planned power failure and the like, for example, the acquisition (meter reading) failure is caused by the fault of a metering device, and the time-sharing power is fitted according to the time attribute by the latest historical peak-valley time-sharing.
For example: and the acquisition fails on the 21 st of 5 months, but the data on the 20 th of 5 months is that the peak-valley time-sharing electric quantity on the 20 th of 5 months is used as the basis of the fitting time-sharing electric quantity on the 21 st of 5 months. And if the data is not 5 months and 20 days, but the data is 5 months and 19 days, the 5 months and 19 days peak-valley time-sharing electric quantity is used as the basis of the 5 months and 21 days fitting time-sharing electric quantity.
6) When the peak-valley time acquisition power consumption is lost, if the lost period has no history basis reference, calculating the lost period according to an arithmetic average value.
7) For the load with small fluctuation of power consumption of a communication base station and the like and stable power consumption in each period, the average value of the daily power consumption for 24 hours can be taken as the time-sharing power consumption.
And 2.2, fitting the non-time-sharing electric quantity by adopting a similar day algorithm. The similarity day fitting algorithm is shown in fig. 1, namely, firstly, matching factor analysis screening is performed to determine external factors, the external factors mainly considered in the embodiment include three factors of weather, time and date, objective weighting method is adopted to measure and calculate weights for the matching factors of the three factors, weighting is performed to obtain final matching factors of the three factors respectively, comprehensive similarity is further calculated according to the final matching factors, finally, the date with the highest comprehensive matching factor is selected to be used as a similarity day, and matching of missing day missing time points is performed by correlating the matching factors with electric quantity of the similarity day.
And 2.2.1, analyzing the correlation between the fitting day of the missing electric quantity and the historical non-fitting day and external factors by adopting a correlation analysis method. The external factors include three factors of weather, time and date type.
And 2.2.2, searching the date with highest similarity between the characteristic type and the fitting date of the missing electric quantity in the non-fitting dates of the history according to the correlation as a similarity date.
And 2.2.2.1, calculating matching coefficients of three factors of weather, time and date types.
(1) Date type factor matching coefficient calculation
In this embodiment, the mapping of the working day is set to 0.1, the mapping value of the holiday is set to 0.2, the mapping value of the holiday is set to 0.3, the mapping value of the holiday is set to 0.7, and the mapping value of the spring festival is set to 1. Calculating the week factor matching coefficient of the ith history day and the fitting day by adopting the following calculation formulaThe larger the date type factor matching coefficient should be, the larger the date type similarity degree is.
Wherein,X i representing the ith historyThe day of which,X 0 the fitting day is represented, and the date types are Monday, tuesday, friday, double holidays, daholidays and spring festival respectively; f (X) i )、f(X 0 ) Is X i And X 0 Mapped values.
(2) Meteorological factor matching coefficient calculation
In the embodiment, the similarity degree of weather on the history day and weather on the fitting day is represented by a weather factor matching coefficient, in the fitting algorithm of the embodiment, the weather on the history day and weather on the fitting day are analyzed by a gray correlation analysis method, and the higher the matching coefficient is, the higher the similarity of weather conditions is, and the calculation formula is as follows:
wherein,for the resolution factor, take the value in (0, 1), if +.>The smaller the correlation coefficient, the larger the difference between the correlation coefficients, the stronger the discrimination ability, usually +.>Taking 0.5.Xi represents the i-th history day, X0 represents the fitting day, < >>The K-th gray correlation coefficient representing the i-th factor is k=1.
(3) Time factor matching coefficient calculation
The time factor is the time of the historical day from the day to be fitted, i.e. the instant difference. In the design of the time factor matching algorithm, the embodiment simultaneously considers the rules of load change and holiday annual change, and calculates the time factor matching coefficient by adopting the following formula:
wherein int is rounding, mod is a remainder function, t is the number of days of the i-th history day distance fitting day,S i is a variable (value 0, 1), when the i-th history day and the fitting day are the same holiday,S i taking a value of 1, otherwise taking a value of 0;β 1 、β 2 、β 3 is an attenuation coefficient, and generally takes a value of 0.9-0.98, which respectively represents the similar reduction ratio of one week and one year when the distance between the history day and the fitting day is increased every day,N 1 、N 2 、N 3 is a constant value, and is a function of the constant,N 1 andN 2 a value of 7, representing the number of days of the week, for certain major holidays (e.g. five-one, national celebrations) the distance between them is less than 365,N 3 the value of (2) may be suitably reduced (between 340-365).
And 2.2.2.2, weighting the matching coefficients of the three factors of weather, time and date types to obtain final matching coefficients of the three factors respectively.
Calculating the matching coefficient of the meteorological factors, the matching coefficient of the time factors and the matching coefficient of the date type factors by using a matching coefficient calculation model; measuring and calculating the weights of three factors of weather, time and date types through an objective weight algorithm; and the final matching coefficients of the three factors are respectively obtained by multiplying the weight and the matching factor.
More specifically, the matching coefficients of the influence factors are comprehensively considered, each influence factor is weighted by using an objective weighting method, the influence weight of each influence factor is obtained, and the weighting process of each influence factor is as follows:
A. the date factor, weather factor and time factor data are standardized, the dimension of each index is removed, and the standardized formula is as follows:
wherein,Y mj is standardized to the index of the index is that,maximum value of index, & gt>Is the minimum value of the index, and the index is the minimum value of the index,S mj is any index value of the index.
B. Calculating the proportion of the jth index to the index in the mth scheme, wherein the calculation method comprises the following steps:
wherein P is mj The j-th index is the specific gravity of the index in the m-th scheme,Y mj the standard index is obtained, and n is the number of schemes.
C. The information entropy of each index is calculated, and the calculation method is as follows:
wherein,E j the information entropy of the j-th index,E j >=0, ifP mj =0, defineE j =0，P mj The j index is the proportion of the m index in the m scheme, and n is the number of schemes.
D. And determining the weight of each index through information entropy calculation, wherein the calculation formula is as follows:
（j=1,2,3）
wherein,as the weight of the j-th index,E j and (5) information entropy of the j-th index.
If the weights of the date type factor, the sub-weather factor, the time factor are respectivelyThe three factors eventually match the coefficients:
final date type factor matching coefficientThe method comprises the following steps: />。
Final weather factor matching coefficientThe method comprises the following steps: />。
Final time factor matching coefficientThe method comprises the following steps: />。
Wherein,、/>and->The date type factor matching coefficient, the weather factor matching coefficient and the time factor matching coefficient which are calculated before are respectively calculated.
Step 2.2.2.3, the comprehensive similarity is calculated according to the final matching coefficients of the three factors.
And multiplying the three final influence factors to obtain the comprehensive similarity. Specifically, the final date type factor matching coefficient, the final weather factor matching coefficient and the final time factor matching coefficient are multiplied to obtain the comprehensive matching coefficient. The larger the comprehensive matching coefficient is, the closer the selected similarity day and fitting day characteristics are.
In step 2.2.2.4, the date with the highest comprehensive similarity is finally selected as the similarity date.
And 2.2.3, supplementing the missing electric quantity on the fitting day according to the electric quantity curve on the similar day. Specifically, after the similar day is determined, the electric quantity at the time of missing fitting day is calculated by taking the electric quantity proportion of each period of the similar day as a reference according to the electric quantity curve of the similar day.
In a specific embodiment, according to a matching algorithm of the similar days, determining the later time of the similar days, taking the electric quantity proportion of each period of the similar days as a reference, if the electric quantity of 0 point or 24 points is lost, calculating the electric quantity of 0 point or 24 points according to the proportion, then calculating the total electric quantity of the time point of the lost part according to a difference method, and then calculating the electric quantity of the lost time point according to the proportion of the similar days; if the electric quantity of the 0 point or the 24 points is not lost, the total electric quantity of the lost part is directly calculated by adopting a difference method, and then the electric quantity fitting of the lost time point is carried out according to the similar daily proportion.
And supplementing the missing electric quantity on the fitting day by using the calculation result.
And step 3, after the missing electric quantity is obtained, carrying out electric charge compensation on the user according to the missing electric quantity, and carrying out table code adjustment by considering the table code condition of the last month when carrying out electric charge calculation in the next month.
According to the method for repairing the solar clear missing electric quantity, the fitting of the missing data of the meter reading and accounting service can be completed, the meter reading and the electric quantity cutting calculation (removing the non-marketized electric quantity) can be carried out by assisting the meter reading and accounting service of the power grid enterprise in each day, the electric quantity and electric charge clearing can be carried out in a month, the meter reading and accounting service can be promoted to be converted into continuity in stages, and an electric quantity settlement basis is provided for wholesale market users such as electricity selling companies, direct transaction users and the like.
Embodiment two:
the second embodiment of the invention provides a system for repairing solar heat lost electric quantity, which comprises:
the power acquisition module is configured to acquire the power consumption of a user and is divided into non-time-sharing power and time-sharing power according to the power consumption acquisition mode;
the fitting analysis module is configured to fit the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain the missing electric quantity;
the fitting analysis module comprises a time-sharing electric quantity fitting module and a non-time-sharing electric quantity fitting module, wherein the time-sharing electric quantity fitting module is configured to fit the time-sharing electric quantity by adopting a time-sharing analysis method, and the specific steps are as follows:
determining a period corresponding to the electric quantity when the electric quantity is missing;
and fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity.
The non-time-sharing electric quantity fitting module is configured to fit the non-time-sharing electric quantity by adopting a similar day algorithm, and comprises the following specific steps of:
adopting a correlation analysis method to analyze the correlation between the fitting day of the missing electric quantity and the history non-fitting day and external factors;
searching a date with highest similarity between the feature type and the fitting day of the missing electric quantity in the non-fitting days of the history according to the correlation as a similarity day;
and supplementing the missing electric quantity of the fitting day according to the electric quantity curve of the similar day.
The steps involved in the second embodiment correspond to those of the first embodiment, and reference is made to the relevant description of the first embodiment for the implementation manner.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (8)
1. The method for repairing the solar heat lost electric quantity is characterized by comprising the following steps of:
the method comprises the steps of obtaining the electricity consumption of a user, and dividing the electricity consumption into non-time-sharing electricity consumption and time-sharing electricity consumption according to the obtaining mode of the electricity consumption;
fitting the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain a missing electric quantity;
the time division electric quantity is fitted by adopting a time-sharing analysis method, and the specific steps are as follows:
determining a period corresponding to the electric quantity when the electric quantity is missing;
fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity;
fitting the non-time-sharing electric quantity by adopting a similar day algorithm, wherein the specific steps are as follows:
adopting a correlation analysis method to analyze the correlation between the fitting day of the missing electric quantity and the non-fitting day of the history and external factors, wherein the external factors comprise three factors of weather, time and date type;
searching a date with highest similarity between the feature type and the fitting day of the missing electric quantity in the non-fitting days of the history according to the correlation as a similarity day; the specific steps of searching the date with highest similarity between the characteristic type and the fitting date of the missing electric quantity in the non-fitting dates of the history according to the correlation as the similarity date are as follows:
calculating matching coefficients of three factors of weather, time and date types;
weighting the matching coefficients of the three factors of weather, time and date types to obtain final matching coefficients of the three factors respectively;
calculating comprehensive similarity according to the final matching coefficients of the three factors;
finally, selecting the date with highest comprehensive similarity as a similarity date;
and supplementing the missing electric quantity of the fitting day according to the electric quantity curve of the similar day.
2. The method for repairing solar heat lost electricity according to claim 1, wherein the electricity obtained by the electricity obtaining mode with peak-valley section segmented collection function is called time-sharing electricity, and the electricity obtained by the electricity obtaining mode without peak-valley section segmented collection function is called non-time-sharing electricity.
3. The method for restoring the solar power of claim 2, wherein the peak-valley sections include a peak, a peak section, a normal section and a valley section.
4. The method for repairing solar heat lost electric quantity according to claim 3, wherein when electric quantity fitting is carried out according to a time interval algorithm corresponding to different time intervals, the electric quantity of a user is respectively measured according to a peak, a peak segment, a normal segment and a valley segment, and specifically, the electric quantity of each hour is obtained by decomposing by using an arithmetic average method; non-integral points of the peak-valley period are respectively added and then fitted through arithmetic average values according to the composition of the non-integral points; for the same type of discontinuous time periods, the power is combined for calculation.
5. The method for repairing solar heat lost electric quantity according to claim 1, wherein the specific steps of weighting the matching coefficients of three factors of weather, time and date type to obtain the final matching coefficients of the three factors respectively are as follows:
calculating the matching coefficient of the meteorological factors, the matching coefficient of the time factors and the matching coefficient of the date type factors by using a matching coefficient calculation model;
measuring and calculating the weights of three factors of weather, time and date types through an objective weight algorithm;
and the final matching coefficients of the three factors are respectively obtained by multiplying the weight and the matching factor.
6. The method for repairing solar heat lost electric quantity according to claim 1, wherein the specific steps of completing the lost electric quantity on the fitting day according to the electric quantity curve on the similar day are as follows:
after the similar day is determined, calculating the electric quantity at the missing time point of the fitting day by taking the electric quantity proportion of each period of the similar day as a reference according to the electric quantity curve of the similar day;
and supplementing the missing electric quantity on the fitting day by using the calculation result.
7. The method for repairing solar heat lost power according to claim 1, wherein after the lost power is obtained, the electric charge is supplemented according to the lost power, and the table code is adjusted by considering the table code condition of the last month when the electric charge is calculated in the next month.
8. A solar heat recovery system for recovering lost power, comprising:
the power acquisition module is configured to acquire the power consumption of a user and is divided into non-time-sharing power and time-sharing power according to the power consumption acquisition mode;
the fitting analysis module is configured to fit the non-time-sharing electric quantity and the time-sharing electric quantity respectively to obtain the missing electric quantity;
the fitting analysis module comprises a time-sharing electric quantity fitting module and a non-time-sharing electric quantity fitting module, wherein the time-sharing electric quantity fitting module is configured to fit the time-sharing electric quantity by adopting a time-sharing analysis method, and the specific steps are as follows:
determining a period corresponding to the electric quantity when the electric quantity is missing;
fitting the electric quantity according to a time period algorithm corresponding to different time periods to obtain the missing electric quantity;
the non-time-sharing electric quantity fitting module is configured to fit the non-time-sharing electric quantity by adopting a similar day algorithm, and comprises the following specific steps of:
adopting a correlation analysis method to analyze the correlation between the fitting day of the missing electric quantity and the non-fitting day of the history and external factors, wherein the external factors comprise three factors of weather, time and date type;
searching a date with highest similarity between the feature type and the fitting day of the missing electric quantity in the non-fitting days of the history according to the correlation as a similarity day; the specific steps of searching the date with highest similarity between the characteristic type and the fitting date of the missing electric quantity in the non-fitting dates of the history according to the correlation as the similarity date are as follows:
calculating matching coefficients of three factors of weather, time and date types;
weighting the matching coefficients of the three factors of weather, time and date types to obtain final matching coefficients of the three factors respectively;
calculating comprehensive similarity according to the final matching coefficients of the three factors;
finally, selecting the date with highest comprehensive similarity as a similarity date;
and supplementing the missing electric quantity of the fitting day according to the electric quantity curve of the similar day.
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