CN111126656A - Electric energy meter fault quantity prediction method - Google Patents
Electric energy meter fault quantity prediction method Download PDFInfo
- Publication number
- CN111126656A CN111126656A CN201911091522.6A CN201911091522A CN111126656A CN 111126656 A CN111126656 A CN 111126656A CN 201911091522 A CN201911091522 A CN 201911091522A CN 111126656 A CN111126656 A CN 111126656A
- Authority
- CN
- China
- Prior art keywords
- model
- electric energy
- data
- sequence
- predicting
- 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
- 238000000034 method Methods 0.000 title claims abstract description 49
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims abstract description 53
- 238000009499 grossing Methods 0.000 claims abstract description 20
- 238000005311 autocorrelation function Methods 0.000 claims description 33
- 230000001932 seasonal effect Effects 0.000 claims description 18
- 230000007774 longterm Effects 0.000 claims description 17
- 238000005314 correlation function Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 6
- 125000004122 cyclic group Chemical group 0.000 claims description 5
- 230000002441 reversible effect Effects 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 abstract description 22
- 238000004458 analytical method Methods 0.000 description 12
- 230000001788 irregular Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 2
- 241001123248 Arma Species 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for predicting the number of faults of an electric energy meter, and relates to a fault prediction method. The prior art is about the demand of the electric energy meters or a rotation method, and the number of faults of a batch of electric energy meters per month is not predicted. The invention comprises the following steps: firstly, for imported data, a moving average sequence of the sequence is obtained, then an ARIMA model is created for the moving average sequence, and the model is used for predicting the future of the moving average sequence. And finally, restoring the seasonality of the predicted data to obtain a prediction result. According to the technical scheme, a time sequence decomposition model, an exponential smoothing model and a time sequence ARIMA model are combined for use, so that the advantages can be made up for the disadvantages; the accuracy of predicting the fault number of the electric energy meter is improved, the quality risk and property loss caused by the overdue and overstock of the electric energy meter are avoided economically, the cost input of the overdue meter return is reduced, and the overstock of the stored electric energy meter is avoided.
Description
Technical Field
The invention relates to a fault prediction method, in particular to a fault quantity prediction method for an electric energy meter.
Background
With the gradual increase of power equipment, the requirement on the electric energy meter is higher and higher, the fault problem of the electric energy meter is more and more prominent, and the fault quantity is difficult to determine. The current prediction technology only depends on the existing model to predict the demand of the electric energy meter, the number of faults of the electric energy meter cannot be accurately determined, the problems of poor timeliness and low accuracy rate exist, and the problem that the existing model is used for predicting the fault which possibly does not accord with the actual situation exists.
The prior art is about the demand of the electric energy meters or a rotation method, and the number of faults of a batch of electric energy meters per month is not predicted.
Disclosure of Invention
The technical problem to be solved and the technical task to be solved by the invention are to perfect and improve the prior technical scheme and provide a method for predicting the fault quantity of the electric energy meter, so as to achieve the aim. Therefore, the invention adopts the following technical scheme.
A method for predicting the fault quantity of an electric energy meter comprises the following steps:
1) acquiring fault table data;
2) calculating a moving average sequence of the obtained sequence according to the fault table data;
3) obtaining a moving average sequence, determining whether an ARIMA model or an exponential smoothing model is used for prediction according to long-term trend and seasonal variation, wherein most of the prediction data in the ARIMA model is matched;
4) restoring the obtained prediction data to the seasonality of the data;
5) acquiring data after the seasonality is restored, and predicting the number of fault tables according to the data;
wherein the setting of the ARIMA model comprises the steps of:
a) acquiring historical data of a fault table;
b) calculating an autocorrelation function and a partial correlation function according to the acquired historical data; utilizing the autocorrelation function and the partial correlation function to carry out order determination on the ARIMA model;
c) method for moment estimation to determine model parameters
d) Verifying model difference times with significance levels
e) And obtaining the set ARIMA model according to the fixed order, the parameters and the difference times obtained by calculation.
The technical scheme combines time sequence decomposition, an exponential smoothing model and an ARIMA model analysis method; the problem of prediction by a single method is solved; if the time decomposition method is adopted, the time series decomposition is to decompose a long-term trend factor, a seasonal variation factor, a cyclic variation factor and an irregular variation factor and then fit the long-term trend, and the fitting is generally curve fitting, so the effect is poor. Therefore, time sequence decomposition is generally not used independently, simple sequence prediction is inaccurate, a complex sequence model is too thin, and the prediction effect is not ideal in any word, so that analysis is carried out on the basis of time prediction decomposition according to long-term trends and seasonal variations in a moving average sequence, and the analysis and prediction of the roughly decomposed trend are determined by using a mature ARIMA model or an exponential smoothing method model. For ARIMA models and exponential smoothing models. When the model is established, the model is easily influenced by large random fluctuation change of data, and the error of a prediction result is overlarge through data analysis. A time series decomposition model is introduced to solve this problem as much as possible. The first step of the time series decomposition model is used to find the moving average series, i.e. the long-term trend T and the part of seasonal variation C. But the TC portion does not necessarily have a T portion. Seasonal minute fluctuations, i.e., seasonal variations, and occasional variations, i.e., irregular variations, of the data are removed. The fluctuation of the fault number of the electric energy meter can be slowed down, and the final prediction error is reduced. However, the TC part still has seasonal variation through a large amount of data analysis. Analysis is performed based on long-term trends and seasonal variations in the moving average sequence to determine an ARIMA or exponential smoothing model.
And (4) according to actual data, repeatedly predicting and proposing reverse moving average. And the data is processed reversely by adopting moving average, namely the predicted number is increased by two times, then the last number and the next number are subtracted to obtain a result, and for the last number, the last number is increased by one time and the last number is subtracted. And finally, carrying out integral treatment on the prediction result to obtain a final prediction result. Seasonal and irregular variations in the reduced sequence. The technical scheme provides a comprehensive time series prediction model based on three models, namely a time series decomposition model, an ARIMA (single product autoregressive moving average) model and an exponential smoothing model. The time sequence decomposition model reduces the fluctuation of data, the ARIMA model can predict unstable sequences, the exponential smoothing model can predict long-term trend sequences, and the three models in the time sequence are combined appropriately to form a comprehensive time sequence model.
As a preferable technical means: in the step 1), the monthly fault number of a certain batch of electric energy meters is collected through the electricity utilization information collection and remote meter reading system. The collected data can be stored in a central database through a data transmission channel and a data receiving system, and the data in the database is used as a data source for predicting the fault quantity of the electric energy meter.
As a preferable technical means: in the step 2), the data is processed by adopting a moving average, and the data of three months are added by adopting the moving average to calculate the average value, so that the number which does not contain seasonality and has small or no random variation factor is obtained; for each datum, the upper number, the lower number and the number are added and averaged, thus obtaining a sequence without the first and the last datum, and for the last datum, the upper number is added and averaged, the first number is not taken into account, thus obtaining a sequence which only comprises two parts of long-term trend and cyclic variation.
As a preferable technical means: in step 3), the ARIMA model and the exponential smoothing model are easily influenced by large random fluctuation variation of data when being established. A time series decomposition model is introduced to solve this problem as much as possible. The first step of the time series decomposition model is used to find the moving average sequence, i.e. the TC part. But the TC portion does not necessarily have a T portion. Seasonal minute fluctuations, i.e., seasonal variations, and occasional variations, i.e., irregular variations, of the data are removed. The fluctuation of the fault number of the electric energy meter can be slowed down, and the final prediction error is reduced. However, the TC part still has seasonal variation through a large amount of data analysis. Analysis is performed based on long-term trends and seasonal variations in the moving average sequence to determine an ARIMA or exponential smoothing model.
As a preferable technical means: in step 4), the data are processed by adopting a moving average in a reverse direction, the number obtained by prediction is increased by two times, then the last number and the next number are subtracted to obtain a final result, and the last number is increased by one time and subtracted by the last number; and finally, carrying out integral treatment on the prediction result to obtain a final prediction result.
As a preferable technical means: in step b), the method for determining the order p, q value of the model is as follows:
sequence ytThe autocorrelation function measures ytAnd yt-kDegree of linear correlation between them, using pkExpressed, the definition formula is:in the formula,rk=cov(yt,yt-k);r0=cov(yt,yt) Represents the variance of the sequence; the autocorrelation function is characterized by ytAnd yt-kThe degree of linear correlation between; when y istAnd yt-kThere is a correlation between ytAnd yt-kRespectively with their middle part yt-1,yt-2,…,yt-k+1If there is a relationship between given yt-1,yt-2,…,yt-k+1On the premise of (b) for ytAnd yt-kThe conditional correlation between them is described by the partial autocorrelation functionProceeding, the partial autocorrelation function can be represented by the formula:
observing which step of the autocorrelation function trails after the autocorrelation function and the partial autocorrelation function are obtained, and recording as p; marking the step in which the partial correlation function trails as q; the order p and q of the model can be determined; the tail refers to the fact that the model autocorrelation function or partial correlation function exhibits an exponential decay and tends towards 0 with increasing time lag k.
As a preferable technical means: in step c), the method for determining parameters by using moment estimation comprises the following steps:
when k is in the model>q, its autocorrelation coefficient satisfies the equation of the autoregressive section:using estimated autocorrelation coefficientsInstead of rhokK is q +1, q +2, … and q + p to obtain p equations, and solving the equations to obtain the self-healingMoment estimation of return coefficients
Order toThenWherein,then obtained by calculationInstead of the former Instead of gammakThe following can be obtained:
the formula for ARIMA can be derived:further to find outAnd then will beSolving the first two equations by substituting the moving average theta of the ARIMA model1,θ2,…,θqAnd white noise sequence εtMean square errorEstimating the moment of (2);
thus, parameters in the ARIMA model are found: autoregressive coefficient, moving average and white noise sequence epsilontThe mean square error.
In the step d), the difference number of the model is determined by the significance level, wherein the significance level is the probability that the estimation overall parameter is in a certain interval and can make errors, the smaller the α value is, the better the model is, and when d is 0,1,2 and 3, the significance α value of the model is respectively calculated, and the difference number d is determined.
Has the advantages that: according to the technical scheme, the number of the electric energy meters with faults is predicted by combining a time series decomposition method, an exponential smoothing model method and an ARIMA model analysis method, and the final prediction result is obtained by adding seasonality to the prediction result, so that the accuracy of predicting the number of the electric energy meters with faults can be improved. Economically, the quality risk and property loss caused by the overdue and overstock of the electric energy meter are avoided, the cost investment for returning the overdue meter is reduced, and the overstock of the stored electric energy meter is avoided. The time series decomposition model, the exponential smoothing model and the time series ARIMA model are combined for use, and the advantages can be made up for the disadvantages.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow chart of ARIMA model setting of the present invention.
Fig. 3 is a data graph of the number of faults of the electric energy meter in each month from 2013 to 2018 and 9 in a certain batch of electric energy meters.
Fig. 4 is a graph of the number of failures from 6 months to 8 months in 2018 predicted by the moving average sequence.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, the present invention comprises the steps of:
1) acquiring fault table data;
2) calculating a moving average sequence of the obtained sequence according to the fault table data;
3) obtaining a moving average sequence, determining whether an ARIMA model or an exponential smoothing model is used for prediction according to long-term trend and seasonal variation, wherein most of the prediction data in the ARIMA model is matched;
4) restoring the obtained prediction data to the seasonality of the data;
5) acquiring data after the seasonality is restored, and predicting the number of fault tables according to the data;
as shown in fig. 2, the setting of ARIMA model includes the steps of:
a) acquiring historical data of a fault table;
b) calculating an autocorrelation function and a partial correlation function according to the acquired historical data; utilizing the autocorrelation function and the partial correlation function to carry out order determination on the ARIMA model;
c) method for moment estimation to determine model parameters
d) Verifying model difference times with significance levels
e) And obtaining the set ARIMA model according to the fixed order, the parameters and the difference times obtained by calculation.
The method is used for predicting and controlling the number of the faults of the electric energy meters, so that preparation is made for better and more accurately rotating the electric energy meters, the number of the electric energy meters needing to be replaced can be roughly determined according to the prediction result of the method, a purchase plan can be made in advance, and the rotating number of the electric energy meters in each region can be effectively distributed.
Specifically, the technical scheme combines a time series decomposition method, an exponential smoothing model and an ARIMA model analysis method; the problem of prediction by a single method is solved; if the time decomposition method is adopted, the time series decomposition is to decompose a long-term trend factor, a seasonal variation factor, a cyclic variation factor and an irregular variation factor and then fit the long-term trend, and the fitting is generally curve fitting, so the effect is poor. Therefore, time sequence decomposition is generally not used independently, simple sequence prediction is inaccurate, a complex sequence model is too thin, and the prediction effect is not ideal in any word, so that analysis is carried out on the basis of time prediction decomposition according to long-term trends and seasonal variations in a moving average sequence, and the analysis and prediction of the roughly decomposed trend are determined by using a mature ARIMA model or an exponential smoothing method model. For ARIMA models and exponential smoothing models. When the model is established, the model is easily influenced by large random fluctuation change of data, and the error of a prediction result is overlarge through data analysis. A time series decomposition model is introduced to solve this problem as much as possible. For an ARIMA model, the order of the model is determined by an autocorrelation function and a partial correlation function, the model parameters are estimated by using moments, and finally, the difference times are determined by using the significance level, so that the rough trend obtained by time series decomposition is predicted after the model is obtained. The prediction result and the seasonality are added to obtain a final prediction result, so that the accuracy of predicting the fault number of the electric energy meter can be improved. Economically, the quality risk and property loss caused by the overdue and overstock of the electric energy meter are avoided, the cost investment for returning the overdue meter is reduced, and the overstock of the stored electric energy meter is avoided. For the ARIMA model, an exponential smoothing method is adopted to analyze long-term trends, seasonal variation analysis can be added according to actual conditions, then the law of each variation is calculated respectively, and finally the laws of each variation are integrated to form the exponential smoothing model for predicting future data. The time series decomposition model, the exponential smoothing model and the time series ARIMA model are combined for use, and the advantages and the disadvantages can be made up. In particular practice most match the ARIMA model.
The following steps are further specified:
1. data source
The monthly fault number of a certain batch of electric energy meters is collected by adopting an electricity utilization information collection and remote meter reading system, and data is stored in a central database through a data transmission channel and a data receiving system to serve as a data source for predicting the fault number of the electric energy meters by the method.
The analysis object of the method is the fault number of the electric energy meter, namely the number of faults of the electric energy meter per month in a certain batch.
2. Removing seasonality of data
And decomposing the model by using a time series to remove seasonality. Namely, the data is processed by the moving average, and the data of three months are added by the moving average to obtain the average value, so that the number is free from seasonality and has little or no irregular variation factor, namely randomness. Since randomness fluctuates around the median value, the three numbers are added, and the positive and negative fluctuations cancel each other to some extent, it can be considered that there has been no randomness therein. For each data, the upper number, the lower number and the number are added and averaged, thus obtaining a sequence without the first and the last data, and for the last data, the upper number is added and averaged, and the first number is not considered. Thus, a sequence is obtained, which includes only two parts of long-term trend and cyclic variation, and the sequence is called a moving average sequence.
3. ARIMA model
A sequence { XtD-order difference of the sequence is changed into a stable sequence, and the stable sequence after the difference can be modeled by using an ARMA model, so that the sequence is called as { X }tThe model structure of the method is a summation moving average model, which is called ARIMA (p, d, q) for short, wherein d is the difference order, p is the autoregressive order, and q is the moving average order. The main formula expression of the model is To apply the model, the model is determined by scaling and then estimating its parameters, i.e., the autoregressive coefficients, the moving average and the white noise sequence εtThe mean square error.
4. Determining the order p and q of an ARIMA model
The ARIMA model is adopted to model the existing data, the primary problem is to determine the order of the model, namely the corresponding p and q values, and the identification of the ARIMA model is mainly carried out through the autocorrelation function and the partial autocorrelation function of the sequence. Sequence ytThe autocorrelation function measures ytAnd yt-kDegree of linear correlation between them, using pkExpressed, the definition formula is:in the formula, rk=cov(yt,yt-k);r0=cov(yt,yt) Representing the variance of the sequence. The autocorrelation function is characterized by ytAnd yt-kDegree of linear correlation between, and sometimes ytAnd yt-kThere is a correlation between, probably because of ytAnd yt-kRespectively with their middle part yt-1,yt-2,…,yt-k+1If at a given y there is a relationship betweent-1,yt-2,…,yt-k+1On the premise of (b) for ytAnd yt-kThe conditional correlation between them is described by the partial autocorrelation functionProceeding, the partial autocorrelation function can be represented by the formula: and (4) calculating.
Observing which step of the autocorrelation function trails after the autocorrelation function and the partial autocorrelation function are obtained, and recording as p; at which step the partial correlation function trails, denoted as q. The order p and q of the model can be determined. The tail refers to the fact that the model autocorrelation function or partial correlation function exhibits an exponential decay and tends towards 0 with increasing time lag k.
5. Estimation of ARIMA model parameters
For ARIMA model, determining parameters by using moment estimation method, and determining k in the model>q, its autocorrelation coefficient satisfies the equation of the autoregressive section: using estimated autocorrelation coefficientsInstead of rhokK is q +1, q +2, …, q + p to obtain p equations, and the moment estimation of the autoregressive coefficient obtained by solving the equation set
Order toThenWherein,then obtained by calculationInstead of the former Instead of gammakThe following can be obtained:the formula for ARIMA can be derived: further to find outAndthen will beSolving the first two equations by substituting the moving average theta of the ARIMA model1,θ2,…,θqAnd white noise sequence εtMean square errorIs estimated.
All parameters in the ARIMA model are thus found: autoregressive coefficient, moving average and white noise sequence epsilontThe mean square error.
6. Determining the number of differences d of the model
The difference number of the model is determined by using the significance level, wherein the significance level is the probability that the estimation overall parameter falls in a certain interval and a fault is possibly made, and is represented by α, the model is better when the α value is smaller, when d is 0,1,2 and 3, the significance α value of the model is respectively calculated, the difference number d is determined, the difference number cannot be too large, and otherwise, the original model is changed into another model.
7. Restoring seasonality of data
And processing the data by adopting a moving average in the reverse direction, wherein the predicted number is increased by two times, and then the last number and the next number are subtracted to obtain a final result, and for the last number, the last number is increased by one time and subtracted by the last number. And finally, carrying out integral treatment on the prediction result to obtain a final prediction result.
The effects obtained by the present solution are illustrated by way of example below
The implementation is shown in fig. 3 and 4. Fig. 3 is data of the number of faults of the electric energy meter in a certain batch of electric energy meter in the period from 3 months in 2013 to 9 months in 2018, and fig. 4 is the number of faults in the period from 6 months in 2018 to 8 months predicted by the moving average sequence; the upper right-hand icon is the training sequence, predicted data and actual sequence of the moving average sequence from top to bottom.
For 67 total faults from 2013.3 to 2018.9, the method is adopted for analysis, models are built by using the first 63 faults to obtain an ARIMA (1,1,3) model, the models are used for predicting the faults of a moving average sequence, then the moving average is used reversely, the seasonality is increased, the data are integrated, and the predicted final results from 6 months to 8 months in 2018 are 27,19 and 17; error values are 5,2,2 compared to the actual data 22,21, 19; the error is small, and the model is verified to be usable. The method can predict the failure number of the electric energy meter from 10 months to 12 months in 2018, and the prediction results are 13,15 and 19. The method can provide a scientific basis for predicting the number of faults in the future month for the power consumption department, and approximately provides more accurate data for the number of the electric energy meters to be replaced prepared in the future months of the batch of the electric energy meters.
The method for predicting the number of faults of the electric energy meter shown in fig. 1 and 2 is a specific embodiment of the present invention, which already embodies the essential features and the improvements of the present invention, and can be modified equivalently according to the practical use requirements and under the teaching of the present invention, and all that is within the protection scope of the present solution.
Claims (7)
1. A method for predicting the fault quantity of an electric energy meter is characterized by comprising the following steps:
1) acquiring fault table data;
2) calculating a moving average sequence of the obtained sequence according to the fault table data;
3) acquiring a moving average sequence, determining to adopt an ARIMA model or an exponential smoothing model for prediction according to long-term trend and seasonal variation, and importing data into a set ARIMA model for predicting data when the ARIMA model is matched;
4) restoring the obtained prediction data to the seasonality of the data;
5) acquiring data after the seasonality is restored, and predicting the number of fault tables according to the data;
wherein the setting of the ARIMA model comprises the steps of:
a) acquiring historical data of a fault table;
b) calculating an autocorrelation function and a partial correlation function according to the acquired historical data; utilizing the autocorrelation function and the partial correlation function to carry out order determination on the ARIMA model;
c) method for moment estimation to determine model parameters
d) Verifying model difference times with significance levels
e) And obtaining the set ARIMA model according to the fixed order, the parameters and the difference times obtained by calculation.
2. The method for predicting the fault quantity of the electric energy meter according to claim 1, wherein the method comprises the following steps: in the step 1), the monthly fault number of a certain batch of electric energy meters is collected through the electricity utilization information collection and remote meter reading system.
3. The method for predicting the fault quantity of the electric energy meter according to claim 2, wherein the method comprises the following steps: in the step 2), the data is processed by adopting a moving average, and the data of three months are added by adopting the moving average to calculate the average value, so that the number which does not contain seasonality and has small or no random variation factor is obtained; for each datum, the upper number, the lower number and the number are added and averaged, thus obtaining a sequence without the first and the last datum, and for the last datum, the upper number is added and averaged, the first number is not taken into account, thus obtaining a sequence which only comprises two parts of long-term trend and cyclic variation.
4. The method for predicting the fault quantity of the electric energy meter according to claim 3, wherein the method comprises the following steps: in step 4), the data are processed by adopting a moving average in a reverse direction, the number obtained by prediction is increased by two times, then the last number and the next number are subtracted to obtain a final result, and the last number is increased by one time and subtracted by the last number; and finally, carrying out integral treatment on the prediction result to obtain a final prediction result.
5. The method for predicting the fault quantity of the electric energy meter according to claim 1, wherein the method comprises the following steps: in step b), the method for determining the order p, q value of the model is as follows:
sequence ytThe autocorrelation function measures ytAnd yt-kDegree of linear correlation between them, using pkExpressed, the definition formula is:in the formula, rk=cov(yt,yt-k);r0=cov(yt,yt) Represents the variance of the sequence; the autocorrelation function is characterized by ytAnd yt-kThe degree of linear correlation between; when y istAnd yt-kThere is a correlation between ytAnd yt-kRespectively with their middle part yt-1,yt-2,…,yt-k+1If there is a relationship between given yt-1,yt-2,…,yt-k+1On the premise of (b) for ytAnd yt-kThe conditional correlation between them is described by the partial autocorrelation functionProceeding, the partial autocorrelation function can be represented by the formula:
observing which step of the autocorrelation function trails after the autocorrelation function and the partial autocorrelation function are obtained, and recording as p; marking the step in which the partial correlation function trails as q; the order p and q of the model can be determined; the tail refers to the fact that the model autocorrelation function or partial correlation function exhibits an exponential decay and tends towards 0 with increasing time lag k.
6. The method for predicting the fault quantity of the electric energy meter according to claim 5, wherein the method comprises the following steps: in step c), the method for determining parameters by using moment estimation comprises the following steps:
when k > q in the model, its autocorrelation coefficient satisfies the equation of the autoregressive section:
by estimated selfCorrelation coefficientInstead of rhokK is q +1, q +2, …, q + p to obtain p equations, and the moment estimation of the autoregressive coefficient obtained by solving the equation set
Order toThenWherein,then obtained by calculationInstead of the former Instead of gammakThe following can be obtained:
the formula for ARIMA can be derived:further to find outAnd then will beSolving the first two equations by substituting the moving average theta of the ARIMA model1,θ2,…,θqAnd white noise sequence εtMean square errorEstimating the moment of (2);
thus, parameters in the ARIMA model are found: autoregressive coefficient, moving average and white noise sequence epsilontThe mean square error.
7. The method for predicting the fault quantity of the electric energy meter is characterized in that in the step d), the difference times of the model are determined by using the significance level, the significance level is the probability that the overall parameters are estimated to fall within a certain interval and errors are possibly made, the smaller the value of the model is, the better the model is, the smaller the value of α is, and when d is 0,1,2 and 3, the value of the significance α of the model is respectively calculated, and the difference times d are determined.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911091522.6A CN111126656B (en) | 2019-11-10 | 2019-11-10 | Electric energy meter fault quantity prediction method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911091522.6A CN111126656B (en) | 2019-11-10 | 2019-11-10 | Electric energy meter fault quantity prediction method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111126656A true CN111126656A (en) | 2020-05-08 |
CN111126656B CN111126656B (en) | 2023-07-04 |
Family
ID=70495510
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201911091522.6A Active CN111126656B (en) | 2019-11-10 | 2019-11-10 | Electric energy meter fault quantity prediction method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111126656B (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111611519A (en) * | 2020-05-28 | 2020-09-01 | 上海观安信息技术股份有限公司 | Method and device for detecting personal abnormal behaviors |
CN112015169A (en) * | 2020-10-19 | 2020-12-01 | 金税信息技术服务股份有限公司 | Method, device and equipment for monitoring and maintaining equipment running state of intelligent equipment box |
CN114154667A (en) * | 2020-09-07 | 2022-03-08 | 思维实创(哈尔滨)科技有限公司 | Mixed time series elevator operation parameter prediction method based on big data |
CN118297366A (en) * | 2024-06-06 | 2024-07-05 | 湖北华中电力科技开发有限责任公司 | Early warning method and system based on artificial intelligence power supply quality |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0764965A (en) * | 1993-08-30 | 1995-03-10 | Hitachi Ltd | Method for predicting sales quantity |
CN105069535A (en) * | 2015-08-19 | 2015-11-18 | 中国电力科学研究院 | Method for predicting operational reliability of power distribution network based on ARIMA model |
CN105158598A (en) * | 2015-08-15 | 2015-12-16 | 国家电网公司 | Fault prediction method suitable for power equipment |
CN105426991A (en) * | 2015-11-06 | 2016-03-23 | 深圳供电局有限公司 | Method and system for predicting defect rate of transformer |
US20160379676A1 (en) * | 2015-06-25 | 2016-12-29 | HGST Netherlands B.V. | Hdd magnetic head degradation field-failure detection and prediction |
CN106845714A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | A kind of monthly passenger flow method of ARIMA model prediction urban track traffics based on seasonal index number |
US20180196900A1 (en) * | 2017-01-11 | 2018-07-12 | Teoco Ltd. | System and Method for Forecasting Values of a Time Series |
US20190050515A1 (en) * | 2018-06-27 | 2019-02-14 | Intel Corporation | Analog functional safety with anomaly detection |
-
2019
- 2019-11-10 CN CN201911091522.6A patent/CN111126656B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0764965A (en) * | 1993-08-30 | 1995-03-10 | Hitachi Ltd | Method for predicting sales quantity |
US20160379676A1 (en) * | 2015-06-25 | 2016-12-29 | HGST Netherlands B.V. | Hdd magnetic head degradation field-failure detection and prediction |
CN105158598A (en) * | 2015-08-15 | 2015-12-16 | 国家电网公司 | Fault prediction method suitable for power equipment |
CN105069535A (en) * | 2015-08-19 | 2015-11-18 | 中国电力科学研究院 | Method for predicting operational reliability of power distribution network based on ARIMA model |
CN105426991A (en) * | 2015-11-06 | 2016-03-23 | 深圳供电局有限公司 | Method and system for predicting defect rate of transformer |
US20180196900A1 (en) * | 2017-01-11 | 2018-07-12 | Teoco Ltd. | System and Method for Forecasting Values of a Time Series |
CN106845714A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | A kind of monthly passenger flow method of ARIMA model prediction urban track traffics based on seasonal index number |
US20190050515A1 (en) * | 2018-06-27 | 2019-02-14 | Intel Corporation | Analog functional safety with anomaly detection |
Non-Patent Citations (4)
Title |
---|
刘逸涵: "基于电能表需求预测的配送优化研究" * |
斯蒂文・C・惠尔赖特\N\N\N\N,邓俊刚: "时间数列预测的传统分解法" * |
杨志和;向哲;: "风机叶片结冰故障预测模型及其实现方法" * |
潘迪夫等: "基于时间序列分析和卡尔曼滤波算法的 风电场风速预测优化模型" * |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111611519A (en) * | 2020-05-28 | 2020-09-01 | 上海观安信息技术股份有限公司 | Method and device for detecting personal abnormal behaviors |
CN111611519B (en) * | 2020-05-28 | 2023-07-11 | 上海观安信息技术股份有限公司 | Method and device for detecting personal abnormal behaviors |
CN114154667A (en) * | 2020-09-07 | 2022-03-08 | 思维实创(哈尔滨)科技有限公司 | Mixed time series elevator operation parameter prediction method based on big data |
CN112015169A (en) * | 2020-10-19 | 2020-12-01 | 金税信息技术服务股份有限公司 | Method, device and equipment for monitoring and maintaining equipment running state of intelligent equipment box |
CN112015169B (en) * | 2020-10-19 | 2021-02-09 | 金税信息技术服务股份有限公司 | Method, device and equipment for monitoring and maintaining equipment running state of intelligent equipment box |
CN118297366A (en) * | 2024-06-06 | 2024-07-05 | 湖北华中电力科技开发有限责任公司 | Early warning method and system based on artificial intelligence power supply quality |
Also Published As
Publication number | Publication date |
---|---|
CN111126656B (en) | 2023-07-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111126656A (en) | Electric energy meter fault quantity prediction method | |
CN111210093B (en) | Daily water consumption prediction method based on big data | |
Koop et al. | Bayesian analysis of long memory and persistence using ARFIMA models | |
Taieb et al. | Regularization in hierarchical time series forecasting with application to electricity smart meter data | |
Fischer et al. | The impact of human capital on regional labor productivity in Europe | |
CN108399453A (en) | A kind of Electric Power Customer Credit Rank Appraisal method and apparatus | |
CN107958354A (en) | A kind of analysis method of power grid layer utilization rate of equipment and installations major influence factors | |
Weng et al. | Probabilistic baseline estimation based on load patterns for better residential customer rewards | |
CN112712203B (en) | Day-highest load prediction method and system for power distribution network | |
Grigoletto | Bootstrap prediction intervals for autoregressions: some alternatives | |
CN112116149B (en) | Multi-station medium and long term runoff rolling probability prediction method considering forecast uncertainty associated evolution characteristics | |
US10770898B2 (en) | Methods and systems for energy use normalization and forecasting | |
CN111680398B (en) | Single machine performance degradation prediction method based on Holt-windows model | |
CN103853939A (en) | Combined forecasting method for monthly load of power system based on social economic factor influence | |
CN105243449A (en) | Method and device for correcting prediction result of electricity selling amount | |
CN110110339B (en) | Japanese hydrologic forecast error correction method and system | |
Shan et al. | Seasonal warranty prediction based on recurrent event data | |
CN116307039A (en) | Intelligent prediction method for photovoltaic output considering gas aberration anisotropy | |
Heylen et al. | Probabilistic day-ahead inertia forecasting | |
CN115907176A (en) | Power transmission side carbon emission prediction method based on federal learning | |
CN103854073A (en) | Method for comprehensively predicting generation capacity of multi-radial flow type small hydropower station group area | |
CN108256676B (en) | Power load prediction method considering load fluctuation asymmetry characteristic | |
Lee et al. | Concept of seasonality analysis of hydrologic extreme variables and design rainfall estimation using nonstationary frequency analysis | |
CN112182864A (en) | Method for selecting clock error prediction based on drift condition of hydrogen atomic clock | |
CN108108860A (en) | A kind of four steps coupling MEDIUM OR LONG RANGE HYDROLOGIC FORECAST METHOD |
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 |