CN110070204A - A kind of customer charge trend forecasting method based on Moving Average - Google Patents
A kind of customer charge trend forecasting method based on Moving Average Download PDFInfo
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Abstract
The invention discloses a kind of customer charge trend forecasting method based on Moving Average, belongs to technical field of electric power.The presentation fragmentation of existing Power system load data, discretization, the degree of association is not high, can not accurately reflect electric load variation tendency, while being unfavorable for public intuitive displaying electric load variation tendency.The present invention is using the power load data of power customer as research object, using rolling average as core algorithm, by generating prediction model to historical load data constructing technology index, and is shown by graphics mode.The present invention integrates discrete Power system load data by Moving Average, available data can be integrated with depth by being described with the mode of formula function, electric power potentiality are excavated, the raising in efficiency is turned to by the simple accumulation on pure quantity for electric power development and establishes data supporting.
Description
Technical field
The present invention relates to a kind of customer charge trend forecasting method based on Moving Average, belongs to technical field of electric power.
Background technique
With the fast development in digital information epoch, the informatization of many years is passed through in electric system, has accumulated a large amount of
User acquire data.Along with being in full swing for New Generation of Intelligent electric system construction, the rapid development of smart grid makes
Information and Communication Technology is just with the production of unprecedented range, depth and power grid, business administration rapid fusion, and information communication system is
Become " nervous centralis " of smart grid, supports power grid production of new generation and management development.As can these is made full use of to be based on
The actual data of power grid, analyse in depth it, can provide a large amount of high added value service.These value-added services will have
Conducive to power grid security Detection & Controling (including calamity early warning and processing, power supply with power scheduling decision support and more accurately
Electricity demand forecasting), customer electricity behavioural analysis and customer segmentation, electric power enterprise lean operation management etc. are realized more scientific
Demand side management.Wherein, core business of the electric load concerning each power department such as production, scheduling, marketing.
The presentation fragmentation of existing Power system load data, discretization, the degree of association is not high, is unfavorable for government and power department
Prediction is carried out, electric load variation tendency can not be accurately reflected, while being unfavorable for becoming to public intuitive displaying electric load
Change trend.
Summary of the invention
In view of the drawbacks of the prior art, the purpose of the present invention is to provide one kind by Moving Average to discrete data into
Row integration, and then it is able to reflect the customer charge trend forecasting method based on Moving Average of electric load trend.
To achieve the above object, the technical solution of the present invention is as follows:
A kind of customer charge trend forecasting method based on Moving Average, which is characterized in that with the electricity consumption of power customer
Load data is research object, using rolling average as core algorithm, by generating to historical load data constructing technology index
Prediction model, and be shown by graphics mode；
It mainly comprises the steps that
The first step, according to the historical load data of record, calculated load index rolling average EMA index, the EMA index
Including fast moving average value, moving slowly at average value；
Second step is weighted calculated load according to fast moving average value and moving slowly at average value and same day EMA
Difference in value DIF index；
Third step passes through weighting summation according to the deviation average value DEA index of the calculated DIF index of upper step and proxima luce (prox. luc)
Obtain same day deviation average value DEA index；
4th step, according to load difference in value DIF index and deviation average value DEA index calculated load MACD index；
5th step constructs the MACD technical indicator based on load factor rolling average data comprising two lines, one column,
In: Express Order Wire is that load difference in value DIF changes function, and slow line is that deviation average value DEA changes function, and histogram is MACD change
Change function.
The present invention integrates discrete Power system load data by Moving Average, constructs and is moved based on load factor
The MACD technical indicator of dynamic average data, is effectively predicted electric load trend according to the function model of foundation；And then this
Invention can be intuitive, succinct, clearly reflect custom power load variations trend by way of number, curve, figure.With public affairs
The mode of formula function is described can integrate available data with depth, excavate electric power potentiality, be electric power development by pure quantity
The raising that turns in efficiency of simple accumulation establish data supporting.
This not only improves the science of government's policies, decision rule, perspective, or power department and phase
Customer analysis load trend is closed, business strategy is formulated, mutual beneficial co-operation is sought to provide reference.
It is used as a kind of prediction model simultaneously, data discrete before are combined, the comprehensive electric load that embodies becomes
Change trend, lateral comparison and quantitatively evaluating between ground section, industry provide data supporting, are also areal, same industry
It carries out vertical analysis and trend prediction provides data reference.
As optimization technique measure,
Construct load factor rolling average EMA index
Load factor rolling average index is to calculate n days exponential smoothing indexs, the index value is not by the way that weight α is arranged
But by the data influence of history, the date is closer, influences bigger；Date more then influences forward smaller；Weight is set as α, then load
Index rolling average EMA index calculates as follows:
EMA* (1- α)+same day EMA* α of EMA=proxima luce (prox. luc).
As optimization technique measure,
Construct load difference in value DIF index
EMA index is divided into according to the difference of smooth number of days and fast moves average value and move slowly at average value；It is typically chosen
EMA (12) is used as average value, EMA (26) is fast moved and moves slowly at average value；Corresponding weight α is traditionally arranged to be 2/13
With 2/27；Using the difference of the two numerical value as " difference in value " DIF, i.e. EMA numerical value on the 12nd subtracts EMA numerical value on the 26th.Stock point
It analyses in technical application to load forecast, breaks prior art prejudice, be skillfully constructed, scheme is detailed, practical.
Therefore the calculating of DIF value is as follows:
DIF=EMA (12)-EMA (26).
As optimization technique measure,
Construct deviation average value DEA index
For required DIF value, its moving average on the 9th is further calculated, DEA is arrived similar to algorithm using EMA
Value；
As optimization technique measure,
Load MACD index calculation formula is as follows:
MACD=(DIF-DEA) * 2
So far, MACD technical indicator, Express Order Wire DIF and slow line based on load factor rolling average data are constructed
DEA changes function, judges whether the following some cycles internal loading is on the rise.
Compared with prior art, the invention has the following advantages:
The present invention integrates discrete Power system load data by Moving Average, constructs and is moved based on load factor
The MACD technical indicator of dynamic average data, is effectively predicted electric load trend according to the function model of foundation；In turn
The present invention can be intuitive, succinct, clearly reflect custom power load variations by way of number, curve, figure
Trend.Available data can be integrated with depth by being described with the mode of formula function, excavate electric power potentiality, be electric power development by list
Data supporting is established in the raising that pure quantitative simple accumulation turns in efficiency.
This not only improves the science of government's policies, decision rule, perspective, or power department and phase
Customer analysis load trend is closed, business strategy is formulated, mutual beneficial co-operation is sought to provide reference.
It is used as a kind of prediction model simultaneously, data discrete before are combined, the comprehensive electric load that embodies becomes
Change trend, lateral comparison and quantitatively evaluating between ground section, industry provide data supporting, are also areal, same industry
It carries out vertical analysis and trend prediction provides data reference.
Detailed description of the invention
Fig. 1 is inventive algorithm flow chart；
Fig. 2 is for practical power load and based on a kind of implementation diagrammatic illustration of customer charge trend prediction of the invention；
Fig. 3 is practical power load and implements diagrammatic illustration based on customer charge trend prediction another kind of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
On the contrary, the present invention covers any substitution done on the essence and scope of the present invention being defined by the claims, repairs
Change, equivalent method and scheme.Further, in order to make the public have a better understanding the present invention, below to of the invention thin
It is detailed to describe some specific detail sections in section description.Part without these details for a person skilled in the art
The present invention can also be understood completely in description.
As shown in Figure 1, a kind of customer charge trend forecasting method based on Moving Average, uses based on graph topology
Shortest path calculates the terminal temperature difference selection algorithm for carrying out multilayer screening: being research pair with the power load data of power customer
As by generating prediction model to historical load data constructing technology index, and passing through using rolling average as core algorithm
Graphics mode is shown.
It mainly comprises the steps that
The first step, according to the historical load data of record, calculated load index rolling average EMA index, the EMA index
Including fast moving average value, moving slowly at average value；
Second step is weighted calculated load according to fast moving average value and moving slowly at average value and same day EMA
Difference in value DIF index；
Third step passes through weighting summation according to the deviation average value DEA index of the calculated DIF index of upper step and proxima luce (prox. luc)
Obtain same day deviation average value DEA index；
4th step, according to load difference in value DIF index and deviation average value DEA index calculated load MACD index；
5th step constructs the MACD technical indicator based on load factor rolling average data comprising two lines, one column,
In: Express Order Wire is that load difference in value DIF changes function, and slow line is that deviation average value DEA changes function, and histogram is MACD change
Change function.
The present invention integrates discrete Power system load data by Moving Average, constructs and is moved based on load factor
The MACD technical indicator of dynamic average data, is effectively predicted electric load trend according to the function model of foundation；In turn
The present invention can be intuitive, succinct, clearly reflect custom power load variations by way of number, curve, figure
Trend.Available data can be integrated with depth by being described with the mode of formula function, excavate electric power potentiality, be electric power development by list
Data supporting is established in the raising that pure quantitative simple accumulation turns in efficiency.
This not only improves the science of government's policies, decision rule, perspective, or power department and phase
Customer analysis load trend is closed, business strategy is formulated, mutual beneficial co-operation is sought to provide reference.
It is used as a kind of prediction model simultaneously, data discrete before are combined, the comprehensive electric load that embodies becomes
Change trend, lateral comparison and quantitatively evaluating between ground section, industry provide data supporting, are also areal, same industry
It carries out vertical analysis and trend prediction provides data reference.
Construct load factor rolling average EMA index
Load factor rolling average index is to calculate n days exponential smoothing indexs, the index value is not by the way that weight α is arranged
But by the data influence of history, the date is closer, influences bigger；Date more then influences forward smaller；Weight is set as α, then load
Index rolling average EMA index calculates as follows:
EMA* (1- α)+same day EMA* α of EMA=proxima luce (prox. luc).
From the daystart of measurement period, backward daily EMA index calculating is as follows:
Construct load difference in value DIF index
EMA index is divided into according to the difference of smooth number of days and fast moves average value and move slowly at average value；It is typically chosen
EMA (12) is used as average value, EMA (26) is fast moved and moves slowly at average value；Corresponding weight α is traditionally arranged to be 2/13
With 2/27；Using the difference of the two numerical value as " difference in value " DIF, i.e. EMA numerical value on the 12nd subtracts EMA numerical value on the 26th.Stock point
It analyses in technical application to load forecast, breaks prior art prejudice, be skillfully constructed, scheme is detailed, practical.
Therefore the calculating of DIF value is as follows:
DIF=EMA (12)-EMA (26).
Construct deviation average value DEA index
For required DIF value, its moving average on the 9th is further calculated, DEA is arrived similar to algorithm using EMA
Value；
Load MACD index calculation formula is as follows:
MACD=(DIF-DEA) * 2
So far, MACD technical indicator, Express Order Wire DIF and slow line based on load factor rolling average data are constructed
DEA changes function, judges whether the following some cycles internal loading is on the rise.
The algorithm that the prediction of user power utilization load trend is carried out based on Moving Average, is referred to by the load data of history
Mark calculates, and index is depicted as figure, may be implemented by symbiosis at 3 slow lines of key index DEA, DIF cable release and MACD value
Prediction to load trend.Such as illustrating for following two user.
As shown in Fig. 2, the specific embodiment of the invention 1: the power load in certain user's A12 month and based on Moving Average
Customer charge trend prediction.
In the figure, the curve of top is true load, and lower section line is DIF cable release, the slow line of DEA, and bar chart refers to for MACD
Mark.When DIF cable release line slow higher than DEA, illustrate that load is integrally in rising trend；When DIF cable release line slow lower than DEA, explanation
Load is integrally on a declining curve.As can be seen from Figure, the rule and the variation tendency of load are presented consistent, illustrate to pass through the party
The prediction to user power utilization load trend may be implemented in method.
As shown in figure 3, the specific embodiment of the invention 2: the power load in certain user's B12 month and based on Moving Average
Customer charge trend prediction.
In the figure, the curve of top is true load, and lower section line is DIF cable release, the slow line of DEA, and bar chart refers to for MACD
Mark.When DIF cable release line slow higher than DEA, illustrate that load is integrally in rising trend；When DIF cable release line slow lower than DEA, explanation
Load is integrally on a declining curve.The rule and the variation tendency of load are presented consistent it can be seen from Fig. 2, are illustrated by being somebody's turn to do
The prediction to user power utilization load trend may be implemented in method.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (5)
1. a kind of customer charge trend forecasting method based on Moving Average, which is characterized in that negative with the electricity consumption of power customer
Lotus data are research object, using rolling average as core algorithm, by generating pre- to historical load data constructing technology index
Model is surveyed, and is shown by graphics mode；
It mainly comprises the steps that
The first step, according to the historical load data of record, calculated load index rolling average EMA index, the EMA index includes
It fast moves average value, move slowly at average value；
Second step average value and moves slowly at average value and same day EMA according to fast moving, be weighted calculated load difference from
Value DIF index；
Third step is obtained according to the deviation average value DEA index of the calculated DIF index of upper step and proxima luce (prox. luc) by weighting summation
Same day deviation average value DEA index；
4th step, according to load difference in value DIF index and deviation average value DEA index calculated load MACD index；
5th step constructs the MACD technical indicator based on load factor rolling average data comprising two lines, one column, in which: fast
Fast line is that load difference in value DIF changes function, and slow line is that deviation average value DEA changes function, and histogram is that MACD changes letter
Number.
2. a kind of customer charge trend forecasting method based on Moving Average as described in claim 1, which is characterized in that structure
Build load factor rolling average EMA index
Load factor rolling average index is to calculate n days exponential smoothing indexs by the way that weight α is arranged, the index value not only by
The data influence of history, the date is closer, influences bigger；Date more then influences forward smaller；Weight is set as α, then load factor
Rolling average EMA index calculates as follows:
EMA* (1- α)+same day EMA* α of EMA=proxima luce (prox. luc).
3. a kind of customer charge trend forecasting method based on Moving Average as claimed in claim 2, which is characterized in that structure
Build load difference in value DIF index
EMA index is divided into according to the difference of smooth number of days and fast moves average value and move slowly at average value；It is typically chosen EMA
(12) as average value is fast moved, EMA (26) is used as and moves slowly at average value；Corresponding weight α is traditionally arranged to be 2/13 He
2/27；Using the difference of the two numerical value as " difference in value " DIF, i.e. EMA numerical value on the 12nd subtracts EMA numerical value on the 26th；
Therefore the calculating of DIF value is as follows:
DIF=EMA (12)-EMA (26).
4. a kind of customer charge trend forecasting method based on Moving Average as claimed in claim 3, which is characterized in that structure
Build deviation average value DEA index
For required DIF value, its moving average on the 9th is further calculated, DEA value is arrived similar to algorithm using EMA；
5. a kind of customer charge trend forecasting method based on Moving Average as claimed in claim 4, which is characterized in that negative
Lotus MACD index calculation formula is as follows:
MACD=(DIF-DEA) * 2
So far, MACD technical indicator, Express Order Wire DIF and slow line DEA of the building based on load factor rolling average data become
Change function, judges whether the following some cycles internal loading is on the rise.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111144624A (en) * | 2019-12-10 | 2020-05-12 | 昆明电力交易中心有限责任公司 | Optimization method for power quota allocation of renewable energy |
| CN111369073A (en) * | 2020-03-19 | 2020-07-03 | 华泰证券股份有限公司 | Laplace operator-based stock index trend prediction method |
| CN112258013A (en) * | 2020-10-17 | 2021-01-22 | 中国石油化工股份有限公司 | Heat exchanger key easy-scaling group division and energy efficiency state monitoring method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086930A (en) * | 2018-07-27 | 2018-12-25 | 广东电网有限责任公司 | A kind of user power utilization behavior analysis method based on electric power K line chart and depth network |
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- 2019-03-05 CN CN201910165804.XA patent/CN110070204A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086930A (en) * | 2018-07-27 | 2018-12-25 | 广东电网有限责任公司 | A kind of user power utilization behavior analysis method based on electric power K line chart and depth network |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111144624A (en) * | 2019-12-10 | 2020-05-12 | 昆明电力交易中心有限责任公司 | Optimization method for power quota allocation of renewable energy |
| CN111144624B (en) * | 2019-12-10 | 2023-05-02 | 昆明电力交易中心有限责任公司 | Optimization method for renewable energy power quota allocation |
| CN111369073A (en) * | 2020-03-19 | 2020-07-03 | 华泰证券股份有限公司 | Laplace operator-based stock index trend prediction method |
| CN112258013A (en) * | 2020-10-17 | 2021-01-22 | 中国石油化工股份有限公司 | Heat exchanger key easy-scaling group division and energy efficiency state monitoring method |
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