CN105844342A - Short-term electricity market forecasting system and method - Google Patents
Short-term electricity market forecasting system and method Download PDFInfo
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Abstract
The invention discloses a short-term electricity market forecasting system and method. The method comprises the steps of through an electric power control center power grid energy management system, acquiring historical load data of monthly electricity consumption in a power supply business region and storing the historical load data; processing the stored historical load data to make a monthly distribution map of electricity consumption over the years; according to the monthly distribution map of electricity consumption over the years, calculating an electricity consumption season factor f(M), and then determining an annual monthly electricity sale forecasting value calculation formula based on the electricity sale variation value caused by reasons other than seasonal variation; and according to the annual monthly electricity sale forecasting value calculation formula, carrying out a short-term forecast of and displaying the monthly power consumption. The invention can effectively avoid the subjective influence on the forecast accuracy and reduce the forecast risk by using a season factor forecasting method whereby the monthly electricity sale this year can be forecasted based on objective data of electricity consumption over the past years.
Description
Technical field
The present invention relates to Electrical Market Forecasting, be specifically related to a kind of short term electric power market prognoses system and method.
Background technology
During Operation of Electric Systems, many power departments are all played an important role by Electrical Market Forecasting problem.
It relates to the many aspects such as the operation of power system economic security, power market transaction.Along with power industry development, gradually
Coming into the market, market prediction plays more and more important role in power industry, and have become as the marketing and
The core business of transaction department, this also degree of accuracy and stability to market prediction have higher requirement.
At present, in business district, monthly power consumption is big with history power consumption and electricity consumption rate of increase dependency.Predictor formula is as follows:
A (M)=B (M) * (1+a)
Wherein: A (M) is power consumption in anticipated M in current year month business district；
B (M) is moon power consumption in same period prior year of business district；
A is power consumption year-on-year growth rate in business district in the anticipated current year.
Under normal circumstances, in business district, power consumption year-on-year growth rate is proportional with this area economic growth rate.Therefore
Market prediction data can be calculated by above-mentioned formula.
But, when there is the factors such as Changes in weather, the larger power user production schedule, user's industry expansion engineering, above-mentioned prediction
Method is not accurate enough, it is impossible to meet the existing requirement to short term electric power market precision of prediction.
Summary of the invention
For solving the deficiency that prior art exists, the invention discloses a kind of short term electric power market prognoses system and method,
Power consumption seasonal factor predicted method of the present invention, it is determined that electricity sales amount predictor formula, seasonal factor predicted method is according to sale of electricity in former years
The objective data of amount predicts the monthly electricity sales amount in the current year, can effectively evade the subjective impact of predictablity rate, reduces pre-
Survey risk.
For achieving the above object, the concrete scheme of the present invention is as follows:
A kind of short term electric power market Forecasting Methodology, comprises the following steps:
By power regulation Central Grid EMS obtains going through of monthly power consumption in this business district of power supply
These historical load data are also stored by history load data；
The historical load data of storage are processed, makes power consumption over the years monthly scattergram；
According to power consumption over the years monthly scattergram, be calculated power consumption seasonal factor f (M), further according to seasonal variations outside
The electricity sales amount changing value that causes of other reasons determine year monthly power demand predictor calculation formula；
According to this monthly power demand predictor calculation formula in year, power consumption monthly is carried out short-term forecast and opens up
Show.
Further, power consumption seasonal factor, when calculating, utilizes electricity sales amount in former years to divide moon data, calculates prediction respectively
Year monthly electricity sales amount X and the electricity sales amount overall average in all months, with forecast year monthly electricity sales amount X divided by all months
Electricity sales amount overall average calculates seasonal factor f (M) monthly.
Further, seasonal factor f (M) monthly is multiplied by forecast year monthly electricity sales amount X and obtains being affected by seasonal variations
Annual monthly power demand predictive value.
Further, the sale of electricity quantitative change that the other reasons outside year seasonal variations included by monthly power demand predictive value causes
Change value is specially the electricity sales amount changing value obtained by client's maintenance and need for electricity change.
Further, year monthly power demand predictive value A (M) computing formula is as follows:
Wherein: f (M) is seasonal factor；
X is the monthly electricity sales amount of forecast year；
XiThe electricity sales amount changing value that other reasons outside for seasonal variations causes, such as, when there is client's maintenance and electricity consumption
When demand changes two kinds of situations, n=2, the electricity sales amount changing value X obtained for client's inspection and repair shop1And need for electricity changes caused
Electricity sales amount changing value X2。
A kind of short term electric power market prognoses system, including:
Power regulation Central Grid EMS, for gathering the monthly electricity consumption in business district of power supply to be predicted
The historical load data of amount, and these historical load data are transmitted through the network in the data base of power prediction server；
Power prediction server carries out data process according to the data obtained, and specially utilizes data processing unit to make and goes through
Year power consumption monthly scattergram；
Power consumption seasonal factor computing module, for according to power consumption over the years monthly scattergram calculate power consumption season because of
Son；
Annual monthly power demand predictor calculation module, for former according to other outside power consumption seasonal factor and seasonal variations
Electricity sales amount changing value because causing determines year monthly power demand predictor calculation formula；
Display module, for the annual monthly power demand predictive value that will calculate according to year monthly power demand predictor calculation formula
It is shown.
Further, power consumption seasonal factor computing module, specifically at power consumption seasonal factor when calculating, utilize former years
Electricity sales amount divides moon data, calculates the electricity sales amount overall average in forecast year monthly electricity sales amount X and all months respectively, with prediction
Year, monthly electricity sales amount X calculated seasonal factor f (M) monthly divided by the electricity sales amount overall average in all months.
Further, year monthly power demand predictor calculation module includes that the annual monthly power demand affected by seasonal variations is pre-
Measured value, is specially and is multiplied by, with seasonal factor f (M) monthly, the year that forecast year monthly electricity sales amount X obtains being affected by seasonal variations
Degree monthly power demand predictive value.
Further, in annual monthly power demand predictor calculation module, season included by monthly power demand predictive value in the year
The electricity sales amount changing value that other reasons outside change causes is specially the electricity sales amount obtained by client's maintenance and need for electricity change
Changing value.
Beneficial effects of the present invention:
The present invention utilizes seasonal factor predicted method, is that objective data prediction the monthly of the current year according to electricity sales amount in former years is sold
Electricity, can effectively evade the subjective impact of predictablity rate, reduces forecasting risk.
It addition, the electricity sales amount changing value that the other reasons that invention also contemplates that outside seasonal variations causes, thus avoid
When there is Changes in weather, the larger power user production schedule, user industry be when expanding the factors such as engineering, and above-mentioned Forecasting Methodology is the most accurate
Really, it is impossible to the problem meeting the existing requirement to short term electric power market precision of prediction.
Accompanying drawing explanation
Fig. 1 2011-2014 electricity sales amount over the years monthly scattergram；
The electricity sales amount prediction flow chart of Fig. 2 present invention；
Fig. 3 present invention and traditional method predictablity rate comparison diagram.
Detailed description of the invention:
The present invention is described in detail below in conjunction with the accompanying drawings:
By power regulation Central Grid EMS obtains going through of monthly power consumption in this business district of power supply
These historical load data are also stored by history load data；The historical load data of storage are processed, draws and sell over the years
Electricity monthly scattergram, as it is shown in figure 1, after systematic analysis monthly electricity sales amount in former years, find monthly electricity sales amount and deposit between season
At certain Changing Pattern, create power consumption seasonal factor predicted method according to this rule, it is determined that electricity sales amount predictor formula,
And the computational methods of seasonal factor are described in detail.Seasonal factor predicted method is that the objective data according to electricity sales amount in former years predicts this
The monthly electricity sales amount in year, can effectively evade the subjective impact of predictablity rate, reduces forecasting risk.
Electricity sales amount prediction flow chart is as in figure 2 it is shown, include: the collection monthly power demand data several years ago of institute as far as possible, counts
Calculate the electricity sales amount overall average in electricity sales amount meansigma methods monthly and all months, calculate divided by overall average by monthly average value
Obtaining seasonal factor monthly, seasonal factor is multiplied by the monthly average value of annual prediction electricity sales amount and is by selling that seasonal variations is affected
SOC values, investigates this moon local with or without unit maintenance situation, determines that electricity sales amount predicts final numerical value,
Utilize electricity sales amount over the years to divide moon data, there is certain rule according to its change with season, establish electricity sales amount
Seasonal factor predicted method.Determine electricity sales amount predictor formula, it was predicted that formula is as follows:
Wherein: f (M) is seasonal factor；
X is the monthly electricity sales amount of forecast year；
XiThe electricity sales amount changing value that other reasons outside for seasonal variations causes.
Electricity sales amount changing factor is summarized as two parts by predictor formula.Part I is the electricity sales amount caused by seasonal variations
Change, is affected by seasonal factor and annual monthly electricity sales amount；Part II is caused by client's maintenance, need for electricity change etc.
Change, when not having repair schedule, this component values is defaulted as 0.
Part I computational methods: utilize electricity sales amount in former years to divide moon data, calculate electricity sales amount meansigma methods monthly respectively
And the electricity sales amount overall average in all months, can be calculated seasonal factor monthly by monthly average value divided by overall average.Season
The factor is multiplied by the monthly average value of annual prediction electricity sales amount and is the electricity sales amount numerical value affected by seasonal variations.Table 1 is for utilizing 2011
The fraction of the year of year-2014, the moon, power consumption calculated detailed numerical value and the process of seasonal factor.
Table 1 seasonal factor calculates process and detailed value
The method is verified in company's short-term market prediction work, by contrast, utilizes the electricity sales amount that the method is predicted
Accuracy rate is in higher level.The method contrasts as shown in Figure 3 with traditional method predictablity rate.
Although the detailed description of the invention of the present invention is described by the above-mentioned accompanying drawing that combines, but not the present invention is protected model
The restriction enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme, and those skilled in the art are not
Need to pay various amendments or deformation that creative work can make still within protection scope of the present invention.
Claims (8)
1. a short term electric power market Forecasting Methodology, is characterized in that, comprises the following steps:
Born by the history of monthly power consumption in this business district of power supply of acquisition in power regulation Central Grid EMS
These historical load data are also stored by lotus data；
The historical load data of storage are processed, makes power consumption over the years monthly scattergram；
According to power consumption over the years monthly scattergram, be calculated power consumption seasonal factor f (M), further according to seasonal variations outside its
The electricity sales amount changing value that his reason causes determines year monthly power demand predictor calculation formula；
According to this monthly power demand predictor calculation formula in year, power consumption monthly is carried out short-term forecast and is shown；
Annual monthly power demand predictive value A (M) computing formula is as follows:
Wherein: f (M) is seasonal factor；X is the monthly electricity sales amount of forecast year；XiWhat the other reasons outside for seasonal variations caused sells
Electric quantity change value, n is positive integer.
2. a kind of short term electric power market as claimed in claim 1 Forecasting Methodology, is characterized in that, power consumption seasonal factor is calculating
Time, utilize electricity sales amount in former years to divide moon data, calculate forecast year monthly electricity sales amount X respectively and the electricity sales amount in all months is always put down
Average, calculates seasonal factor f monthly with forecast year monthly electricity sales amount X divided by the electricity sales amount overall average in all months
(M)。
3. a kind of short term electric power market as claimed in claim 1 Forecasting Methodology, is characterized in that, seasonal factor f (M) monthly takes advantage of
The annual monthly power demand predictive value affected by seasonal variations is obtained with forecast year monthly electricity sales amount X.
4. a kind of short term electric power market as claimed in claim 1 Forecasting Methodology, is characterized in that, annual monthly power demand predictive value institute
Including seasonal variations outside the electricity sales amount changing value that causes of other reasons be specially client's maintenance and need for electricity changes gained
The electricity sales amount changing value arrived.
5. a short term electric power market prognoses system, is characterized in that, including:
Power regulation Central Grid EMS, for gathering monthly power consumption in business district of power supply to be predicted
Historical load data, and these historical load data are transmitted through the network in the data base of power prediction server；
Power prediction server carries out data process according to the data obtained, and specially utilizes data processing unit to make use over the years
Electricity monthly scattergram；
Power consumption seasonal factor computing module, for calculating power consumption seasonal factor according to power consumption over the years monthly scattergram；
Annual monthly power demand predictor calculation module, for drawing according to the other reasons outside power consumption seasonal factor and seasonal variations
The electricity sales amount changing value risen determines year monthly power demand predictor calculation formula；
Display module, for carrying out the annual monthly power demand predictive value calculated according to year monthly power demand predictor calculation formula
Show.
6. a kind of short term electric power market as claimed in claim 5 prognoses system, is characterized in that, power consumption seasonal factor calculates mould
Block, specifically when the calculating of power consumption seasonal factor, utilizes electricity sales amount in former years to divide moon data, calculates forecast year respectively monthly
The electricity sales amount overall average in electricity sales amount X and all months, total divided by the electricity sales amount in all months with forecast year monthly electricity sales amount X
Mean value calculation obtains seasonal factor f (M) monthly.
7. a kind of short term electric power market as claimed in claim 5 prognoses system, is characterized in that, annual monthly power demand predictive value meter
Calculate the annual monthly power demand predictive value that module includes being affected by seasonal variations, be specially and be multiplied by pre-with seasonal factor f (M) monthly
Survey the annual monthly power demand predictive value that annual monthly electricity sales amount X obtains being affected by seasonal variations.
8. a kind of short term electric power market as claimed in claim 5 prognoses system, is characterized in that, annual monthly power demand predictive value meter
Calculating in module, the electricity sales amount changing value that the other reasons outside annual seasonal variations included by monthly power demand predictive value causes is concrete
Electricity sales amount changing value obtained by changing for client's maintenance and need for electricity.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111091232A (en) * | 2019-11-25 | 2020-05-01 | 黑龙江电力调度实业有限公司 | Power load prediction method considering power demand change trend |
| CN114169878A (en) * | 2021-10-18 | 2022-03-11 | 中标慧安信息技术股份有限公司 | Prepayment management method and system based on edge calculation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103530701A (en) * | 2013-10-12 | 2014-01-22 | 国家电网公司 | User month electricity consumption predicating method and system based on seasonal index method |
| CN103606022A (en) * | 2013-12-05 | 2014-02-26 | 国家电网公司 | Short-term load prediction method |
-
2016
- 2016-03-15 CN CN201610147574.0A patent/CN105844342A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103530701A (en) * | 2013-10-12 | 2014-01-22 | 国家电网公司 | User month electricity consumption predicating method and system based on seasonal index method |
| CN103606022A (en) * | 2013-12-05 | 2014-02-26 | 国家电网公司 | Short-term load prediction method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111091232A (en) * | 2019-11-25 | 2020-05-01 | 黑龙江电力调度实业有限公司 | Power load prediction method considering power demand change trend |
| CN111091232B (en) * | 2019-11-25 | 2023-02-03 | 黑龙江电力调度实业有限公司 | Power load prediction method considering power demand change trend |
| CN114169878A (en) * | 2021-10-18 | 2022-03-11 | 中标慧安信息技术股份有限公司 | Prepayment management method and system based on edge calculation |
| CN114169878B (en) * | 2021-10-18 | 2022-09-20 | 中标慧安信息技术股份有限公司 | Prepayment management method and system based on edge calculation |
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Application publication date: 20160810 |