CN107748938A - A kind of electric power demand forecasting method based on Vector Autoression Models - Google Patents
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Abstract
The present invention relates to a kind of electric power demand forecasting method based on Vector Autoression Models.Step 1: collect mesh area to be predicted（Country, province or city）Economic data and electricity needs data；Step 2: and data are carried out with arrangement cleaning, be stored in database；Step 3: definition needs the minimum interval for the time cycle and data predicted, and to corresponding data, carry out logarithmetics processing；Step 4: for 4 column datas after processing（Investment in fixed assets, the total retail sales of consumer goods, net export total volume of trade, electricity needs）Carry out stationary test；Step 5: structure is based on Vector Autoression Models equation；Step 6: the lag order of model is determined using AIC akaike information criterions and SIC Schwarzs criterion；Step 7: correct and use based on Vector Autoression Models to be predicted to electricity needs.The present invention has the characteristics that stronger generalization ability, precision of prediction are higher.
Description
Technical field
The present invention relates to a kind of electric power demand forecasting method based on Vector Autoression Models, particular by Econometric
Vector Autoression Models in carry out electric power demand forecasting.
Background technology
Tradition is growing with power system, and electric power has turned into most important secondary in industrial production and resident living
The energy.The characteristics of electric energy compared with other commodity, has certain particularity as commodity, and its is maximum is to store, i.e., electric
Production, transport, distribution and the consumption of energy are completed simultaneously.Therefore, the available generating capacity in power system, in normal condition
Should meet the needs of system internal loading down.When generating capacity should not enough take necessary measure, such as increase generating set, reduction are negative
Lotus or from other power network input powers；On the contrary, working as generating capacity excess, then need to take reduction generating set defeated to other power networks
Send surplus power.In order to reduce the difference between generating capacity and electricity needs as far as possible, reduce extra operation (cut machine, cut it is negative
Lotus etc.), electric power enterprise operation cost is reduced, effective method should be taken to be predicted electricity needs.
At present, according to the time length of prediction, electric power demand forecasting can be divided into short-term forecast, medium-term forecast and long-term pre-
Survey, its corresponding time span is respectively a few minutes to one week, monthly or season, more than 1 year.China started to draw in 1992
Enter demand Side Management (Demand Side Management DSM) concept, and then start grinding for electric power demand forecasting
Study carefully and explore.The method for the electric power demand forecasting for being seen in document at present is added, and is broadly divided into classical Forecasting Methodology and modern prediction
Method.Classical forecast reason often only focuses on qualitative analysis and prediction, can not meet the needs of existing electric power enterprise.It is modern
Electric power demand forecasting turns into current main flow Forecasting Methodology, and it mainly includes following characteristics：Measure quantifies, in face of data analysis, fixed
Amount with it is qualitative be combined, using nonlinear prediction and to intelligent Forecasting technology development.Therefore, using a kind of the pre- of precise and high efficiency
Survey means are predicted to the electricity needs in different time scales, plan and instruct electricity power enterprise, power grid enterprises to carry out efficient
Production and scheduling electric energy have important economic implications and construction value.
At present, more the Forecasting Methodology of main flow mainly includes bearing using least square method or the medium-term and long-term of PLS
Lotus is predicted, using artificial neural network (Artificial Neural NetworkANN), ARMA model
(Autoregression Moving Average ARMA), SVMs (Support Vector Machine SVM) etc.
Method carries out applying.These methods construct corresponding anticipation function often by the historical data of electricity consumption,
Seldom it is described and quantifies for other index such as related economic indexs that may influence electricity needs in region, simultaneously for
The consideration of the correlation analysis of economy and electric power is not comprehensive enough, causes forecast model accuracy to be short of.
The content of the invention
It is an object of the invention to provide a kind of electric power demand forecasting method based on Vector Autoression Models, this method tool
There is the features such as stronger generalization ability, precision of prediction is higher.
To achieve the above object, the technical scheme is that：A kind of electricity needs based on Vector Autoression Models is pre-
Survey method, by collecting regional economic data to be predicted and electric power data, using Vector Autoression Models, carry out electricity needs
Prediction.
In an embodiment of the present invention, the economic data includes 3 economic indicators, respectively investment in fixed assets, society
Can the total volume of retail sales of consumer goods, net export total volume of trade；The electric power data includes Analyzing Total Electricity Consumption.
In an embodiment of the present invention, the economic data and electric power data need to carry out arrangement cleaning, and carry out logarithm
Change is handled.
In an embodiment of the present invention, after the economic data and electric power data carry out logarithmetics processing, need to carry out steady
Property verification, i.e., examined using dickey-fuller, examine each row sequence data to whether there is unit root, if in the presence of to this
Sequence data carries out difference processing, then carries out dickey-fuller inspections to difference sequence, if there are still enter one if unit root
Difference processing is walked, repeats above step, untill unit root is not present in sequence.
In an embodiment of the present invention, the Vector Autoression Models are：
Xt=α+A1Xt-1+…+AkXt-k+εt
Wherein, εtRepresent zero-mean, the random perturbation of finite variance, XtRepresent m × 1 of m endogenous variable composition of t phases
Vector, Xt-kIt is vectorial to represent m × 1 of the m endogenous variable composition of hysteresis k phases, AkM × m coefficient matrixes are represented, it is normal that α represents m × 1
Number vector；It is as follows that above formula is write as matrix form：
In an embodiment of the present invention, the variable in the Vector Autoression Models is the economic number verified by stationarity
According to and electric power data.
In an embodiment of the present invention, the Vector Autoression Models need to be accurate by red pond information criterion and Schwarz information
The lag order of variable in model then is determined, its calculation formula is as follows：
Wherein, M is Maximum-likelihood estimation, and R is sample size, and L is lag order；The 1 AIC and SIC values for arriving k ranks are calculated,
When both get minimum value in identical exponent number, the L values are exactly the lag order needed；If AIC and SIC values take in different rank
When obtaining minimum value, lag order is determined referring again to likelihood ratio test method LR.
Compared to prior art, the invention has the advantages that：
The present invention combines the economic data of statistics bureau's announcement and the electricity consumption data of power system, passage time sequence
Correlation analysis and causality analysis, the electricity consumption influence factor of objective area (country, saving, city) is analyzed.Really
It is fixed that maximum economic indicator is influenceed on electricity consumption, and corresponding data is exported and forms endogenous variable；Meanwhile difference can be selected
Data time interval predict the electricity consumption under different cycles；Based on analysis result, establish objective area and (country, save, city
City) economic data and electricity needs Vector Autoression Models come predict future electrical energy consume；The present invention has stronger general
Change ability, it can be predicted and verify that empirical result is good for the electricity consumption based on various different economic indicators, different regions
Good, precision of prediction is high.
Brief description of the drawings
Fig. 1 is the electric power demand forecasting method schematic diagram of the invention based on Vector Autoression Models.
Embodiment
Below in conjunction with the accompanying drawings, technical scheme is specifically described.
A kind of electric power demand forecasting method based on Vector Autoression Models of the present invention, it is to be predicted regional by collecting
Economic data and electric power data, using Vector Autoression Models, carry out electric power demand forecasting.The economic data includes 3 warps
Ji index, respectively investment in fixed assets, the total retail sales of consumer goods, net export total volume of trade；The electric power data includes
Analyzing Total Electricity Consumption.
The economic data and electric power data need to carry out arrangement cleaning, and carry out logarithmetics processing.The economic data
After carrying out logarithmetics processing with electric power data, stationarity verification need to be carried out, i.e., is examined using dickey-fuller, examined each
Row sequence data whether there is unit root, if in the presence of, difference processing is carried out to the sequence data, then to difference sequence progress
Dickey-fuller is examined, and if there are still further difference processing if unit root, above step is repeated, until sequence is not deposited
Untill unit root.
The Vector Autoression Models are：
Xt=α+A1Xt-1+…+AkXt-k+εt
Wherein, εtRepresent zero-mean, the random perturbation of finite variance, XtRepresent m × 1 of m endogenous variable composition of t phases
Vector, Xt-kIt is vectorial to represent m × 1 of the m endogenous variable composition of hysteresis k phases, AkM × m coefficient matrixes are represented, it is normal that α represents m × 1
Number vector；It is as follows that above formula is write as matrix form：
Variable in the Vector Autoression Models is the economic data and electric power data by stationarity verification.It is described to
Amount autoregression model need to determine the lag order of variable in model by red pond information criterion and Schwarz information criterion, and it is counted
It is as follows to calculate formula：
Wherein, M is Maximum-likelihood estimation, and R is sample size, and L is lag order；The 1 AIC and SIC values for arriving k ranks are calculated,
When both get minimum value in identical exponent number, the L values are exactly the lag order needed；If AIC and SIC values take in different rank
When obtaining minimum value, lag order is determined referring again to likelihood ratio test method LR.
It is below the specific implementation process of the present invention.
As shown in figure 1, the present invention is first to the economic data with prediction area (country, province or city), (fixed assets are thrown
Money, the total retail sales of consumer goods, net export total volume of trade) and electricity needs data (Analyzing Total Electricity Consumption) be collected processing
By establishing Vector Autoression Models, future electrical energy demand is predicted, is comprised the following steps that：
Step 1: the economic data and electricity needs data in area to be predicted (country, province or city) are collected, and to data
Arrangement cleaning is carried out, is stored in database；
Step 2: definition needs the minimum interval for the time cycle and data predicted, and phase is read from database
Economic data and electricity needs data between seasonable, carry out logarithmetics processing；
Step 3: for 4 column datas (investment in fixed assets, the total retail sales of consumer goods, net export trade after processing
Total value, Analyzing Total Electricity Consumption) carry out stationary test：Examined using dickey-fuller (DF), examine each row sequence data
With the presence or absence of unit root, if in the presence of, difference processing is carried out to the time series, then to difference sequence progress dickey-
Fuller (DF) is examined, and if there are still further difference processing if unit root, above step is repeated, until sequence is in the absence of single
Untill position root (i.e. steady)；
Step 4: using 4 column datas in database as the endogenous variable of vector auto regression, ignore the shadow of external variable
Ring, structure Vector Autoression Models equation is as follows：
Xt=α+A1Xt-1+…+AkXt-k+εt
Wherein, εtRepresent zero-mean, the random perturbation of finite variance, XtRepresent m × 1 of m endogenous variable composition of t phases
Vector, Xt-kIt is vectorial to represent m × 1 of the m endogenous variable composition of hysteresis k phases, AkM × m coefficient matrixes are represented, it is normal that α represents m × 1
Number vector；It is as follows that above formula is write as matrix form：
Step 5: utilize the red pond information criterions of Akaike information criterion (AIC) and Schwarz
Information criterion (SIC) Schwarz information criterions determine the lag order of variable in model, its calculation formula
It is as follows：
Wherein, M is Maximum-likelihood estimation, and R is sample size, and L is lag order；The 1 AIC and SIC values for arriving k ranks are calculated,
When both get minimum value in identical exponent number, the L values are exactly the lag order needed；If AIC and SIC values take in different rank
When obtaining minimum value, lag order is determined referring again to likelihood ratio test method LR.
Step 6: for the data set arranged in step 3, corresponding statistical analysis software (STATA, EVIEWS) structure is utilized
Vector Autoression Models are made, on this basis, electricity consumption is predicted using obtained Vector Autoression Models.
The present invention combines the economic data of statistics bureau's announcement and the electricity consumption data of power system, passage time sequence
Correlation analysis and causality analysis, the electricity consumption influence factor of objective area (country, saving, city) is analyzed.Really
It is fixed that maximum economic indicator is influenceed on electricity consumption, and corresponding data is exported and forms endogenous variable.Meanwhile difference can be selected
Data time interval predict the electricity consumption under different cycles.
Above is presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, caused function are made
During with scope without departing from technical solution of the present invention, protection scope of the present invention is belonged to.
Claims (7)
- A kind of 1. electric power demand forecasting method based on Vector Autoression Models, it is characterised in that：By collecting area to be predicted Economic data and electric power data, utilize Vector Autoression Models, carry out electric power demand forecasting.
- 2. according to the method for claim 1, it is characterised in that：The economic data includes 3 economic indicators, respectively solid Determine assets investment, the total retail sales of consumer goods, net export total volume of trade；The electric power data includes Analyzing Total Electricity Consumption.
- 3. according to the method for claim 2, it is characterised in that：The economic data and electric power data need arrange clearly Wash, and carry out logarithmetics processing.
- 4. according to the method for claim 3, it is characterised in that：The economic data and electric power data carry out logarithmetics processing Afterwards, stationarity verification need to be carried out, i.e., is examined using dickey-fuller, examines each row sequence data to whether there is unit root, If in the presence of, difference processing is carried out to the sequence data, then dickey-fuller inspections are carried out to difference sequence, if still depositing In unit root then further difference processing, above step is repeated, until sequence is in the absence of untill unit root.
- 5. according to the method for claim 4, it is characterised in that：The Vector Autoression Models are：Xt=α+A1Xt-1+…+AkXt-k+εtWherein, εtRepresent zero-mean, the random perturbation of finite variance, XtIt is vectorial to represent m × 1 of m endogenous variable composition of t phases, Xt-kIt is vectorial to represent m × 1 of the m endogenous variable composition of hysteresis k phases, AkRepresent m × m coefficient matrixes, α represent the constant of m × 1 to Amount；It is as follows that above formula is write as matrix form：<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>1</mn> <mi>t</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>2</mn> <mi>t</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>t</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mi>&alpha;</mi> <mo>+</mo> <msub> <mi>A</mi> <mn>1</mn> </msub> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>1</mn> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>2</mn> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mo>...</mo> <mo>+</mo> <msub> <mi>A</mi> <mi>k</mi> </msub> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>1</mn> <mi>t</mi> <mo>-</mo> <mi>k</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mn>2</mn> <mi>t</mi> <mo>-</mo> <mi>k</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>x</mi> <mrow> <mi>m</mi> <mi>t</mi> <mo>-</mo> <mi>k</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <msub> <mi>&epsiv;</mi> <mi>t</mi> </msub> <mo>.</mo> </mrow>
- 6. according to the method for claim 5, it is characterised in that：Variable in the Vector Autoression Models is by steady Property verification economic data and electric power data.
- 7. according to the method for claim 6, it is characterised in that：The Vector Autoression Models need to pass through red pond information criterion The lag order of variable in model is determined with Schwarz information criterion, its calculation formula is as follows：<mrow> <mi>A</mi> <mi>I</mi> <mi>C</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>ln</mi> <mi> </mi> <mi>M</mi> </mrow> <mi>R</mi> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <mn>2</mn> <mi>L</mi> </mrow> <mi>R</mi> </mfrac> </mrow><mrow> <mi>S</mi> <mi>I</mi> <mi>C</mi> <mo>=</mo> <mo>-</mo> <mn>2</mn> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>ln</mi> <mi> </mi> <mi>M</mi> </mrow> <mi>R</mi> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <mn>2</mn> <mi>L</mi> <mi> </mi> <mi>ln</mi> <mi> </mi> <mi>R</mi> </mrow> <mi>R</mi> </mfrac> </mrow>Wherein, M is Maximum-likelihood estimation, and R is sample size, and L is lag order；The 1 AIC and SIC values for arriving k ranks are calculated, when two For person when identical exponent number gets minimum value, the L values are exactly the lag order needed；If AIC and SIC values obtain most in different rank During small value, lag order is determined referring again to likelihood ratio test method LR.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109523053A (en) * | 2018-09-26 | 2019-03-26 | 国网江苏省电力有限公司 | A kind of multipotency streaming system load forecasting method |
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| CN114593411A (en) * | 2022-02-28 | 2022-06-07 | 中国大唐集团科学技术研究院有限公司西北电力试验研究院 | Water supply control method and system for optimizing direct current furnace based on vector autoregression water-coal ratio |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102799512A (en) * | 2012-07-04 | 2012-11-28 | 南京邮电大学 | Virtual machine monitoring method in vector-autoregression-based cloud computing |
| CN104809522A (en) * | 2015-05-07 | 2015-07-29 | 国家电网公司 | Integrated energy predicting method |
| CN106203693A (en) * | 2016-07-05 | 2016-12-07 | 华北电力大学 | A kind of system and method for Power Output for Wind Power Field climbing event prediction |
| CN106600029A (en) * | 2016-11-02 | 2017-04-26 | 国网浙江省电力公司经济技术研究院 | Macro-economy predictive quantization correction method based on electric power data |
-
2017
- 2017-11-06 CN CN201711075768.5A patent/CN107748938A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102799512A (en) * | 2012-07-04 | 2012-11-28 | 南京邮电大学 | Virtual machine monitoring method in vector-autoregression-based cloud computing |
| CN104809522A (en) * | 2015-05-07 | 2015-07-29 | 国家电网公司 | Integrated energy predicting method |
| CN106203693A (en) * | 2016-07-05 | 2016-12-07 | 华北电力大学 | A kind of system and method for Power Output for Wind Power Field climbing event prediction |
| CN106600029A (en) * | 2016-11-02 | 2017-04-26 | 国网浙江省电力公司经济技术研究院 | Macro-economy predictive quantization correction method based on electric power data |
Non-Patent Citations (1)
| Title |
|---|
| 刘志坚 等: "基于社会经济指标影响的电力***月度负荷组合预测模型", 《昆明理工大学学报(自然科学版)》 * |
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