CN107220735A - A kind of multivariable rural power grids power predicating method of power industry classification - Google Patents
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Abstract
The invention discloses a kind of multivariable rural power grids power predicating method of power industry classification, it is characterised in that：The primary industry classified using Multi-variable Grey Model to power industry, secondary industry, the tertiary industry, resident living power utility amount are predicted respectively, improve accuracy rate.The basic procedure of the algorithm is：Plan as a whole somewhere grid company all departments and municipal sector, collect the history year for being related to all indexs in variable（5 years）Data, data prediction computing is carried out using gray model respectively to town dweller's household electricity in primary industry power consumption, secondary industry power consumption, tertiary industry power consumption and resident living power utility amount and life in the countryside electricity consumption after handling data, then the predicted value to power industry point industry carries out the Multi-variable Grey Model accuracy test of result of calculation.Inspection meets required precision, tries to achieve area's Analyzing Total Electricity Consumption predicted value.This method introduces regional urbanization rate as one of predictive variable, makes regional rural power grids power quantity predicting more efficient and accurate.There is actual directive significance to the planning upgrading of power network.
Description
Technical field
The present invention relates to a kind of regional Analyzing Total Electricity Consumption Forecasting Methodology of rural power grids, belong to power industry Electric Power Network Planning skill
Art field.
Background technology
The development of rural power grids is a complicated arduous system engineering, with scale is big, uncertain factor is more, relate to
And field it is wide the characteristics of.Assurance to power network development direction, be by present situation electrical network analysis, the prediction of load and electricity so as to
Carry out the determination and implementation of planning construction modification scheme and realize.And accurate power quantity predicting, then pair determine that a planning construction changes
The scheme of making has important impulse, it is therefore necessary to set up the accurate power predicating method of science.
The change of one regional Analyzing Total Electricity Consumption multiple variables or the coefficient result of factor in reality.Simultaneously
The development and change of single variable in these variables are not isolated presence, are generally influenceed by other variables, often
The other variables of influence.Accurately hold various variables or factor, especially major influence factors and its weight, development and change rule
Rule, is more conducive to strengthen the accuracy of power quantity predicting.For power predicating method, mainly there is current power load prediction at present
Method is basically divided into three major types：Classical Forecasting Methodology, traditional prediction method, modern Forecasting Methodology.Wherein classical Forecasting Methodology
Including electricity index scaling method per capita, unit consumption method, load density method, elastic coefficient method, proportionality coefficient growth method etc.；Classical forecast
Method includes trend extrapolation, time series method, regression analysis etc.；It is pre- that modern Forecasting Methodology includes fuzzy prediction method, grey
Survey method, artificial neural network method and combination forecasting method etc..
Existing method is primarily present problems with：
（1）The requirement to data such as elastic coefficient method, unit consumption method is all higher in classical Forecasting Methodology, and previous work amount is big, number
It is difficult according to collecting, and precision is less desirable, it is adaptable to Mid-long term load forecasting；
（2）The application of time series method is fairly simple in traditional prediction method, but when load data fluctuation is larger, prediction is missed
Difference is mainly used in short-term forecast than larger；Regression analysis equally accurate is less desirable, it is adaptable to Mid-long term load forecasting.
（3）The precision of prediction of Artificial Neural Network is higher；But the selection of input variable is more difficult to be determined, study
Process is relatively slow and needs complicated programming；The practical application of fuzzy prediction method shows to apply this method precision poor merely, held
It is vulnerable to the influence of subjective factor, it is adaptable to which future society economic development has the medium- and long-term forecasting of very big uncertain factor；Combination
Predicted method integrates the advantage of each model, and precision depends on the selection of selected single model algorithm and weight coefficient, precision
Restricted, process is complicated.
The content of the invention
The present invention proposes a kind of multivariable rural power grids power predicating method of power industry classification.This Forecasting Methodology
Traditional Forecasting Methodology is different to be：Each industry and electricity consumption of resident are predicted respectively, each branch is so predicted
Power consumption development trend is so that prediction is more accurate, and it can only be overall unidirectional increasing or decreasing prediction side to overcome Classical forecast
The drawbacks of method.This Forecasting Methodology is predicted using Multi-variable Grey Model each branch's power consumption, according to the several of influence power consumption
Individual important pertinency factor and power consumption are together predicted, urbanization rate is introduced for rural power grids as gray model variable, is made pre-
Measured value is more accurate.
The technical solution adopted in the present invention is：
First, the foundation and inspection of multivariable grey forecasting model
1. the foundation of multivariable grey forecasting model
It is assumed that non-negative original data vector sequence is
It is rightAccumulating generation is done, obtains generating ordered series of numbers
Can be by ordered series of numbersAt the time ofRegard continuous variable ast, and by ordered series of numbersThen regard the time ast
Function.If ordered series of numbersIt is rightRate of change produce influence, then can set up albefaction
Formula differential equation
IfFor system action sequence,,For correlative factor sequence,For1-AGO sequences, claimnFirst first-order ordinary differential equation system
For multivariable MGM（1,N）Model.
Order
Then formula（1）It can be converted into： (2)
Title formula（2）For the albinism differential equation of MGM (1, N) model.Its corresponding continuous time respective function is：
(3)
Discretization can arrive MGM (1, N) solution to model：
(4)
For identified parametersAWithB, will（2）Differential variation is carried out, is obtained：
(5)
Wherein：。
(6)
It is right（5）Formula, according to principle of least square method, can recognize and obtain parameterAWithB：
(7)
Wherein：
Will be by（7）The parameter drawn is brought into（4）In
(8)
Have using above formula regressive reduction original data sequence
2. the inspection of multivariable grey forecasting model
Tested with posteriority difference method, step is as follows：
（1）Seek residual errorAnd relative error：
（2）Seek residual error average
（3）Seek original data series average
（4）Seek original row covariance
（5）Seek residual covariance
In formula：：For original data sequence and predicted value data sequence；
N：Original data sequence length.
（6）Ratio is checked after calculatingCAnd small error possibilityP
According toC、PEvaluation model precision, evaluation criterion such as table 1.
The precision of prediction grade of table 1
Met after inspection after required precision, it is possible to carry out multivariable gray prediction.
2nd, each industry electricity consumption amount prediction of power industry classification
1. primary industry electricity demand forecasting
Farming power is largely well irrigation electricity consumption and processing of farm products electricity consumption.Farming power load character：It is seasonal strong, year
Maximum is using hour is low, power factor (PF) is low and rural area load configuration changes greatly.Seasonality be mainly reflected in spring agricultural irrigation and
Autumn agricultural irrigation, it is especially apparent in vast rural area.Farming power shows stronger Seasonal Analysis, but overall power consumption
Seldom, the ratio of power consumption is small in the whole society.As agricultural is from the extensive development pattern of tradition to new intensive modern agriculture
Development transformation, agriculture power consumption will slowly increase and amplitude is smaller.The present invention is adopted to primary industry electricity demand forecasting
It is that average gray model GM (1,1) is predicted with single argument gray model, is used as average grey using primary industry power consumption is main
The master variable of model.
2. secondary industry electricity demand forecasting
Industrial electricity accounts for society's electricity consumption amount significant proportion and more stable climate seasonal effect is small, but different industrial trades are each
There is its feature, its load characteristic and power consumption differ greatly.The state of development of industrial electricity and secondary industry GDP has closely
It is related.Heavy industry is more flourishing, and the GDP that it is created is more, and corresponding power consumption is bigger.Industrial output value is created most of
GDP, it is most of that corresponding power consumption also accounts for society's electricity consumption amount.With the adjustment of Economic-industrial Structure in recent years, secondary industry exists
The ratio of the industrial structure has reduced, and traditional highly energy-consuming and production capacity surplus industry such as steel and iron industry, cement industry wait different journeys
Degree is reduced, and its power consumption decreases.The new high in technological content and low hi-tech enterprise of power consumption is emerged in multitude, its electricity consumption
Amount will increase substantially.Wherein influence secondary industry power consumption main factor be secondary industry whole industry proportion,
The GDP of secondary industry.Therefore the present invention regard secondary industry power consumption as Multi-variable Grey Model secondary industry electricity demand forecasting
Master variable, regard secondary industry accounting and the two factors of secondary industry GDP as two correlated variables.
3. tertiary industry electricity demand forecasting
With the development of urbanization, the raising of people's income level and the change of consumption idea are greatly facilitated catering trade, tourist industry
Prosperity and development with the tertiary industry such as cultural industry.More and more widely using sky in addition to illumination in tertiary industry service trade
The electrical equipment sensitive to temperature climatic season such as tune, electric fan, refrigeration and heating so that though its power consumption shows not as good as industry
The trend increased rapidly.The GDP of the tertiary industry is with expanding economy cumulative year after year, and it accounts for the total GDP of industry ratio（Tertiary industry
Industry accounting）Also corresponding increase year by year.With the adjustment of Economic-industrial Structure, the tertiary industry is greatly developed, and corresponding electric power is needed
Asking significantly to be lifted.In urbanization process, the aggregation and consumption of population necessarily promote the development of related service industry, indirectly increase
The need for electricity of the tertiary industry.Therefore the present invention using generated energy as Multi-variable Grey Model tertiary industry electricity demand forecasting main transformer
Amount, assign tertiary industry accounting and the two factors of urbanization rate as two correlated variables.
4. resident living power utility amount is predicted
With the development of urbanization, people in the countryside flow to cities and towns from rural area, and increasing peasant turns into town dweller.Due to agriculture
Village is different with the cities and towns level of economic development, and living environment is different, disposable income is different, so town dweller and urban residents people
Equal power consumption is also differed.The factor of electricity consumption per capita of influence town dweller and urban residents mainly have resident average per capita disposable receipts
Enter, urbanization rate, using energy source structure and Seasonal.Per capita disposable income is higher, and its ability of improving the living condition is got over
By force, so household electrical appliance use is more universal.Urban residents' disposable income per capita is higher than urban residents people, and household electrical appliance are used
It is more universal, so cities and towns per capita household electricity consumption is higher than rural area.The power of urbanization is industrial expansion, and industrial expansion is universal
A large amount of labours are needed, therefore industrial development necessarily brings the raising that people take in.It may be said that urbanization rate is higher, industry is all the more
Reach, people's income is higher.Urbanization rate and resident living power utility have close indirect relation.From using energy source structural point
See, town dweller's life cooks the overwhelming majority using natural gas and electric energy, and urban residents use coal and part naturally mostly
Gas and electric energy.In terms of temperature climatic season angle, cooling in summer warming, town dweller uses air-conditioning or electric heater mostly,
Urban residents not as good as town dweller and winter use coal stove for heating mostly because of income level limitation air conditioning utilization rate.In summary,
Household electricity and urban residents' household electricity have larger difference to town dweller per capita, thus by town dweller per capita household electricity and
Grey Model is respectively adopted in urban residents' household electricity, wherein the main factor of influence resident's per capita household electricity consumption is per capita may be used
Dominate income and urbanization rate.Two related changes that the two factors are predicted as Multi-variable Grey Model resident living power utility amount
Amount.So three variables of town dweller's life per capita household electricity consumption Multi-variable Grey Model prediction are used per capita for town dweller's life
Electricity, urban residents' disposable income per capita and urbanization rate；Urban residents' life per capita household electricity consumption Multi-variable Grey Model is pre-
Three variables surveyed are urban residents' life per capita household electricity consumption, rural per-capita disposable income and urbanization rate.Its is relative
The town dweller's population and urban residents' population answered then are calculated with regional total number of people amount by urbanization rate and obtained.
3rd, whole society's electricity consumption total amount is calculated
The result for each industry electricity consumption amount that power industry is classified is summed, so as to obtain the prediction of regional Analyzing Total Electricity Consumption
As a result.
The invention will be further described with reference to the accompanying drawings and detailed description simultaneously.
Brief description of the drawings
Fig. 1 is multivariable index structure process analysis chart in the present invention；
Fig. 2 calculates schematic diagram for regional Analyzing Total Electricity Consumption in the present invention；
Embodiment
1. planning as a whole somewhere grid company all departments and municipal sector, the history year for being related to all indexs in variable is collected（5
Year）Data, are calculated such as urban population quantity for needing early stage to handle.
2. pair historical data carries out Accumulating generation sequence, primary industry power consumption, secondary industry are used using gray model
Electricity, tertiary industry power consumption and resident living power utility amount carry out data prediction computing respectively.
3. the predicted value of pair power industry point industry carries out the Multi-variable Grey Model accuracy test of result of calculation, according to
Proof-tested in model precision theoretical calculation goes out P, C value, and whether judgment models precision belongs to 1 grade, and required precision is met if belonging to 1 grade,
Can be for being predicted.
4. the progress summation operation that predicts the outcome of pair power industry point industry is finally predicted the outcome, the regional whole society uses
Electricity.
Claims (4)
1. a kind of multivariable rural power grids power predicating method of power industry classification, it is characterised in that：Including using multivariable grey
The primary industry that model is classified to power industry, secondary industry, the tertiary industry, resident living power utility amount are predicted respectively.
2. the multivariable urban and rural power grids power predicating method classified based on power industry according to claim 1, it is special
Levy and be：By primary industry power consumption, secondary industry power consumption, industry electricity consumption amount accounting and GDP, tertiary industry electricity consumption
Amount, industry electricity consumption amount accounting and urbanization rate, the cities and towns of resident living power utility and urban residents' life per capita household electricity consumption, per capita may be used
The variable that income, urbanization rate parameter are dominated respectively as gray model carries out prediction of result.
3. the multivariable urban and rural power grids power predicating method classified based on power industry according to claim 2, it is special
Levy and be：By to resident living power utility is divided into cities and towns and rural area, introducing urbanization rate parameter, rural power grids Area Inhabitants are lived
Electricity consumption carries out detailed predicting.
4. the multivariable urban and rural power grids power predicating method classified based on power industry according to claim 3, it is special
Levy and be：By rural power grids power industry classification Multi-variable Grey Model predicted value, examined using the posteriority difference method of gray model,
Analyzing Total Electricity Consumption is obtained in the case where precision meets the requirements to the summation of every profession and trade value to predict the outcome.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109325634A (en) * | 2018-10-24 | 2019-02-12 | 南方电网科学研究院有限责任公司 | Rural power grid load prediction method considering potential power consumption demand of user |
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| CN111985716A (en) * | 2020-08-21 | 2020-11-24 | 北京交通大学 | Passenger traffic prediction system with visualized passenger traffic information |
| CN112308338A (en) * | 2020-11-23 | 2021-02-02 | 国网北京市电力公司 | Power data processing method and device |
| CN113139672A (en) * | 2020-05-07 | 2021-07-20 | 国网能源研究院有限公司 | Resident life electricity consumption prediction method |
| CN113159359A (en) * | 2020-10-19 | 2021-07-23 | 国网能源研究院有限公司 | Method and device for predicting influence of trade contention end on total power consumption |
| CN114077927A (en) * | 2021-11-19 | 2022-02-22 | 国网辽宁省电力有限公司鞍山供电公司 | Industry GDP-power consumption analysis method |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325634A (en) * | 2018-10-24 | 2019-02-12 | 南方电网科学研究院有限责任公司 | Rural power grid load prediction method considering potential power consumption demand of user |
| CN109325634B (en) * | 2018-10-24 | 2022-03-18 | 南方电网科学研究院有限责任公司 | Rural power grid load prediction method considering potential power consumption demand of user |
| CN110852628A (en) * | 2019-11-13 | 2020-02-28 | 国网江西省电力有限公司经济技术研究院 | Rural medium and long term load prediction method considering development mode influence |
| CN110852628B (en) * | 2019-11-13 | 2023-09-12 | 国网江西省电力有限公司经济技术研究院 | Rural medium-long term load prediction method considering development mode influence |
| CN113139672A (en) * | 2020-05-07 | 2021-07-20 | 国网能源研究院有限公司 | Resident life electricity consumption prediction method |
| CN111985716A (en) * | 2020-08-21 | 2020-11-24 | 北京交通大学 | Passenger traffic prediction system with visualized passenger traffic information |
| CN111985716B (en) * | 2020-08-21 | 2024-05-14 | 北京交通大学 | Passenger traffic volume prediction system with passenger traffic information visualization function |
| CN113159359A (en) * | 2020-10-19 | 2021-07-23 | 国网能源研究院有限公司 | Method and device for predicting influence of trade contention end on total power consumption |
| CN112308338A (en) * | 2020-11-23 | 2021-02-02 | 国网北京市电力公司 | Power data processing method and device |
| CN114077927A (en) * | 2021-11-19 | 2022-02-22 | 国网辽宁省电力有限公司鞍山供电公司 | Industry GDP-power consumption analysis method |
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