CN107818386A - Power grid enterprises' Operating profit Forecasting Methodology - Google Patents
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Abstract
The invention discloses a kind of power grid enterprises' Operating profit Forecasting Methodology, including obtain power grid enterprises' history operation data；It is determined that influence the parameter of power grid enterprises' Operating profit and carry out analysis prediction；Establish power grid enterprises' management forecast model；It is determined that influence sensitiveness of the parameter to profit of power grid enterprises' Operating profit；Setting influences the excursion of the parameter of power grid enterprises' Operating profit；The probability distribution data of power grid enterprises' Operating profit are obtained, complete the prediction to the Operating profit of power grid enterprises.The present invention, which provides, considers the limitation of the affiliated Industry Policy of power grid enterprises and macroeconomic polytropy, the business revenue situation of enterprise itself, cost control, on the basis of the factors such as investment and financing lever, physical analogy is established, the analysis method of expert's artificial judgment amendment, the uncertainty in time span of forecast can be greatly decreased, the Operating profit in prediction power grid enterprises' time in future is realized, there is great application value to analyzing following enterprise business risk and decision-making.
Description
Technical field
Present invention relates particularly to a kind of power grid enterprises' Operating profit Forecasting Methodology.
Background technology
With the development and the improvement of people's living standards of national economy technology, electric energy has become the daily production of people
With secondary energy sources essential in life, production and life to people bring endless facility.
Meanwhile as electric power structure is deepened reforms, power grid enterprises' operations objective prediction and Analysis of Policy Making need more finely, more
Precisely, more rigorous data supporting and targetedly analysis method.Meanwhile macroeconomic polytropy in recent years, power network enterprise
The important indicators such as the business revenue situation of industry itself, cost control, investment and financing lever occur more obvious fluctuation with can not be pre-
The property surveyed, moreover, potential risk, electricity slowdown in growth, external economy environment and the electricity for causing the market share to be lost in are decontroled in sale of electricity side
Change the factors such as policy and uncertainty be present, can further increase the difficulty of enterprise operation forecasting of profit.For more science, rationally
Power grid enterprises' Operating profit is calculated, just the Operating profit (predicted value) set to early stage proposes higher requirement, but
The coupled relation between uncertainty and index of these indexs in time span of forecast is assessed in rare research, also without to multifactor wind
Power grid enterprises' Operating profit of dangerous probability analysis predicts targetedly method.
The content of the invention
It is an object of the invention to provide one kind to consider multifactor risk, and predicts that accurate power grid enterprises manage
Forecasting of profit method.
This power grid enterprises' Operating profit Forecasting Methodology provided by the invention, comprises the following steps：
S1. the history operation data of power grid enterprises is obtained；
S2. in the history operation data that step S1 is obtained, it is determined that influenceing the parameter of power grid enterprises' Operating profit；
S3. the parameter of the step S2 influence power grid enterprises Operating profits determined is analyzed and predicted；
S4. knot is predicted in the analysis obtained according to the parameter of the step S2 influence power grid enterprises Operating profits determined and step S3
Fruit, establish power grid enterprises' management forecast model；
S5. the power grid enterprises' management forecast model established according to step S4, it is determined that influenceing the ginseng of power grid enterprises' Operating profit
The sensitiveness of several Operating profits to power grid enterprises；
S6. power network is influenceed according to the obtained sensitivity analysis results of step S5 and step S3 analysis prediction result, setting
The excursion of the parameter of enterprise operation profit；
S7. the excursion and step S4 of the parameter of the influence power grid enterprises Operating profit obtained according to step S6 obtain
Power grid enterprises' management forecast model, obtain the probability distribution data of power grid enterprises' Operating profit, complete the warp to power grid enterprises
The prediction of commercial profit.
The history operation data of power grid enterprises described in step S1, specifically include power grid operation data, power network historical forecast
Data and power grid enterprises' management data etc..
Really the parameter for ringing power grid enterprises' Operating profit is fixed described in step S2, it is specially true using the principle of expert judging
The fixing parameter for ringing power grid enterprises' Operating profit.
The parameter for influenceing power grid enterprises Operating profits described in step S2, specifically include short term loan benchmark interest rate, long-term
Benchmark interest rate, electricity sales amount growth rate, the electricity price of providing a loan change, project under construction turns solid rate, line loss per unit and allowance for depreciation etc..
The parameter to influence power grid enterprises Operating profit described in step S3 is analyzed and predicted, specially using as follows
Principle is analyzed and predicted：
R1. using first-order autoregression moving average ARIMA models to short term loan interest rate, long-term loan interest rate, allowance for depreciation
Analyzed and predicted with line loss per unit；
R2. electricity price is changed using gray forecasting method and line loss per unit carries out analysis prediction；
R3. BP neural network is used, with reference to macroeconomic data and environmental data, electricity sales amount and electricity sales amount growth rate are entered
Row analysis prediction.
Described macroeconomic data includes GDP, CPI, PPI and fixed investment.
Power grid enterprises' management forecast model is established described in step S4, specially establishes power grid enterprises' warp using following steps
Seek forecast model：
A. structure proportion method is used, power selling income is predicted according to electricity sales amount and electricity price variable；Using time series method
Predict other operating incomes in addition to power selling income；
B. the operating income of other operating incomes of the power selling income according to step A and power grid enterprises prediction power network；
C. according to sale of electricity growth rate and line loss per unit, purchase of electricity is predicted using equation below：
Purchase of electricity=last electricity sales amount * (1+ electricity sales amounts growth rate)/(1- line losses per unit)
D. the purchase of electricity obtained according to step C, purchases strategies are calculated with reference to power purchase voltage；
E. solid rate, allowance for depreciation, cost of labor, material are turned according to project under construction and repairs rate and the prediction power transmission and distribution of other rates
Cost；
F. running cost is predicted by purchases strategies, power transmission and distribution cost and other operating costs；
G. according to short term loan interest rate and long-term loan interest rate, using classification accounting method prediction financial expenses；
H. using growth rate method prediction operation cost, administration fee, Write-downs loss, investment return, non-business income,
Non-operating outlay；
I. according to step A~H result of calculation, using the Operating profit forecast model of following formula calculating power grid enterprises：
Total profit=operating income-running cost-business tax and additional-financial expenses-operation cost-administration fee
With-Write-downs loss+investment return+non-business income-non-operating outlay.
It is fixed really described in step S5 and rings the sensitivity of the parameter of power grid enterprises Operating profits to the Operating profit of power grid enterprises
Property, specially determine sensitiveness using following steps：
A. the parameter of power grid enterprises' Operating profit is influenceed by adjusting and records the change of power grid enterprises' Operating profit in real time
Scope, so as to obtain the sensitivity coefficient of each factor；The value size of the sensitivity coefficient is：If power grid enterprises are influenceed through commercial
The ratio of the parameter i of profit mobility scale and the excursion of power grid enterprises Operating profits is smaller, then influences power grid enterprises and manage
The parameter i of profit sensitivity coefficient is bigger；It is bigger to influence the sensitivity coefficient of the parameter of power grid enterprises' Operating profit, shows that power network is looked forward to
The Operating profit of industry is stronger to the sensitiveness for influenceing the parameter of power grid enterprises' Operating profit；
B. choosing the maximum top n of sensitivity coefficient influences the parameter of power grid enterprises' Operating profit, as key parameter.
Setting described in step S6 influences the excursion of the parameter of power grid enterprises' Operating profit, specially using following step
Suddenly set：
(1) key parameter chosen to step S5, analysis and prediction result with reference to described in step S3, key parameter is obtained
Analysis prediction result, and combine expertise, excursion of the setting key parameter in the prediction time；
(2) test of normality is carried out to the key parameter described in step (1) using KS tests of normality method；
(3) if upchecking, the normal distribution model for setting key parameter isWherein μ is average, and σ is default discrete standard difference variable；
If examine not by the way that it is uniformly distributed function probability distribution to set key.
The probability distribution data of power grid enterprises' Operating profit are obtained described in step S7, specially using Monte Carlo simulation
Principle carries out risk analysis of multiple stochastic variable factors to Operating profit Index Influence, so as to obtain power grid enterprises' Operating profit
The probability distribution data of index.
This power grid enterprises' Operating profit Forecasting Methodology provided by the invention, considers the affiliated Industry Policy of power grid enterprises
Limitation and macroeconomic polytropy, the business revenue situation of enterprise itself, cost control, the factor such as investment and financing lever basis
On, physical analogy is established, the analysis method of expert's artificial judgment amendment, the uncertainty in time span of forecast can be greatly decreased, is realized
The Operating profit in prediction power grid enterprises' time in future, have with decision-making to analyzing following enterprise business risk and greatly apply valency
Value.
Brief description of the drawings
Fig. 1 is the method flow diagram of the inventive method.
Embodiment
It is the method flow diagram of the inventive method as shown in Figure 1：This power grid enterprises' Operating profit provided by the invention is pre-
Survey method, comprises the following steps：
S1. the history operation data of power grid enterprises is obtained；Specifically include power grid operation data, power network historical forecast data and
Power grid enterprises' management data etc., associated by data preparation, cleaning with synthesis, so as to form final analytical database；
S2. in the history operation data that step S1 is obtained, it is determined that influenceing the parameter of power grid enterprises' Operating profit；Specially
Determine to influence the parameter of power grid enterprises' Operating profit using the principle of expert judging；In the specific implementation, the bag that the present invention chooses
Include short term loan benchmark interest rate, long-term loan benchmark interest rate, electricity sales amount growth rate, electricity price variation, the solid rate of project under construction turn, line loss
Rate and allowance for depreciation etc.；
S3. the parameter of the step S2 influence power grid enterprises Operating profits determined is analyzed and predicted；Specially use
Following principle is analyzed and predicted：
R1. using first-order autoregression moving average ARIMA models to short term loan interest rate, long-term loan interest rate, allowance for depreciation
Analyzed and predicted with line loss per unit；
R2. electricity price is changed using gray forecasting method and line loss per unit carries out analysis prediction；
R3. BP neural network is used, with reference to macroeconomic data (including GDP, CPI, PPI and fixed investment etc.)
With environmental data (such as meteorological data, disaster data etc.), analysis prediction is carried out to electricity sales amount and electricity sales amount growth rate；
S4. knot is predicted in the analysis obtained according to the parameter of the step S2 influence power grid enterprises Operating profits determined and step S3
Fruit, establish power grid enterprises' management forecast model；Specially power grid enterprises' management forecast model is established using following steps：
A. structure proportion method is used, power selling income is predicted according to electricity sales amount and electricity price variable；Using time series method
Predict other operating incomes in addition to power selling income；
B. the operating income of other operating incomes of the power selling income according to step A and power grid enterprises prediction power network；
C. according to sale of electricity growth rate and line loss per unit, purchase of electricity is predicted using equation below：
Purchase of electricity=last electricity sales amount * (1+ electricity sales amounts growth rate)/(1- line losses per unit)
D. the purchase of electricity obtained according to step C, purchases strategies are calculated with reference to power purchase voltage；
E. solid rate, allowance for depreciation, cost of labor, material are turned according to project under construction and repairs rate and the prediction power transmission and distribution of other rates
Cost；
F. running cost is predicted by purchases strategies, power transmission and distribution cost and other operating costs；
G. according to short term loan interest rate and long-term loan interest rate, using classification accounting method prediction financial expenses；
H. using growth rate method prediction operation cost, administration fee, Write-downs loss, investment return, non-business income,
Non-operating outlay；
I. according to step A~H result of calculation, using the Operating profit forecast model of following formula calculating power grid enterprises：
Total profit=operating income-running cost-business tax and additional-financial expenses-operation cost-administration fee
With-Write-downs loss+investment return+non-business income-non-operating outlay.
S5. the power grid enterprises' management forecast model established according to step S4, it is determined that influenceing the ginseng of power grid enterprises' Operating profit
The sensitiveness of several Operating profits to power grid enterprises；Specially sensitiveness is determined using following steps：
A. the parameter of power grid enterprises' Operating profit is influenceed by adjusting and records the change of power grid enterprises' Operating profit in real time
Scope, so as to obtain the sensitivity coefficient of each factor；The value size of the sensitivity coefficient is：If power grid enterprises are influenceed through commercial
The ratio of the parameter i of profit mobility scale and the excursion of power grid enterprises Operating profits is smaller, then influences power grid enterprises and manage
The parameter i of profit sensitivity coefficient is bigger；It is bigger to influence the sensitivity coefficient of the parameter of power grid enterprises' Operating profit, shows that power network is looked forward to
The Operating profit of industry is stronger to the sensitiveness for influenceing the parameter of power grid enterprises' Operating profit；
B. choosing the maximum top n of sensitivity coefficient influences the parameter of power grid enterprises' Operating profit, as key parameter；
S6. power network is influenceed according to the obtained sensitivity analysis results of step S5 and step S3 analysis prediction result, setting
The excursion of the parameter of enterprise operation profit；Specially set using following steps：
(1) key parameter chosen to step S5, analysis and prediction result with reference to described in step S3, key parameter is obtained
Analysis prediction result, and combine expertise, excursion of the setting key parameter in the prediction time；
(2) test of normality is carried out to the key parameter described in step (1) using KS tests of normality method；
(3) if upchecking, the normal distribution model for setting key parameter isWherein μ is average, and σ is default discrete standard difference variable；
If examine not by the way that it is uniformly distributed function probability distribution to set key；
S7. the excursion and step S4 of the parameter of the influence power grid enterprises Operating profit obtained according to step S6 obtain
Power grid enterprises' management forecast model, multiple stochastic variable factors are carried out to Operating profit index using Monte Carlo simulation principle
The risk analysis of influence, complete the prediction to the Operating profit of power grid enterprises.
Claims (10)
1. a kind of power grid enterprises' Operating profit Forecasting Methodology, comprises the following steps：
S1. the history operation data of power grid enterprises is obtained；
S2. in the history operation data that step S1 is obtained, it is determined that influenceing the parameter of power grid enterprises' Operating profit；
S3. the parameter of the step S2 influence power grid enterprises Operating profits determined is analyzed and predicted；
S4. the analysis prediction result obtained according to the parameter of the step S2 influence power grid enterprises Operating profits determined and step S3,
Establish power grid enterprises' management forecast model；
S5. the power grid enterprises' management forecast model established according to step S4, it is determined that influenceing the parameter pair of power grid enterprises' Operating profit
The sensitiveness of the Operating profit of power grid enterprises；
S6. power grid enterprises are influenceed according to the obtained sensitivity analysis results of step S5 and step S3 analysis prediction result, setting
The excursion of the parameter of Operating profit；
S7. the power network that the excursion and step S4 of the parameter of the influence power grid enterprises Operating profit obtained according to step S6 obtain
Enterprise operation forecast model, the probability distribution data of power grid enterprises' Operating profit are obtained, complete the Operating profit to power grid enterprises
Prediction.
2. power grid enterprises' Operating profit Forecasting Methodology according to claim 1, it is characterised in that the power network described in step S1
The history operation data of enterprise, specifically include power grid operation data, power network historical forecast data and power grid enterprises' management data.
3. power grid enterprises' Operating profit Forecasting Methodology according to claim 2, it is characterised in that the determination described in step S2
The parameter of power grid enterprises' Operating profit is influenceed, specially determines to influence power grid enterprises' Operating profit using the principle of expert judging
Parameter.
4. power grid enterprises' Operating profit Forecasting Methodology according to claim 3, it is characterised in that the influence described in step S2
The parameter of power grid enterprises' Operating profit, specifically include short term loan benchmark interest rate, long-term loan benchmark interest rate, electricity sales amount and increase
Rate, electricity price change, project under construction turns solid rate, line loss per unit and allowance for depreciation.
5. power grid enterprises' Operating profit Forecasting Methodology according to claim 4, it is characterised in that described in step S3 to shadow
The parameter for ringing power grid enterprises' Operating profit is analyzed and predicted, is specially analyzed and is predicted using following principle：
R1. using first-order autoregression moving average ARIMA models to short term loan interest rate, long-term loan interest rate, allowance for depreciation and line
Loss rate is analyzed and predicted；
R2. electricity price is changed using gray forecasting method and line loss per unit carries out analysis prediction；
R3. BP neural network is used, with reference to macroeconomic data and environmental data, electricity sales amount and electricity sales amount growth rate are divided
Analysis prediction.
6. power grid enterprises' Operating profit Forecasting Methodology according to claim 5, it is characterised in that described macroeconomy number
According to including GDP, CPI, PPI and fixed investment.
7. power grid enterprises' Operating profit Forecasting Methodology according to claim 6, it is characterised in that the foundation described in step S4
Power grid enterprises' management forecast model, specially establish power grid enterprises' management forecast model using following steps：
A. structure proportion method is used, power selling income is predicted according to electricity sales amount and electricity price variable；Predicted using time series method
Operating income in addition to power selling income；
B. the business of the power selling income according to step A and the prediction power network of the operating income in addition to power selling income of power grid enterprises
Income；
C. according to sale of electricity growth rate and line loss per unit, purchase of electricity is predicted using equation below：
Purchase of electricity=last electricity sales amount * (1+ electricity sales amounts growth rate)/(1- line losses per unit)
D. the purchase of electricity obtained according to step C, purchases strategies are calculated with reference to power purchase voltage；
E. solid rate, allowance for depreciation, cost of labor, material are turned according to project under construction and repairs rate and except project under construction turns solid rate, depreciation
Rate, cost of labor, material repair the forecasting of cost power transmission and distribution cost outside rate；
F. running cost is predicted by purchases strategies, power transmission and distribution cost and the operating cost in addition to purchases strategies, power transmission and distribution cost；
G. according to short term loan interest rate and long-term loan interest rate, using classification accounting method prediction financial expenses；
H. using growth rate method prediction operation cost, administration fee, Write-downs loss, investment return, non-business income and battalion
Expenditure out of trade；
I. according to step A~H result of calculation, using the Operating profit forecast model of following formula calculating power grid enterprises：
Total profit=operating income-running cost-business tax and additional-financial expenses-operation cost-administration fee-money
Produce depreciation loss+investment return+non-business income-non-operating outlay.
8. power grid enterprises' Operating profit Forecasting Methodology according to claim 7, it is characterised in that the determination described in step S5
The parameter of influence power grid enterprises Operating profit is specially determined to the sensitiveness of the Operating profit of power grid enterprises using following steps
Sensitiveness：
A. the parameter of power grid enterprises' Operating profit is influenceed by adjusting and records the excursion of power grid enterprises' Operating profit in real time,
So as to obtain the sensitivity coefficient of each factor；The value size of the sensitivity coefficient is：If influence power grid enterprises' Operating profit
The ratio of parameter i mobility scale and the excursion of power grid enterprises Operating profits is smaller, then influences power grid enterprises' Operating profit
Parameter i sensitivity coefficient it is bigger；It is bigger to influence the sensitivity coefficient of the parameter of power grid enterprises' Operating profit, shows power grid enterprises
Operating profit is stronger to the sensitiveness for influenceing the parameter of power grid enterprises' Operating profit；
B. choosing the maximum top n of sensitivity coefficient influences the parameter of power grid enterprises' Operating profit, as key parameter.
9. power grid enterprises' Operating profit Forecasting Methodology according to claim 8, it is characterised in that the setting described in step S6
The excursion of the parameter of power grid enterprises' Operating profit is influenceed, is specially set using following steps：
(1) key parameter chosen to step S5, analysis and prediction result with reference to described in step S3, point of key parameter is obtained
Prediction result is analysed, and combines expertise, excursion of the setting key parameter in the prediction time；
(2) test of normality is carried out to the key parameter described in step (1) using KS tests of normality method；
(3) if upchecking, the normal distribution model for setting key parameter isWherein μ is average, and σ is default discrete standard difference variable；
If examine not by the way that it is uniformly distributed function probability distribution to set key.
10. power grid enterprises' Operating profit Forecasting Methodology according to claim 9, it is characterised in that obtained described in step S7
The probability distribution data of power grid enterprises' Operating profit, multiple stochastic variable factors are specially carried out using Monte Carlo simulation principle
Risk analysis to Operating profit Index Influence, so as to obtain the probability distribution data of power grid enterprises' Operating profit index.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109002925A (en) * | 2018-07-26 | 2018-12-14 | 北京京东金融科技控股有限公司 | Traffic prediction method and apparatus |
| CN109118013A (en) * | 2018-08-29 | 2019-01-01 | 黑龙江工业学院 | A kind of management data prediction technique, readable storage medium storing program for executing and forecasting system neural network based |
| WO2019201001A1 (en) * | 2018-04-20 | 2019-10-24 | 阿里巴巴集团控股有限公司 | Fund forecasting method and device, and electronic device |
| CN111047067A (en) * | 2018-10-12 | 2020-04-21 | 国家电投集团信息技术有限公司 | Real-time daily profit prediction method and system |
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| WO2019201001A1 (en) * | 2018-04-20 | 2019-10-24 | 阿里巴巴集团控股有限公司 | Fund forecasting method and device, and electronic device |
| CN109002925A (en) * | 2018-07-26 | 2018-12-14 | 北京京东金融科技控股有限公司 | Traffic prediction method and apparatus |
| CN109118013A (en) * | 2018-08-29 | 2019-01-01 | 黑龙江工业学院 | A kind of management data prediction technique, readable storage medium storing program for executing and forecasting system neural network based |
| CN111047067A (en) * | 2018-10-12 | 2020-04-21 | 国家电投集团信息技术有限公司 | Real-time daily profit prediction method and system |
| WO2021004324A1 (en) * | 2019-07-09 | 2021-01-14 | 平安科技（深圳）有限公司 | Resource data processing method and apparatus, and computer device and storage medium |
| CN112651572A (en) * | 2020-12-31 | 2021-04-13 | 车主邦（北京）科技有限公司 | Profit prediction method and apparatus |
| CN114155072A (en) * | 2021-11-23 | 2022-03-08 | 安徽经邦软件技术有限公司 | Financial prediction model construction method and system based on big data analysis |
| CN115330531A (en) * | 2022-09-05 | 2022-11-11 | 南方电网数字电网研究院有限公司 | Enterprise risk prediction method based on electricity utilization change period |
| CN115330531B (en) * | 2022-09-05 | 2023-12-22 | 南方电网数字电网研究院有限公司 | Enterprise risk prediction method based on electricity consumption fluctuation period |
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