CN110415140A - A kind of annual power consumption prediction method based on industrial production person's producer price index - Google Patents
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Abstract
The invention discloses a kind of methods based on industrial production person's producer price exponential forecasting Analyzing Total Electricity Consumption, and by PPI history value, the electricity consumption history value in previous year, linear recurrence obtains the PPI- electricity consumption growth indices regressive prediction model in somewhere；By the monthly PPI of prior year, the announced monthly PPI of this forecast year State Statistics Bureau, current year PPI prediction model is established, the PPI predicted value in the current year is obtained；According to current year PPI predicted value, PPI- electricity consumption growth indices regressive prediction model, electricity consumption growth indices, the electricity consumption of current year this area are predicted.Present invention introduces the annual power consumption prediction values obtained after industrial production person's producer price index closer to the true value of Urban Annual Electrical Power Consumption amount, can expect to provide relatively reliable foundation for annual power generation arrangement, power supply and demand situation.
Description
Technical field
The invention belongs to quantity of electricity requirement forecasting technical fields, are based on industrial production person's producer price more particularly, to one kind
The annual power consumption prediction method of grid index.
Background technique
Electricity consumption or Analyzing Total Electricity Consumption refer to all power consumption total amounts with electrical domain such as primary ,secondary and tertiary industries,
Including commercial power, farming power, commercial power, residential electricity consumption, communal facility electricity consumption and other electricity consumptions etc..As society passes through
The development of Ji, living standards of the people are continuously improved, and Analyzing Total Electricity Consumption is continuously increased, and the accuracy rate of power quantity predicting is to electric power enterprise
Development have large effect.Power quantity predicting be Electric Power Network Planning work pith, result to grid power transmission, power generation with
And the construction of electric power facility has important directive significance.In the case where guaranteeing power load, how power quantity predicting is improved
Accuracy is to guarantee that power supply quality is most important.
Existing power predicating method has certain limitation: (1) tracing analysis method, different model data result phases
Difference is larger；(2) elastic coefficient method, elastic coefficient method is closely bound up with national economy, with the development of the social economy, electricity consumption is
It is not remarkable and national economy hook.
Industrial production person's producer price index is the change for reflecting whole industrial products producer price total levels in the regular period
The relative number of dynamic trend and amplitude of fluctuation, is the principal economic indicators of production field price change situation.However, industry is raw at present
Production person's producer price index is mostly used to formulate related economic policy and carry out national economic accounting, there is not yet being used to carry out area
The explanation of Analyzing Total Electricity Consumption prediction.Therefore, it is necessary to study a kind of new electricity demand forecasting method.
Authorization Notice No. is that the patent application of CN105574325B discloses a kind of medium-term and long-term electricity consumption of combination demographic indicator
Prediction technique is measured, obtains in the passing k in area to be predicted all types of user electricity consumption in every year and permanent resident population's quantity first；Then
Consider that power consumption growth factor calculates power consumption predicted value per capita per capita；Permanent resident population's quantitative forecast value is calculated later；It will per capita
Power consumption predicted value is multiplied with permanent resident population's quantitative forecast value, obtains the sum of resident's electricity consumption and commercial user's electricity consumption
Predicted value；It is pre- that industrial user's electricity demand forecasting value, non-technical family electricity demand forecasting value and other users electricity consumption are calculated later
Measured value；Finally calculate total electricity predicted value.The invention prediction technique algorithm is simple, can reduce data requirements amount, and operability is high,
The key point of various types power consumer electricity demand forecasting is effectively held, prediction result precision is high, can provide battalion for power supply enterprise
Sell decision support.However, the invention actually use be still tracing analysis method, the prediction result that this method obtains and true
Real result difference is larger.
The patent application of Publication No. CN109492818A discloses a kind of entitled based on energy development and Shapley value
Electricity demand forecasting method, comprising the following steps: based on the major economic factor for influencing the energy and electricity consumption development, predict economic hair
Open up situation；Energy development situation is predicted using multivariate regression models；According to history electricity consumption data and multi-energy data, based on described
The prediction result of economic development situation and energy development situation carries out the development of region electricity consumption using a variety of electricity demand forecasting models
Prediction, obtains multiple electricity demand forecasting values；It is weighted processing by the theoretical multiple electricity demand forecasting values of Shapley value, is obtained
Final electricity demand forecasting result.Compared with prior art, finally resulting electricity demand forecasting result can be examined preferably for the invention
Long-term electricity consumption increases situation of change in worry, can provide foundation for power grid long term planning target, polygonal from economy and the energy
Degree accurately analyzes electricity consumption conditions of demand, provides effective reference for Electric Power Network Planning.However, the patent application is to be directed to be
Power grid long term planning target provides foundation, and it is impossible to meet the required precisions of annual power consumption prediction for prediction result.
Summary of the invention
It is dispatched from the factory in view of this, in view of the deficiencies of the prior art, it is an object of the present invention to provide one kind based on industrial production person
The annual power consumption prediction method of price index introduces the annual power consumption prediction obtained after industrial production person's producer price index
It is worth the true value closer to Urban Annual Electrical Power Consumption amount, can expects to provide more for annual power generation arrangement, power supply and demand situation
Reliable foundation.
In order to achieve the above objectives, the invention adopts the following technical scheme:
A kind of annual power consumption prediction method based on industrial production person's producer price index, comprising the following steps:
Step 1 calculates electricity consumption growth indices annual in previous history year；
Step 2, the electricity consumption growth indices according to obtained in the PPI history value and step 1 in previous year, establish PPI- use
Electricity growth indices regressive prediction model；
Step 3, the monthly PPI average value for calculating this forecast year and previous year；
Step 4 establishes this forecast year PPI prediction model；
Step 5, by obtained forecast year PPI value PPI in step 4iThe PPI- electricity consumption growth for substituting into step 2 refers to
In number regressive prediction model, this forecast year electricity consumption growth indices predicted value, electricity demand forecasting value are obtained.
Further, in step 1, the calculation formula of electricity consumption growth indices annual in previous history year is as follows:
Wherein, subscript n represents time, Elec-raten、Elecn、Elecn-1The electricity consumption growth indices of respectively n, n
Year electricity consumption, n-1 electricity consumption.
Further, in step 2, PPI- electricity consumption growth indices regressive prediction model is as follows:
Elec-rate′n=aPPIn+c
Wherein, subscript n represents time, Elec-rate 'nTo refer to through the growth of the electricity consumption in regression forecasting obtained n year
Number match value, PPInFor the PPI value in n year, a, c are respectively according to obtained by the electricity consumption growth indices of historical years, PPI value
Linear regression coeffficient and constant.
Further, in step 3, this forecast year and the monthly PPI average value of previous year it is as follows:
Wherein, subscript i indicates year, and subscript m indicates month, and m is the integer from 1 to 12,For according to a upper i-
The PPI value of the 1 annual m monthWith the PPI value of this prediction i annual m monthAnd obtained average value, due to this
The electricity demand forecasting in year is often arranged in spring, and m is less than or equal to 4.
Further, in step 4, this forecast year PPI prediction model it is as follows:
Wherein,For to the monthly PPI average value in step 3It carries out monthly obtained from Time Series Regression
The match value of PPI average value, a ', c ' are respectively to be fitted resulting linear regression coeffficient and constant through time series, by m=12 generation
Enter in linear fit equation, the PPI average value match value that December is year end can be obtainedAccording to the PPI average value at year end
Match valueWith December previous year i.e. upper year end PPI valueObtain this forecast year December namely sheet
The PPI predicted value in year
Further, in steps of 5, by obtained forecast year PPI value PPI in step 4iSubstitute into step 2
In PPI- electricity consumption growth indices regressive prediction model, it is pre- to obtain this forecast year electricity consumption growth indices predicted value, electricity consumption
Measured value calculation formula is as follows:
Wherein, Elec-rate 'i、Elec′i、Eleci-1Respectively this forecast year electricity consumption growth indices predicted value, sheet
Forecast year electricity demand forecasting value, previous year electricity consumption.
Further, the data that the PPI data which uses are issued from national statistics board web, index
Class is " industrial production person's producer price index (same period last year=100) ".
Further, in this forecast year PPI prediction model of step 4, the monthly PPI value in certain year December
The PPI value PPI in the yeari, annual PPI history value in step 2 three meaning it is consistent.
The beneficial effects of the present invention are:
It is well known that power quantity predicting is the pith of Electric Power Network Planning work, result is to grid power transmission, power generation and electricity
The construction of power facility has important directive significance.In the case where guaranteeing power load, the accurate of power quantity predicting how is improved
Property to guaranteeing that power supply quality is most important.However, mainly having tracing analysis method and elasticity for the method for power quantity predicting at present
Y-factor method Y.Wherein, tracing analysis method is called regression analysis, is exactly to pass through statistical analysis variable using theory of regression analysis
Historical data determines the functional relation between each variable, realizes the purpose predicted time span of forecast electricity consumption, tracing analysis method
Mature with Parameters in Regression Model estimation technique, process is simple, the fast advantage of predetermined speed, but this method wants historical data
Ask high, when data are there are when large error or incompleteness, different model data result differences is larger.And elastic coefficient method, it utilizes
Electricity elasticity coefficients as the relationship reflected between electric power industry development and the national economic development, are illustrated in macroeconomics
A kind of generality index of development trend can be used as and measure the ginseng whether electric power development adapts to the national economic development
Number；Electricity elasticity coefficients predict electricity sales amount, key be your coefficient of elasticity of selected ground whether economical rationality, it is necessary to it is first pre-
Survey the growth rate of regional GDP, but it is reasonable to prove great numerical value without enough scientific basis and method, together
When, elastic coefficient method is closely bound up with national economy, with the development of the social economy, electricity consumption has not been remarkable and national
Economy hook.
Industrial production person's producer price index is the change for reflecting whole industrial products producer price total levels in the regular period
The relative number of dynamic trend and amplitude of fluctuation, is the principal economic indicators of production field price change situation.However, industry is raw at present
Production person's producer price index is mostly used to formulate related economic policy and carry out national economic accounting, there is not yet being used to carry out area
The explanation of Analyzing Total Electricity Consumption prediction.That is, this field uses industrial production person's producer price exponent pair annual not yet
The case that electricity consumption is predicted, those skilled in the art are never to consider according to conventional annual power consumption prediction method
To based on industrial production person's producer price exponential forecasting year electricity consumption.Moreover, even if those skilled in the art may consider
It is predicted to using industrial production person's producer price exponent pair year electricity consumption, but how to establish model, how according to phase
It closes model to predict annual electricity consumption, is all that unknown, not ready-made technology can be used.The present invention passed through with former years
PPI history value, the electricity consumption history value of degree, linear recurrence obtain the PPI- electricity consumption growth indices regression forecasting mould in somewhere
Type；By the monthly PPI of prior year, the announced monthly PPI of this forecast year State Statistics Bureau, current year PPI prediction is established
Model obtains the PPI predicted value in the current year；According to current year PPI predicted value, PPI- electricity consumption growth indices regression forecasting mould
Type predicts electricity consumption growth indices, the electricity consumption of current year this area.The present invention compares and the electricity consumption based on GDP speedup
The relationship of prediction technique, PPI and Urban Annual Electrical Power Consumption amount speedup will more closely, and the fluctuation situation of the two more tends to unanimously, thus is based on
The electricity demand forecasting of PPI is more more acurrate than the electricity demand forecasting increased based on GDP, and then can be used as annual power generation peace
The estimated foundation of row, power supply and demand situation.
The present invention is directed to the development of the social economy, living standards of the people are continuously improved, and Analyzing Total Electricity Consumption constantly increases
Add, profound change occurs for power structure, and the annual power quantity predicting value and whole year that are obtained using existing power predicating method are used
The true value difference of electricity is larger, and it is impossible to meet the requirements under the new situation to the accuracy of power quantity predicting, provide one kind and are based on
The annual power consumption prediction method of industrial production person's producer price index, firstly, calculating electricity consumption annual in previous history year
Measure growth indices；Later, the electricity consumption growth indices according to obtained in the PPI history value in previous year and step 1, establish PPI-
Electricity consumption growth indices regressive prediction model；Later, the monthly PPI average value of this forecast year and previous year are calculated；Later,
Establish this forecast year PPI prediction model；Finally, by obtained forecast year PPI value PPI in step 4iSubstitute into step 2
PPI- electricity consumption growth indices regressive prediction model in, obtain this forecast year electricity consumption growth indices predicted value, electricity consumption
Predicted value.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by written explanation
Specifically noted structure is achieved and obtained in book, claims and attached drawing.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is 2011 to the 2018 GDP indexes in Henan Province, PPI and electricity consumption growth indices change curve.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
Attached drawing, the technical solution of the embodiment of the present invention is clearly and completely described.Obviously, described embodiment is this hair
Bright a part of the embodiment, instead of all the embodiments.Based on described the embodiment of the present invention, ordinary skill
Personnel's every other embodiment obtained, shall fall within the protection scope of the present invention.
Embodiment 1:
Refering to attached drawing 1, by taking Henan Province's Analyzing Total Electricity Consumption prediction in 2018 as an example, one kind being based on industrial production person's producer price
The method of grid index prediction Analyzing Total Electricity Consumption, comprising the following steps:
Step 1 calculates electricity consumption growth indices annual in previous history year；
Electricity consumption growth indices calculation formula are as follows:
Wherein, subscript n represents time, Elec-raten、Elecn、Elecn-1The electricity consumption growth indices of respectively n, n
Year electricity consumption, n-1 electricity consumption.
In the present embodiment, using Henan Province's correlation annual data, electricity consumption data source in State Grid Henan Power Supply Company,
PPI data source is in State Statistics Bureau.Using 2011-2017 as the sample phase, 2018 are time span of forecast, related data such as 1 institute of table
Show:
In the present embodiment, annual electricity consumption growth indices are calculated since 2011.Wherein, electricity consumption in 2011 increases
Long index is 2659/2354*100=113.0, and electricity consumption growth indices are 2748/2659*100=103.3 within 2012, with this
Analogize.
Step 2 establishes PPI- electricity consumption growth indices regressive prediction model；
According to 2011-2017 electricity consumption growth indices, PPI data in table 2, the two return in excel and is divided
Analysis selects linear regression type, regressive prediction model through comparing are as follows:
Elec-rate′n=0.8584PPIn+ 18.541, (R2=0.67)
Wherein, subscript n represents time, Elec-rate 'nTo refer to through the growth of the electricity consumption in regression forecasting obtained n year
Number match value, PPInFor the PPI value in n year.
Step 3, the monthly PPI average value for calculating i.e. 2017 year of i.e. 2018 year of this forecast year and previous year；
Calculation formula are as follows:
Wherein, subscript indicates year, and subscript m indicates month, and m is the integer from 1 to 12.Generally, due to this year
The electricity demand forecasting of degree is often arranged in spring, and m is generally less than and is equal to 4, as shown in table 2:
Step 4, establish 2018 year PPI prediction model, and predicted；
Prediction model are as follows:
Wherein,For to monthly PPI average valueIt carries out predicting match value obtained from Time Series Regression,
A ', c ' are respectively to be fitted resulting linear regression coeffficient and constant through time series, and m=12 is substituted into linear fit equation,
PPI average value match value at the end of December can be obtained i.e.According to the match value of the PPI average value at year end
With the PPI value PPI of 2017 annual December i.e. 2017 year2017, so that it may obtain 2018 annual PPI values in DecemberNamely
The PPI predicted value PPI in 2018 years2018；
In the present embodiment, time series fitting is carried out to monthly PPI average value, obtains regression equation:
M=12 is substituted into above formula, obtains year end PPI mean value:
The then PPI predicted value in 2018 years are as follows:
Step 5 predicts 2018 annual electricity consumption growth indices, electricity consumption；
In the present embodiment, by 2018 year PPI value 103.48 obtained in step 4, the PPI- electricity consumption of step 2 is substituted into
It measures in growth indices regressive prediction model, obtains 2018 annual electricity consumption growth indices predicted values are as follows:
Elec-rate′2018=0.8584PPI2018+ 18.541=0.8584103.48+18.541=107.37
Then electricity demand forecasting value are as follows:
Elec′2018=Elec2017·Elec-rate′2018/ 100=3166107.37/100=3399.3
The predicted value of i.e. 2018 Henan Province's Analyzing Total Electricity Consumptions is 3399.3 hundred million kilowatt hours.
2018 obtained Prediction of annual electricity consumption results are compared with actual value, as shown in table 3, electricity demand forecasting error
Only 0.55%, there is accurate prediction effect.
Embodiment 2:
Be above by taking the prediction of Henan Province's Analyzing Total Electricity Consumption in 2018 as an example, can intuitively by predicted value and actual value into
Row compares.Next, one kind is referred to based on industrial production person's producer price by taking Henan Province's Analyzing Total Electricity Consumption prediction in 2019 as an example
The method of number prediction Analyzing Total Electricity Consumption, comprising the following steps:
Step 1 calculates electricity consumption growth indices annual in previous history year；
Electricity consumption growth indices calculation formula are as follows:
Wherein, subscript n represents time, Elec-raten、Elecn、Elecn-1The electricity consumption growth indices of respectively n, n
Year electricity consumption, n-1 electricity consumption.
In the present embodiment, using Henan Province's correlation annual data, electricity consumption data source in State Grid Henan Power Supply Company,
PPI data source is in State Statistics Bureau.Using 2011-2018 as the sample phase, 2019 are time span of forecast, the related data of sample phase
It is as shown in table 4:
In the present embodiment, annual electricity consumption growth indices are calculated since 2011.Wherein, electricity consumption in 2011 increases
Long index is 2659/2354*100=113.0, and electricity consumption growth indices are 2748/2659*100=103.3 within 2012, with this
Analogize.
Step 2 establishes PPI- electricity consumption growth indices regressive prediction model；
According to 2011-2018 electricity consumption growth indices, PPI data in table 1, the two return in excel and is divided
Analysis selects linear regression type, regressive prediction model through comparing are as follows:
Elec-rate′n=0.8718PPIn+ 17.266, (R2=0.70)
Wherein, subscript n represents time, Elec-rate 'nTo refer to through the growth of the electricity consumption in regression forecasting obtained n year
Number match value, PPInFor the PPI value in n year.
Step 3, the monthly PPI average value for calculating i.e. 2018 year of i.e. 2019 year of this forecast year and previous year；
Calculation formula are as follows:
Wherein, subscript indicates year, and subscript m indicates month, and m is the integer from 1 to 12.Generally, due to this year
The electricity demand forecasting of degree is often arranged in spring, and m is generally less than and is equal to 4, in the present embodiment, according to national statistics office data,
2019 years PPI were updated to April, as shown in table 5:
Step 4, establish 2019 year PPI prediction model, and predicted；
Prediction model are as follows:
Wherein,For to monthly PPI average valueIt carries out predicting match value obtained from Time Series Regression,
A ', c ' are respectively to be fitted resulting linear regression coeffficient and constant through time series, and m=12 is substituted into linear fit equation,
PPI average value match value at the end of December can be obtained i.e.According to the match value of the PPI average value at year end
With the PPI value PPI of 2018 annual December i.e. 2018 year2018, so that it may obtain 2019 annual PPI values in December
The PPI predicted value PPI in i.e. 2019 years2019；
In the present embodiment, time series fitting is carried out to monthly PPI average value, obtains regression equation:
M=12 is substituted into above formula, obtains year end PPI mean value:
The then PPI predicted value in 2019 years are as follows:
Step 5 predicts 2019 annual electricity consumption growth indices, electricity consumption；
In the present embodiment, by 2019 year PPI value 98.96 obtained in step 4, the PPI- electricity consumption of step 2 is substituted into
It measures in growth indices regressive prediction model, obtains 2019 annual electricity consumption growth indices predicted values are as follows:
Elec-rate′2019=0.8718PPI2019+ 17.266=0.871898.96+17.266=103.5
Then electricity demand forecasting value are as follows:
Elec′2019=Elec2018·Elec-rate′2019/ 100=3418103.5/100=3537.6
The predicted value of i.e. 2019 Henan Province's Analyzing Total Electricity Consumptions is 3537.6 hundred million kilowatt hours.
Table 6 is 2011 to the 2018 GDP indexes in Henan Province, PPI and electricity consumption growth indices tables of data, and table 6 is made
It is made line chart and obtains Fig. 2, compare and the electricity demand forecasting method based on GDP speedup, PPI and Urban Annual Electrical Power Consumption amount speedup
Relationship will more closely, and the fluctuation situation of the two more tends to unanimously, thus based on the electricity demand forecasting of PPI than being increased based on GDP
Electricity demand forecasting want more acurrate, and then can be used as the estimated foundation of annual power generation arrangement, power supply and demand situation.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, this field is common
Other modifications or equivalent replacement that technical staff makes technical solution of the present invention, without departing from technical solution of the present invention
Spirit and scope, be intended to be within the scope of the claims of the invention.
Claims (8)
1. a kind of annual power consumption prediction method based on industrial production person's producer price index, which is characterized in that including following
Step:
Step 1 calculates electricity consumption growth indices annual in previous history year；
Step 2, the electricity consumption growth indices according to obtained in the PPI history value and step 1 in previous year, establish PPI- electricity consumption
Growth indices regressive prediction model；
Step 3, the monthly PPI average value for calculating this forecast year and previous year；
Step 4 establishes this forecast year PPI prediction model；
Step 5, by obtained forecast year PPI value PPI in step 4iThe PPI- electricity consumption growth indices for substituting into step 2 are returned
Return in prediction model, obtains this forecast year electricity consumption growth indices predicted value, electricity demand forecasting value.
2. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 1,
It is characterized by: in step 1, the calculation formula of annual electricity consumption growth indices is as follows in previous history year:
Wherein, subscript n represents time, Elec-raten、Elecn、Elecn-1The electricity consumption growth indices of respectively n, n
The electricity consumption of electricity consumption, n-1.
3. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 2,
It is characterized by: in step 2, PPI- electricity consumption growth indices regressive prediction model is as follows:
Elec-rate′n=aPPIn+c
Wherein, subscript n represents time, Elec-rate 'nIt is quasi- through the electricity consumption growth indices in regression forecasting obtained n year
Conjunction value, PPInFor the PPI value in n year, a, c are respectively electricity consumption growth indices according to historical years, the obtained line of PPI value
Property regression coefficient and constant.
4. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 3,
It is characterized by: in step 3, this forecast year and the monthly PPI average value of previous year it is as follows:
Wherein, subscript i indicates year, and subscript m indicates month, and m is the integer from 1 to 12,For according to a upper i-1
Spend the PPI value of the m monthWith the PPI value of this prediction i annual m monthAnd obtained average value, m are less than or equal to
4。
5. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 4,
It is characterized by: in step 4, this forecast year PPI prediction model it is as follows:
Wherein,For to the monthly PPI average value in step 3Carry out monthly PPI obtained from Time Series Regression
The match value of average value, a ', c ' are respectively to be fitted resulting linear regression coeffficient and constant through time series, and m=12 is substituted into
In linear fit equation, the PPI average value match value that December is year end can be obtainedAccording to the PPI average value at year end
Match valueWith December previous year i.e. upper year end PPI valueObtain this forecast year December namely this year
The PPI predicted value of degree
6. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 5,
It is characterized by: in steps of 5, by obtained forecast year PPI value PPI in step 4iSubstitute into the PPI- electricity consumption of step 2
It measures in growth indices regressive prediction model, obtains this forecast year electricity consumption growth indices predicted value, electricity demand forecasting value calculates
Formula is as follows:
Wherein, Elec-rate 'i、Elec′i、Eleci-1Respectively this forecast year electricity consumption growth indices predicted value, this prediction
Annual power consumption prediction value, previous year electricity consumption.
7. a kind of annual electricity consumption based on industrial production person's producer price index according to any one of claims 1 to 6
Prediction technique, it is characterised in that: the data that the PPI data that the prediction technique uses are issued from national statistics board web,
Index class is " industrial production person's producer price index (same period last year=100) ".
8. a kind of annual power consumption prediction method based on industrial production person's producer price index according to claim 5,
It is characterized by: in this forecast year PPI prediction model of step 4, the monthly PPI value in certain year DecemberThe year
PPI value PPIi, annual PPI history value in step 2 three meaning it is consistent.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111680938A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Power flow type big data based rework and production monitoring method and system and readable medium |
| CN113052385A (en) * | 2021-03-29 | 2021-06-29 | 国网河北省电力有限公司经济技术研究院 | Method, device, equipment and storage medium for predicting power consumption in steel industry |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680938A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Power flow type big data based rework and production monitoring method and system and readable medium |
| CN113052385A (en) * | 2021-03-29 | 2021-06-29 | 国网河北省电力有限公司经济技术研究院 | Method, device, equipment and storage medium for predicting power consumption in steel industry |
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