CN105894112A - Method for predicting the power consumption in regional tertiary industry - Google Patents
Method for predicting the power consumption in regional tertiary industry Download PDFInfo
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- CN105894112A CN105894112A CN201610195843.0A CN201610195843A CN105894112A CN 105894112 A CN105894112 A CN 105894112A CN 201610195843 A CN201610195843 A CN 201610195843A CN 105894112 A CN105894112 A CN 105894112A
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Abstract
The invention provides a method for predicting the power consumption in regional tertiary industry. The method comprises steps of: acquiring commercial utilization area, first season average temperature, third season average temperature, fourth season average temperature, and tertiary industry power consumption data of each season in each year of a historical sample section; constructing a tertiary industry power consumption prediction model of each season; acquiring the commercial utilization area, first season average temperature, third season average temperature, fourth season average temperature of the predicted year; and obtaining the tertiary industry power consumption predicted value of each season in the predicted year by using the tertiary industry power consumption prediction model of each season. The method uses the commercial utilization area and the temperature as principal factors influencing the power consumption in regional tertiary industry, takes accounts of the particularity of the temperature factor, constructs the tertiary industry power consumption prediction models according to seasons, increases the rationality and the accuracy in tertiary industry power consumption prediction, and may provide an important reference basis for tertiary industry power consumption demand planning.
Description
Technical field
The present invention relates to electrical network power consumption requirement forecasting technical field, specifically a kind of region tertiary industry
Industry electricity demand forecasting method.
Background technology
At present, domestic scholars, to electricity consumption quantitative analysis and prediction, is mostly focused on Analyzing Total Electricity Consumption
Angle.Wherein, correlative factor typically uses the index of the reflection national economic development, it was predicted that method master
Regression analysis to be had, neural network, grey method, time series method etc..But, with
Development change and the adjusting and optimizing of the industrial structure of economy, consumption structure is also occurring
Change.Economic transition be unable to do without the tertiary industry along with the optimization of the industrial structure, industrial structure optimization
Fast-developing.Proportion in tertiary industry power consumption power consumption in the whole society also keeps rising year by year
Trend, the whether accurate of tertiary industry electricity demand forecasting can affect whole society's electricity consumption to a certain extent
The accuracy of amount prediction.
Summary of the invention
It is an object of the invention to provide a kind of region tertiary industry electricity demand forecasting method, by dividing
Season builds tertiary industry electricity demand forecasting model, provides one more for tertiary industry need for electricity
Forecasting Methodology accurately.
The technical scheme is that
A kind of region tertiary industry electricity demand forecasting method, the method comprises the following steps:
(1) historical sample interval the business usable floor area in each year, first quarter average air are obtained
Gentle first quarter tertiary industry power consumption data, build first quarter tertiary industry electricity demand forecasting
Model；
(2) historical sample interval the business usable floor area in each year, the tertiary industry second quarter are obtained
Industry power consumption data, build the tertiary industry electricity demand forecasting model second quarter；
(3) historical sample interval the business usable floor area in each year, the average air third season are obtained
The gentleness tertiary industry power consumption data third season, build the tertiary industry electricity demand forecasting third season
Model；
(4) historical sample interval the business usable floor area in each year, average air fourth quarter are obtained
Gentleness tertiary industry power consumption data fourth quarter, build tertiary industry electricity demand forecasting fourth quarter
Model；
(5) the business usable floor area of forecast year, first quarter temperature on average, the third quarter are obtained
Degree temperature on average and temperature on average data fourth quarter；
(6) utilize various quarters tertiary industry electricity demand forecasting model, obtain each season of forecast year
Degree tertiary industry electricity demand forecasting value；
(7) the various quarters tertiary industry electricity demand forecasting value of forecast year is added up, predicted
The tertiary industry electricity demand forecasting value in year.
Described region tertiary industry electricity demand forecasting method, in step (1), the described first season
Degree tertiary industry electricity demand forecasting model is:
Yt1=A1+B1*St+C1*Xt1
Wherein, Yt1Represent the first quarter tertiary industry power consumption in t year, StRepresent t year
Business usable floor area, Xt1Represent the first quarter temperature on average in t year, A1、B1、C1For constant,
It is by interval for the historical sample business usable floor area in each year, first quarter temperature on average and the first season
Degree tertiary industry power consumption data substitute in described first quarter tertiary industry electricity demand forecasting model
Matching obtains；
In step (2), described second quarter, tertiary industry electricity demand forecasting model was:
Yt2=A2+B2*St
Wherein, Yt2Represent the tertiary industry power consumption second quarter in t year, StRepresent t year
Business usable floor area, A2、B2For constant, it is that the business in interval for historical sample each year is used face
Amass and the tertiary industry power consumption data substitution second quarter tertiary industry power consumption described second quarter
In forecast model, matching obtains；
In step (3), described third season, tertiary industry electricity demand forecasting model was:
Yt3=A3+B3*St+C3*Xt3
Wherein, Yt3Represent the tertiary industry power consumption third season in t year, StRepresent t year
Business usable floor area, Xt3Represent the temperature on average third season in t year, A3、B3、C3For often
Number, is by interval for the historical sample business usable floor area in each year, the temperature on average third season and the
The third quater tertiary industry power consumption data substitution tertiary industry electricity demand forecasting mould described third season
In type, matching obtains；
In step (4), described fourth quarter, tertiary industry electricity demand forecasting model was:
Yt4=A4+B4*St+C4*Xt4
Wherein, Yt4Represent tertiary industry power consumption fourth quarter in t year, StRepresent t year
Business usable floor area, Xt4Represent temperature on average fourth quarter in t year, A4、B4、C4For often
Number, is by interval for the historical sample business usable floor area in each year, temperature on average fourth quarter and the
Fourth quater tertiary industry power consumption data substitution tertiary industry electricity demand forecasting mould described fourth quarter
In type, matching obtains.
Described region tertiary industry electricity demand forecasting method, described historical sample interval each year
Business usable floor area, first quarter temperature on average, the temperature on average third season, fourth quarter are average
Temperature, first quarter tertiary industry power consumption, the tertiary industry power consumption second quarter, the third season
Tertiary industry power consumption and tertiary industry power consumption data fourth quarter are based on authoritative institution and issue
Statistical data obtain.
Described region tertiary industry electricity demand forecasting method, the business of described forecast year uses face
Long-pending, first quarter temperature on average, the temperature on average third season and fourth quarter temperature on average data equal
The statistical data issued based on authoritative institution obtains.
Described region tertiary industry electricity demand forecasting method, the business usable floor area in described t year
The commercial distribution area sum using t-10～t-1 year is calculated；The first of described t year
1～the temperature on average in February in temperature on average employing t year in season；The third quarter in described t year
Degree temperature on average uses the temperature on average in July in t year；The fourth quarter in described t year is average
Temperature uses the December temperature on average in t year.
The invention have the benefit that
As shown from the above technical solution, the present invention using business usable floor area and temperature as influence area
The principal element of tertiary industry power consumption, and in view of the particularity of Temperature Factor, point season builds
Tertiary industry electricity demand forecasting model, improves the reasonability of tertiary industry electricity demand forecasting with accurate
Property, it is possible to provide important reference for the planning of tertiary industry need for electricity.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention；
Tu2Shi Anhui Province value-added of the tertiary industry speedup and tertiary industry power consumption speedup comparison diagram；
Tu3Shi Anhui Province business usable floor area speedup and tertiary industry power consumption speedup comparison diagram；
Tu4Shi Anhui Province 6～August temperature on average with tertiary industry power consumption speedup comparison diagram.
Detailed description of the invention
Below in conjunction with the accompanying drawings, as a example by Anhui Province's tertiary industry electricity demand forecasting of 2015, one is entered
The step explanation present invention.
As it is shown in figure 1, a kind of region tertiary industry electricity demand forecasting method, comprise the following steps:
S1, qualitative analysis affect the factor of tertiary industry power consumption；
The tertiary industry mainly includes transportation, storage and postal industry, wholesale and retail business, lodging
With catering trade, financial circles, real estate and other, every profession and trade production and management enviroment summer is for fall
The demand of temperature (winter is for heating up) is rigid demand, and the impact on power consumption is more notable, i.e.
The fluctuation of meteorological factor is one of major reason affecting the fluctuation of Third Industry Economy electricity consumption relation.
Contrast as in figure 2 it is shown, observe value-added of the tertiary industry speedup with tertiary industry power consumption speedup
Relation finds, Anhui Province's value-added of the tertiary industry speedup maintains about 10%, tertiary industry electricity consumption
Amount speedup entirety tendency with itself and not quite identical, tertiary industry power consumption speedup fluctuate situation more
Acutely.As it is shown on figure 3, observe tertiary industry power consumption speedup and business usable floor area (previous decade
Commercial distribution area sum) discovery of speedup relativity, except indivedual years especially, both tendencies are consistent
Property is more stable, causes indivedual time both sides relation instability reason to be not excluded for meteorological factor.2008
Year is affected by global economic crisis, and commercial distribution area occurs in that negative growth phenomenon, thus causes
Gliding by a relatively large margin occurs in business usable floor area speedup in 2009, but 2009 6～put down August
All temperature was apparently higher than 2008, and this has occurred as soon as Anhui Province's value-added of the tertiary industry in 2009 and has increased
When speed, business usable floor area speedup glide, tertiary industry power consumption speedup still keeps rapid growth
Phenomenon.As shown in Figure 4, tertiary industry power consumption speedup and 6～temperature on average in August is observed
Relativity finds, tertiary industry power consumption speedup is obvious by meteorological factor influence.Therefore, the 3rd
Industry power consumption is relevant with business usable floor area and temperature.
S2, according to four seasons climate characteristic, point season builds tertiary industry electricity demand forecasting model；
In the present embodiment, using Anhui Province's data of the most each same season, Data Source is in Anhui power saving
Power economic technology academy.With the various quarters in 2005～2014 for the sample phase, 2015 and each season
Degree is time span of forecast, and the related data of sample phase is as shown in table 1:
Table 1
Anhui Province is located in China's north-south climate intermediate zone, and four seasons weather is clearly demarcated, 4 annual～June
Part temperature is suitable, typically need not use air-conditioning to carry out adjusting ambient temperature.And summer especially 7,8
Month hot climate, use air-conditioning temperature-reducing, air-conditioning usage amount is more；1～February and December
Weather severe cold, it is also possible to use warming by air conditioner.Therefore, first and third, the fourth quater tertiary industry is used
Selecting business usable floor area and temperature as explaining the factor during power quantity predicting, the second quarter is then only simultaneously
Use business usable floor area as explaining the factor.
Anhui Province first, second, third and fourth season tertiary industry electricity demand forecasting model is specific as follows:
log(Yt1)=4.1694+1.0842*log (St)-0.0149*Xt1
log(Yt2)=4.7953+0.9781*log (St)
Yt3=-591941.3064+137.3667*St+20941.4570*Xt3
Yt4=29687.6644+109.1289*St-4414.49*Xt4
Wherein, Yt1、Yt2、Yt3、Yt4Be respectively the first quarter in t year, the second quarter, the
The third quater, fourth quarter Anhui Province's tertiary industry power consumption；StAnhui Province's business for t year makes
With area, the commercial distribution area sum using t-10 to t-1 year is calculated；Xt1For
Anhui Province's first quarter temperature on average (employing 1～temperature on average in February) in t year, Xt3For t
The Anhui Province's temperature on average third season (using temperature on average in July) in year, Xt4For t year
Anhui Province's temperature on average fourth quarter (use December temperature on average).
S3, according to historical law, the business usable floor area of external given forecast year, the first quarter
Temperature on average, the temperature on average third season and the predictive value of temperature on average fourth quarter, it was predicted that each season
The tertiary industry power consumption of degree；
According to above-mentioned first, second, third and fourth season tertiary industry electricity demand forecasting model, outward
The amount of changing is business usable floor area, first quarter mean temperature, the mean temperature third season and the 4th
Season mean temperature.Therefore, in order to predict following Anhui Province tertiary industry power consumption, need given
The predictive value of following these exogenous variables of phase.Anhui Province's business usable floor area in 2013～2014 same
It is respectively 14.20%, 14.25%, Anhui Province's business usable floor area speedup prediction in 2015 than speedup
Value takes the meansigma methods 14.22% of business usable floor area speedup on year-on-year basis in 2013 and 2014.2005～
The meansigma methods of Anhui Province's first quarter temperature on average of 2014 is 3.62 DEG C, it is contemplated that 2005
First quarter temperature on average with 2008 is on the low side, therefore takes the first quarter of 2009～2014
The 3.85 DEG C of predictions as Anhui Province's first quarter temperature on average in 2015 of the meansigma methods of temperature on average
Value.2005～2014 the third season temperature on average meansigma methods be 28.46 DEG C, it is contemplated that
Higher or on the low side to the temperature on average third season of 2012,2013 and 2014, therefore take
2005～2011 the third season temperature on average meansigma methods 28.16 DEG C (part July i.e. throughout the year
Temperature on average) as Anhui Province 2015 the third season temperature on average predictive value.2005～
2014 fourth quarter temperature on average meansigma methods be 4.48 DEG C, in this, as Anhui Province 2015
Year fourth quarter temperature on average predictive value.Predictive value according to these factors and various quarters
Three industry electricity demand forecasting models, obtain four season in 2015 tertiary industry electricity demand forecasting value.
S4, four season in 2015 tertiary industry electricity demand forecasting value is added up, i.e. obtain 2015
The annual prediction value of year tertiary industry power consumption was 213.35 hundred million kilowatt hours, with tertiary industry in 2015
The actual value 213.77 of industry power consumption is compared, and error is about 0.196%.
The above embodiment is only to be described the preferred embodiment of the present invention, not
The scope of the present invention is defined, on the premise of designing spirit without departing from the present invention, this area
Various deformation that technical scheme is made by those of ordinary skill and improvement, all should fall into this
In the protection domain that claims of invention determine.
Claims (5)
1. a region tertiary industry electricity demand forecasting method, it is characterised in that the method includes
Following steps:
(1) historical sample interval the business usable floor area in each year, first quarter average air are obtained
Gentle first quarter tertiary industry power consumption data, build first quarter tertiary industry electricity demand forecasting
Model；
(2) historical sample interval the business usable floor area in each year, the tertiary industry second quarter are obtained
Industry power consumption data, build the tertiary industry electricity demand forecasting model second quarter；
(3) historical sample interval the business usable floor area in each year, the average air third season are obtained
The gentleness tertiary industry power consumption data third season, build the tertiary industry electricity demand forecasting third season
Model；
(4) historical sample interval the business usable floor area in each year, average air fourth quarter are obtained
Gentleness tertiary industry power consumption data fourth quarter, build tertiary industry electricity demand forecasting fourth quarter
Model；
(5) the business usable floor area of forecast year, first quarter temperature on average, the third quarter are obtained
Degree temperature on average and temperature on average data fourth quarter；
(6) utilize various quarters tertiary industry electricity demand forecasting model, obtain each season of forecast year
Degree tertiary industry electricity demand forecasting value；
(7) the various quarters tertiary industry electricity demand forecasting value of forecast year is added up, predicted
The tertiary industry electricity demand forecasting value in year.
Region the most according to claim 1 tertiary industry electricity demand forecasting method, its feature
Being, in step (1), described first quarter tertiary industry electricity demand forecasting model is:
Yt1=A1+B1*St+C1*Xt1
Wherein, Yt1Represent the first quarter tertiary industry power consumption in t year, StRepresent t year
Business usable floor area, Xt1Represent the first quarter temperature on average in t year, A1、B1、C1For constant,
It is by interval for the historical sample business usable floor area in each year, first quarter temperature on average and the first season
Degree tertiary industry power consumption data substitute in described first quarter tertiary industry electricity demand forecasting model
Matching obtains；
In step (2), described second quarter, tertiary industry electricity demand forecasting model was:
Yt2=A2+B2*St
Wherein, Yt2Represent the tertiary industry power consumption second quarter in t year, StRepresent t year
Business usable floor area, A2、B2For constant, it is that the business in interval for historical sample each year is used face
Amass and the tertiary industry power consumption data substitution second quarter tertiary industry power consumption described second quarter
In forecast model, matching obtains；
In step (3), described third season, tertiary industry electricity demand forecasting model was:
Yt3=Ag+B3*St+C3*Xt3
Wherein, Yt3Represent the tertiary industry power consumption third season in t year, StRepresent t year
Business usable floor area, Xt3Represent the temperature on average third season in t year, A3、B3、C3For often
Number, is by interval for the historical sample business usable floor area in each year, the temperature on average third season and the
The third quater tertiary industry power consumption data substitution tertiary industry electricity demand forecasting mould described third season
In type, matching obtains；
In step (4), described fourth quarter, tertiary industry electricity demand forecasting model was:
Yt4=A4+B4*St+C4*Xt4
Wherein, Yt4Represent tertiary industry power consumption fourth quarter in t year, StRepresent t year
Business usable floor area, Xt4Represent temperature on average fourth quarter in t year, A4、B4、C4For often
Number, is by interval for the historical sample business usable floor area in each year, temperature on average fourth quarter and the
Fourth quater tertiary industry power consumption data substitution tertiary industry electricity demand forecasting mould described fourth quarter
In type, matching obtains.
Region the most according to claim 1 tertiary industry electricity demand forecasting method, its feature
Be: the interval business usable floor area in each year of described historical sample, first quarter temperature on average,
The third season temperature on average, temperature on average fourth quarter, first quarter tertiary industry power consumption,
Tertiary industry power consumption, the tertiary industry power consumption third season and tertiary industry fourth quarter for the second quarter
Power consumption data are based on the statistical data acquisition that authoritative institution issues.
Region the most according to claim 1 tertiary industry electricity demand forecasting method, its feature
It is: the business usable floor area of described forecast year, first quarter temperature on average, the third season put down
All temperature and temperature on average data fourth quarter are based on the statistical data acquisition that authoritative institution issues.
Region the most according to claim 2 tertiary industry electricity demand forecasting method, its feature
It is: the business usable floor area in described t year uses the commercial distribution area in t-10～t-1 year
Sum is calculated；The first quarter temperature on average in described t year uses 1～the February in t year
Part temperature on average；The temperature on average third season in described t year uses July annual for t average
Temperature；Temperature on average fourth quarter in described t year uses the December temperature on average in t year.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116526474A (en) * | 2023-06-08 | 2023-08-01 | 国网安徽省电力有限公司淮北供电公司 | Regional power grid electricity load prediction method and system |
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2016
- 2016-03-29 CN CN201610195843.0A patent/CN105894112A/en active Pending
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| CN104504479A (en) * | 2015-01-05 | 2015-04-08 | 国家电网公司 | Temperature/ economic growth factor considered monthly total electricity consumption predication method |
| CN105069536A (en) * | 2015-08-19 | 2015-11-18 | 国网安徽省电力公司经济技术研究院 | Electricity demand predication method based on temperature and economic growth |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116526474A (en) * | 2023-06-08 | 2023-08-01 | 国网安徽省电力有限公司淮北供电公司 | Regional power grid electricity load prediction method and system |
| CN116526474B (en) * | 2023-06-08 | 2023-10-20 | 国网安徽省电力有限公司淮北供电公司 | Regional power grid electricity load prediction method and system |
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