AU2020103560A4 - Method for predicting household electricity consumption adapted to supply-side structural reform - Google Patents
Method for predicting household electricity consumption adapted to supply-side structural reform Download PDFInfo
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Abstract
The invention discloses a method for predicting household electricity consumption adapted to
supply-side structural reform, comprising: determining the historical data of household electricity
consumption and a total population, and acquiring a predicted population in a target year; predicting
per capita household electricity consumption in the target year adapted to the supply-side structural
reform according to an econometric model; and determining and outputting household electricity
consumption in the target year. The method can comprehensively and quantitatively analyze the
influence of the supply-side structural reform on economic and social development and household
electricity consumption. According to the method, the econometric model is used to obtain the per
capita household electricity consumption in the target year, and based on the analysis and prediction
of the total urban/rural population over the years and the predicted urban/rural population in the
target year, the predicted household electricity consumption in the target year is finally obtained.
Predicting household electricity consumption is conducive to rational and effective power planning,
and can promote safe, economical and reliable operation of a power system.
1/3
Si
determining the historical data of household
electricity consumption and a total population, and a
predicted population in a target year
S2
predicting per capita household electricity S2
consumption in the target year adapted to the supply
side structural reform according to an econometric
model
I S3
determining and outputting household electricity
consumption in the target year according to the
predicted population in a target year
Fig. I
Description
1/3
Si determining the historical data of household electricity consumption and a total population, and a predicted population in a target year
S2 predicting per capita household electricity S2 consumption in the target year adapted to the supply side structural reform according to an econometric model
I S3 determining and outputting household electricity consumption in the target year according to the predicted population in a target year
Fig. I
Technical Field
The invention relates to the technical field of power analysis, in particular to a method for predicting household electricity consumption adapted to supply-side structural reform.
Background Art
Electricity is an important basic resource for economic and social development, and power demand predicting technology is the basis for formulating strategic plans and policies for energy and power development.
In view of this, a prediction method that can comprehensively and quantitatively analyze the impact of supply-side structural reform on economic and social development and household electricity consumption is provided.
Summary of the Invention
The invention provides a method for predicting household electricity consumption adapted to supply-side structural reform, comprising the following steps:
determining the historical data of household electricity consumption and a total population, and acquiring a predicted population in a target year;
predicting per capita household electricity consumption in the target year adapted to the supply-side structural reform according to an econometric model; and determining and outputting household electricity consumption in the target year.
In the above method, the step of determining the historical data of household electricity consumption and a total population, and acquiring a predicted population in a target year specifically comprises:
acquiring the historical data of the household electricity consumption and the total population; acquiring the predicted population in the target year; and
calculating per capita household electricity consumption of urban residents and rural residents respectively according to the historical data of the household electricity consumption and the total population; wherein the household electricity consumption includes the household electricity consumption of both urban residents and rural residents, and the total population includes an urban population and a rural population.
In the above method, the step of predicting per capita household electricity consumption in the target year adapted to the supply-side structural reform according to an econometric model specifically comprises:
determining an influence mechanism of the supply-side structural reform on the per capita household electricity consumption; determining specific indicators reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents; establishing a regression model about the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents by using the econometric model and the specific indicators;
predicting indicator values reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents in the target year by means of trend extrapolation or other methods; and
predicting the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents in the target year according to the predicted indicator values and the regression model.
In the above method, the step of determining and outputting household electricity consumption in the target year comprises:
calculating the household electricity consumption of urban residents and the household electricity consumption of rural residents in the target year respectively according to the predicted per capita household electricity consumption of urban residents and per capita household electricity consumption of rural residents in the target year and the predicted population in the target year; and
adding the household electricity consumption of urban residents and rural residents in the target year to obtain the predicted household electricity consumption in the target year.
In the above method, the specific indicators include the consumer spending, LED indoor lighting output value and domestic sales of household air conditioners of urban residents, and the consumer spending and housing area of rural residents.
In the above method, the establishment of the regression model about the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents by using the econometric model and the specific indicators is specifically as follows:
(1) a regression model about the per capita household electricity consumption ri of urban residents
and the specific indicators is as follows:
r1 = 0.008858e 1 + (-0.540608)e 2 + 185.245exp(e 3) - 103.593
where ei, e2 and e3 are the per capita consumer spending, per capita LED indoor lighting output value and per capita domestic sales of household air conditioners of urban residents respectively, which are calculated by dividing the consumer spending, LED indoor lighting output value and domestic sales of household air conditioners of urban residents by the urban population; and
(2) a regression model about the per capita household electricity consumption r2 of rural residents and the specific indicators is as follows:
r2 = 0.023466fi + 10.2310f2 + 114.352
wheref is the per capita consumer spending of rural residents andf2 is the per capita housing area of rural residents, which are calculated by dividing the consumer spending and housing area of rural residents by the rural population.
In the above method, the prediction indicators include the per capita consumer spending Ei, per capita LED indoor lighting output value E2 and per capita domestic sales of household air conditioners E3 of urban residents, and the per capita consumer spending Fi and per capita housing area F2 of rural residents in the target year.
In the above method, the prediction of the per capita household electricity consumption Ri of urban residents and the per capita household electricity consumption R2 of rural residents in the target year is specifically as follows:
Ri=f(Ei, E2, E3)
R2=f(F1, F2).
The invention provides the method for predicting Chinese household electricity consumption adapted to the supply-side structural reform, which can comprehensively and quantitatively analyze the influence of the supply-side structural reform on economic and social development and household electricity consumption. According to the method, the econometric model is used to obtain the per capita household electricity consumption in the target year, and based on the analysis and prediction of the total urban/rural population over the years and the predicted urban/rural population in the target year, the predicted household electricity consumption in the target year is finally obtained. Predicting household electricity consumption is conducive to rational and effective power planning, and can promote safe, economical and reliable operation of a power system.
Brief Description of the Drawings
Fig. 1 is a flow chart provided by the invention;
Fig. 2 shows detailed implementation steps of step Si provided by the invention; and
Fig. 3 shows detailed implementation steps of step S2 provided by the invention.
Detailed Description of the Invention
The purpose of this patent is to provide a method for predicting household electricity consumption adapted to supply-side structural reform, which can comprehensively and quantitatively analyze the influence of the supply-side structural reform on economic and social development and household electricity consumption. According to the method, an econometric model is used to obtain the per capita household electricity consumption in the target year, and based on the analysis and prediction of the total urban/rural population over the years and the predicted urban/rural population in the target year, the predicted household electricity consumption in the target year is finally obtained. Predicting household electricity consumption is conducive to rational and effective power planning, and can promote safe, economical and reliable operation of a power system. The invention will be described in detail with reference to the specific embodiments and the accompanying drawings.
As shown in Fig. 1, the invention provides a method for predicting household electricity consumption adapted to supply-side structural reform, comprising the following steps:
Si, Determining the historical data of household electricity consumption and a total population, and a predicted population in a target year, which, as shown in Fig. 2, specifically comprises:
S11, Acquiring the historical data of household electricity consumption and the total population, wherein
household electricity consumption includes household electricity consumption wi of urban residents and household electricity consumption W2 of rural residents, and the data comes from China Electricity Council; and the total population includes urban population pi and rural populationP2, both of which come from National Bureau of Statistics, and the time frame can be the last 20 years.
S12, Acquiring the predicted population in the target year;
wherein in the present embodiment, the predicted population in the target year can be acquired by referring to the data of authoritative organizations such as the United Nations, and includes a predicted urban population value Pi and a predicted rural population value P2.
S13, Calculating per capita household electricity consumption of urban residents and per capita household electricity consumption of rural residents according to the data obtained in S11;
wherein in the present embodiment, the per capita household electricity consumption ri (kWh/person) of urban residents and the per capita household electricity consumption r2 (kWh/person) of rural residents are calculated according to the historical data of the household electricity consumption and the total population.
Pi=
2 P2 = r2
S2, Predicting per capita household electricity consumption in the target year adapted to the supply side structural reform according to an econometric model.
S21, Determining an influence mechanism of the supply-side structural reform on the per capita household electricity consumption;
wherein in the present embodiment, the impact of the supply-side structural reform on household electricity consumption can be achieved mainly by "complementing shortcomings" and "reducing costs"; as for "complementing shortcomings", due to the improvement of living standards, the per capita housing area and per capita consumer spending have increased, which has led to an increase in people's demand for household appliances such as air conditioners, thus increasing the per capita household electricity consumption of urban and rural residents; and as for "reducing costs", by promoting the awareness of energy saving and the use of energy-saving devices such as LED lamps, the per capita household electricity consumption of urban and rural residents will be reduced.
S22, Determining specific indicators reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents from authoritative data sources such as the National Bureau of Statistics, national ministries and trade associations.
Specifically,
the specific indicators affecting the per capita household electricity consumption ri of urban residents include the consumer spending, LED indoor lighting output value and domestic sales of household air conditioners of urban residents, and the per capita consumer spending ei (yuan), per capita LED indoor lighting output value e2 (yuan/person) and per capita domestic sales of household air conditioners e3 (piece/person) of urban residents can be calculated by dividing the above indicators by the urban population pi; and
the specific indicators affecting the per capita household electricity consumption of rural residents include the consumer spending and housing area of rural residents, and the per capita consumer spendingfi (yuan) and per capita housing areaf (m2) of rural residents can be calculated by dividing the above indicators by the rural populationP2.
S23, Establishing a regression model about the per capita household electricity consumption ri of urban residents and the per capita household electricity consumption r2 of rural residents by using the econometric model and the specific indicators, and conducting regression analysis on the per capita household electricity consumption ri of urban residents and the per capita household electricity consumption r of rural residents, which specifically comprises the following steps:
(1) conducting regression analysis on the per capita household electricity consumption ri of urban residents and specific indicators, wherein the specific indicators include the per capita consumer spending ei, per capita LED indoor lighting output value e2 and per capita domestic sales of household air conditioners e3 of urban residents, the regression model is:
ri=f(ei, e2, e3)
and then in the present embodiment, the regression model of the per capita household electricity consumption ri of urban residents with respect to the per capita consumer spending ei, per capita LED indoor lighting output value e2 and per capita domestic sales of household air conditioners e3 of urban residents is as follows:
r, = 0.008858e 1 + (-0.540608)e 2 + 185.245exp(e 3) - 103.593
(2) conducting regression analysis on the per capita household electricity consumption r of rural residents and specific indicators, wherein the specific indicators include the per capita consumer spendingfi and per capita housing areaf2 of rural residents in step 2, the regression model is:
r2=f(fl,f2)
and then in the present embodiment, the regression model of the per capita household electricity consumption r2 of rural residents with respect to the per capita consumer spendingfi and per capita housing areaf2 of rural residents is as follows: r2 = 0.023466fi + 10.2310f2 + 114.352
In the present embodiment, if the goodness of fit R 2 of the above two regression models is greater than 90% and can pass the F test and the T test, the above regression models are respectively taken as the optimal regression models for the per capita household electricity consumption ri of urban residents and the per capita household electricity consumption r2 of rural residents.
According to the calculation and analysis, for the regression model of the per capita household electricity consumption ri of urban residents with respect to the per capita consumer spending ei, per capita LED indoor lighting output value e2 and per capita domestic sales of household air conditioners e3 of urban residents, a coefficient of determination R2=99.65% (a coefficient of determination of ri and ei, e2 and exp(e3 ) herein), indicating that the regression model has a high goodness of fit; F=1882.17, far greater than a critical value 1.11E-24, indicating that the regression model can pass the F test; P-values of the variables ei, e2 and exp(e3 ) in the regression model are 1.42E-10, 5.19E-07 and 0.083648 respectively, which are all less than 0.1, indicating that the regression model can pass the T test; so it can be concluded that the model can be applied to a method for predicting per capita household electricity consumption of urban residents adapted to supply-side reform.
For the regression model of the per capita household electricity consumption r2 of rural residents with respect to the per capita consumer spendingfi and per capita housing areaf2 of rural residents, a coefficient of determination R2=98.25% (a coefficient of determination of r2 andfi andf2 herein), indicating that the regression model has a high goodness of fit; F=374.876, far greater than a critical value 9.74E-18, indicating that the regression model can pass the F test; P-values of the variables fi and f2 in the regression model are 0.000367and 0.000504 respectively, which are all less than 0.1, indicating that the regression model can pass the T test; so it can be concluded that the model can be applied to a method for predicting per capita household electricity consumption of rural residents adapted to supply-side structural reform.
S24, Predicting indicator values reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents in the target year by means of trend extrapolation or other methods based on economic and social development.
The prediction indicators include the per capita consumer spending Ei, per capita LED indoor lighting output value E2 and per capita domestic sales of household air conditioners E3 of urban residents, and the per capita consumer spending Fi and per capita housing area F2 of rural residents in the target year.
S25, Predicting the per capita household electricity consumption Ri of urban residents and the per capita household electricity consumption R2 of rural residents in the target year according to the predicted indicator values in S24 and the regression model about the per capita household electricity consumption of urban and rural residents in S23. Specifically,
R 1=f(Ei, E2, E3)
R2=f(F1, F2)
S3, Determining and outputting household electricity consumption in the target year according to the predicted population in a target year.
S31, Calculating the household electricity consumption Wi of urban residents and the household electricity consumption W2 of rural residents in the target year respectively. Specifically,
Wi=RiPi
W2=R2P2
S32, Adding the household electricity consumption of urban residents and rural residents in the target year to obtain the predicted household electricity consumption W in the target year, that is,
W= Wl+W2
The technical difficulty of this method lies in how to use the econometric model to conduct regression analysis on per capita household electricity consumption and relative indicators, and particularly, the selection of indicators is an important factor affecting prediction results. The prediction of household electricity consumption by this method is conducive to rational and effective power planning, and can promote safe, economical and reliable operation of a power system.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the invention, but not to limit it. Although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that the technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features can be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the invention.
Claims (8)
1. A method for predicting household electricity consumption adapted to supply-side structural reform, characterized by comprising:
determining the historical data of household electricity consumption and a total population, and acquiring a predicted population in a target year;
predicting per capita household electricity consumption in the target year adapted to the supply-side structural reform according to an econometric model; and
determining and outputting household electricity consumption in the target year.
2. The method according to claim 1, wherein the step of determining the historical data of household electricity consumption and a total population, and acquiring a predicted population in a target year specifically comprises:
acquiring the historical data of the household electricity consumption and the total population; acquiring the predicted population in the target year; and
calculating per capita household electricity consumption of urban residents and rural residents respectively according to the historical data of the household electricity consumption and the total population;
wherein the household electricity consumption includes the household electricity consumption of both urban residents and rural residents, and the total population includes an urban population and a rural population.
3. The method according to claim 1, wherein the step of predicting per capita household electricity consumption in the target year adapted to the supply-side structural reform according to an econometric model specifically comprises:
determining an influence mechanism of the supply-side structural reform on the per capita household electricity consumption; determining specific indicators reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents; establishing a regression model about the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents by using the econometric model and the specific indicators;
predicting indicator values reflecting the influence of the supply-side reform on the per capita household electricity consumption of urban and rural residents in the target year by means of trend extrapolation or other methods; and predicting the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents in the target year according to the predicted indicator values and the regression model.
4. The method according to claim 3, wherein the step of determining and outputting household electricity consumption in the target year comprises:
calculating the household electricity consumption of urban residents and the household electricity consumption of rural residents in the target year respectively according to the predicted per capita household electricity consumption of urban residents and per capita household electricity consumption of rural residents in the target year and the predicted population in the target year; and
adding the household electricity consumption of urban residents and rural residents in the target year to obtain the predicted household electricity consumption in the target year.
5. The method according to claim 3, wherein the specific indicators include the consumer spending, LED indoor lighting output value and domestic sales of household air conditioners of urban residents, and the consumer spending and housing area of rural residents.
6. The method according to claim 5, wherein the establishment of the regression model about the per capita household electricity consumption of urban residents and the per capita household electricity consumption of rural residents by using the econometric model and the specific indicators is specifically as follows:
(1) a regression model about the per capita household electricity consumption ri of urban residents and the specific indicators is as follows:
r1 = 0.008858e, + (-0.540608)e 2 + 185.245exp(e) - 103.593
where ei, e2 and e3 are the per capita consumer spending, per capita LED indoor lighting output value and per capita domestic sales of household air conditioners of urban residents respectively, which are calculated by dividing the consumer spending, LED indoor lighting output value and domestic sales of household air conditioners of urban residents by the urban population; and
(2) a regression model about the per capita household electricity consumption r2 of rural residents and the specific indicators is as follows:
r2 = 0.023466fi + 10.2310f2 + 114.352 wherefi is the per capita consumer spending of rural residents andf2 is the per capita housing area of rural residents, which are calculated by dividing the consumer spending and housing area of rural residents by the rural population.
7. The method according to claim 6, wherein the prediction indicators include the per capita consumer spending Ei, per capita LED indoor lighting output value E2 and per capita domestic sales of household air conditioners E3 of urban residents, and the per capita consumer spending Fi and per capita housing area F2 of rural residents in the target year.
8. The method according to claim 7, wherein the prediction of the per capita household electricity consumption Ri of urban residents and the per capita household electricity consumption R2 of rural residents in the target year is specifically as follows:
Ri=f(Ei, E2, E3)
R2=f(Fi, F2).
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010377809.1 | 2020-05-07 | ||
| CN202010377809.1A CN113139672A (en) | 2020-05-07 | 2020-05-07 | Resident life electricity consumption prediction method |
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| CN113609179A (en) * | 2021-06-17 | 2021-11-05 | 国网江西省电力有限公司供电服务管理中心 | Population inflow evaluation method, electronic device and storage medium |
| 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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| CN103440556B (en) * | 2013-09-04 | 2016-01-13 | 国家电网公司 | A kind of electricity demand forecasting method based on economy conduction |
| CN103679289B (en) * | 2013-12-09 | 2017-12-22 | 国家电网公司 | Methods of electric load forecasting based on multiple regression extrapolation |
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| CN109615139A (en) * | 2018-12-14 | 2019-04-12 | 西安理工大学 | A kind of long-term electricity demand forecasting method in the resident based on cultural genetic algorithm |
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-
2020
- 2020-05-07 CN CN202010377809.1A patent/CN113139672A/en active Pending
- 2020-11-19 AU AU2020103560A patent/AU2020103560A4/en not_active Ceased
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| CN113609179A (en) * | 2021-06-17 | 2021-11-05 | 国网江西省电力有限公司供电服务管理中心 | Population inflow evaluation method, electronic device and storage medium |
| 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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| CN116523687A (en) * | 2023-06-26 | 2023-08-01 | 国网能源研究院有限公司 | Multi-factor electricity consumption growth driving force decomposition method, device and storage medium |
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