A method for predicting power demand

Abstract

The present invention discloses a method for predicting medium- and long-term electricity demand, which includes: S1: obtaining economic and electricity historical data necessary for conducting research, wherein the economic data is from the China Statistical Yearbook, and the electricity data is from the China Electricity Council; S2: based on the acquired GDP historical data, using the improved Solow growth model to predict the total GDP; S3: according to the acquired GDP and electricity consumption historical data, calculating the historical value of unit GDP electricity consumption, and predicting the future unit GDP electricity consumption value by exponential smoothing method; S4: predicting the future total electricity consumption according to the GDP and unit GDP electricity consumption prediction results; S5: calculating the historical departmental electricity consumption structure according to the electricity historical data, calculating the future departmental electricity consumption structure by trend extrapolation method, and finally predicting the departmental electricity consumption according to the predicted total electricity consumption. The technical solution provided by the present invention can predict the growth trend of my country's medium- and long-term electricity demand, and provide a reference for medium- and long-term electricity development and planning.

Description

Technical Field

[0001] The present invention relates to the field of electric power engineering technology, and more specifically to a method for predicting medium- and long-term electric power demand. Background Art

[0002] Electricity is an important basic resource supporting economic and social development. Electricity demand forecasting technology is the basis for formulating energy and power development strategic plans and policies.

[0003] How to quantify and analyze the impact of electricity demand is of great significance, but there is still no relevant method model proposed. Therefore, a medium- and long-term electricity demand forecasting method is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a medium- and long-term electricity demand forecasting method, which can comprehensively and quantitatively analyze the impact of economic and social development and electricity demand, while comprehensively considering changes in factors such as scientific and technological progress and environmental protection constraints, and forecasting medium- and long-term electricity demand, which can provide important reference for energy and power development planning.

[0005] The technical solution for achieving the purpose of the present invention is as follows:

[0006] The present invention provides a method for predicting power demand, wherein the method comprises:

[0007] S1, obtain historical GDP data related to electricity and economy for electricity;

[0008] S2, based on the historical GDP data obtained, determine the impact of electricity demand on the macroeconomy and use the improved Solow growth model to predict the total GDP;

[0009] S3, based on the historical data of GDP and electricity consumption, calculate the historical value of unit GDP electricity consumption, and predict the future GDP electricity consumption value of the unit through exponential smoothing method;

[0010] S4, predict the total future electricity consumption based on GDP and the unit's GDP electricity consumption forecast results;

[0011] S5, calculate the historical power consumption structure of each department based on the historical power data, calculate the future power consumption structure of each department using the trend extrapolation method, and finally predict the power consumption of each department based on the predicted total power consumption.

[0012] Among them, S1: Acquisition of power economic and power historical data includes:

[0013] S1-1: Obtain historical data on the power economy, including total GDP, actual capital stock, total population and gender ratio, fertility rate, mortality rate, employed population, and employment rate; historical economic data comes from the China Statistical Yearbook of the National Bureau of Statistics;

[0014] S1-2: Obtain the historical electricity data necessary for conducting the research, including total electricity consumption and electricity consumption by sector. The historical electricity data comes from the statistical data of the China Electricity Council.

[0015] Among them, S2: Based on the historical GDP data obtained, determine the impact of the macroeconomy and use the improved Solow growth model to predict the total GDP. The specific steps are as follows:

[0016] S2-1: Based on GDP (Y), real capital stock (K), and employed population (L) data, calculate the change in total factor productivity as follows;

[0017]

[0018] Among them, ΔA / A is the rate of change of total factor productivity, ΔY / Y refers to the GDP growth rate, ΔK / K refers to the rate of change of real capital stock, ΔL / L refers to the rate of change of employed population, and α and β are constant terms;

[0019] S2-2: Based on the changes in fertility and mortality rates, a population growth prediction model is constructed as shown below to achieve the prediction of the total population and the number of employed people;

[0020]

[0021] Among them, x is the age group, that is, 0 to 99 years old is divided into 20 age groups; Q t+1 represents the total population in year t+1, P 1ti represents the number of females aged i in year t, Bx is the fertility rate of women in the x-age group, and D 1ti P is the mortality rate of female population aged i in year t, 2ti represents the number of male population aged i in year t, D 2ti represents the mortality rate of male population aged i in year t;

[0022] Assuming that the employment rate remains constant, the number of employed people can be calculated;

[0023]

[0024] Among them, q t is the number of employed people, Q t is the total population, for employment rate;

[0025] S2-3: Based on the historical rate of change of total factor productivity, taking the change of total factor productivity in the United States as a reference value, select the benchmark level of the rate of change, and use the Solow growth model to predict the future GDP growth Y t ′、Y t+1 Calculate as follows;

[0026] Y t ′=A t ′+α*K t ′+β*L t '

[0027] Y t+1 =Y t *Y t '

[0028] Among them, Y t ′、A t ′, K t ′, L t ′ are the growth rates of GDP, total factor productivity, capital stock, and labor force in year t, respectively. t+1 is the GDP in year t+1.

[0029] Among them, S3: Based on the historical data of GDP and electricity consumption, the historical value of GDP electricity consumption of this unit is calculated, and the future GDP electricity consumption value of this unit is predicted by exponential smoothing method:

[0030] S3-1: Based on the historical data of GDP and electricity consumption, calculate the historical value of GDP electricity consumption U of this unit according to the following formula;

[0031]

[0032] Among them, U is the historical value of electricity consumption per unit GDP, E is the total electricity consumption, and Y is GDP;

[0033] S3-2: Forecast the future electricity consumption per unit GDP by exponential smoothing method;

[0034] U t ′=(σ*U t-1 *(1-σ)*U′ it-1 )*ρ

[0035] Among them, U t ′ is the predicted value of unit GDP electricity consumption in year t using the first exponential smoothing method, U t-1 is the electricity consumption per unit GDP in year t-1, σ is the smoothing constant (0<σ<1, determined according to the number of historical samples), and ρ is the adjustment coefficient.

[0036] Among them, S4: Based on the forecast results of GDP and unit GDP electricity consumption, the total electricity consumption is predicted as follows;

[0037] E t =U t ′*Y t

[0038] Among them, E t is the predicted total electricity consumption in year t, U t ′ is the predicted value of electricity consumption per unit GDP in year t, and Y is the predicted value of GDP in year t.

[0039] Among them, S5: calculate the historical power consumption structure of each department according to the historical power data, calculate the future power consumption structure of each department by using the trend extrapolation method, and finally predict the power consumption of each department according to the predicted total power consumption. The specific steps are as follows;

[0040] S5-1: Based on the acquired historical electricity data, calculate the electricity consumption structure of each department according to the following formula;

[0041]

[0042] Among them, θ i is the proportion of electricity consumption of sector i to total electricity consumption; i includes the primary industry, secondary industry, tertiary industry, and residents’ life sectors, e i is the electricity consumption of the i sector, and E is the total electricity consumption;

[0043] S5-2: Based on the changes in the proportion of electricity consumption of various sectors in the total electricity consumption in the past three years, the trend extrapolation method is used to predict the proportion of electricity consumption of the primary industry, the tertiary industry, and residents' daily life in the total electricity consumption in the future according to the following formula:

[0044] θ it =δ*mθ it-1 +nθ it-2 +oθ it-3

[0045] Among them, θ it is the predicted value of the proportion of electricity consumption of sector i in total electricity consumption in year t, δ is the optimization coefficient of the conversion of new and old kinetic energy, and m, n, and o are constants calculated based on historical data;

[0046] S5-3: Calculate the proportion of electricity consumption in the secondary industry to total electricity consumption as follows:

[0047] θ 第二产业 =1-θ 第一产业 -θ 第三产业 -θ 居民生活

[0048] S5-4: Calculate the electricity consumption of each department according to the following formula:

[0049] e it =θ it *E t

[0050] Among them, e it is the electricity consumption of industry i in year t, θ it is the proportion of electricity consumption of sector i in the total electricity consumption in year t, E t is the total electricity consumption in year t. Compared with the closest prior art, the technical solution provided by the present invention has the following advantages:

[0051] Based on the technical solution of the present invention, a medium- and long-term electricity demand forecasting method is constructed. It can quantitatively analyze macroeconomic growth, energy utilization efficiency, and the conversion of new and old growth momentum, etc., and realize the forecast of medium- and long-term electricity demand for the whole society and sub-sectors in a top-down manner, which can provide important decision-making basis for the formulation of energy and power development planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a logical architecture diagram of the prediction model of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0054] Embodiment 1:

[0055] See also Figure 1 , a method for predicting medium- and long-term electricity demand, comprising the following steps:

[0056] S1: Obtain the economic and electricity historical data necessary for the study, including:

[0057] S1-1: Obtain the economic historical data necessary for conducting research, including total GDP, actual capital stock, total population and gender ratio, fertility rate, mortality rate, employed population, and employment rate. The economic historical data comes from the China Statistical Yearbook of the National Bureau of Statistics;

[0058] S1-2: Obtain the historical electricity data necessary for conducting the research, including total electricity consumption and electricity consumption by sector. The historical electricity data comes from the statistical data of the China Electricity Council.

[0059] S2: Use the improved Solow growth model to predict GDP:

[0060] S2-1: According to the total factor productivity calculation formula, it is calculated that my country's annual average TFP change rate was about 2 percentage points between 1999 and 2014. During the same period, the United States, as a technological innovation-driven power, also had an annual average TFP growth rate of about 2 percentage points. Therefore, it is assumed that TFP grows by 2 percentage points annually.

[0061] S2-2: Assuming the employment rate remains unchanged, and based on the population forecast formula, it can be calculated that the labor force decreases by 0.47% each year.

[0062] S2-3: Select the historical data of GDP from 2008 to 2017, and use the GDP forecast formula to calculate the GDP growth;

[0063]

[0064] S3: Based on the historical data of GDP and electricity consumption, the historical value of electricity consumption per unit of GDP is calculated, and the future value of electricity consumption per unit of GDP is predicted by exponential smoothing method:

[0065] S3-1: Calculate the historical value of unit GDP electricity consumption (U, in kWh / yuan) based on the historical data of GDP and electricity consumption;

[0066]

[0067]

[0068]

[0069] S3-2: Use exponential smoothing method to predict future unit GDP electricity consumption:

[0070] When the smoothing constant σ=0.8 is selected, the smoothed values ​​of electricity consumption per unit GDP calculated from 2009 to 2018 are as follows. The error rate between the smoothed value and the historical actual electricity consumption per unit GDP is between -1.2% and 0.8%, and the error rate is extremely small:

[0071] Similarly, the smoothing constant σ=0.8 is selected, and the annual adjustment coefficient (ρ) for 2019-2030 is taken as follows:

[0072]

[0073] The electricity consumption per unit of GDP from 2019 to 2030 is calculated as follows:

[0074]

[0075] S4: Based on the forecast results of future GDP and electricity consumption per unit of GDP, forecast the total electricity consumption between 2019 and 2030 (in billion kWh);

[0076]

[0077] S5: Calculate the historical power consumption structure of each department based on the historical power data, calculate the future power consumption structure of each department using the trend extrapolation method, and finally predict the power consumption of each department based on the predicted total power consumption. The specific steps are as follows:

[0078] S5-1: Based on the acquired historical electricity data, calculate the electricity consumption structure by sector, and the results are listed in the following table;

[0079]

[0080] S5-2: Based on the changes in the proportion of electricity consumption of each department in the total electricity consumption in the previous three years, using the trend extrapolation method, establish a three-variable linear regression equation to predict the proportion of electricity consumption of the primary industry, the tertiary industry, and residents' life in the total electricity consumption in the future;

[0081] For the primary industry: θ it =δ i *0.5141*θ it-1 +0.4161*θ it-2 +0.249*θ it-3

[0082] For the tertiary industry: θ it =δ i *0.302*θ it-1 +0.5686*θ it-2 +0.1864*θ it-3

[0083] For residents’ life: it =δ i *0.2163*θ it-1 +0.6809*θ it-2 +0.1448*θ it-3

[0084] For the secondary industry: θ 第二产业 =1-θ 第一产业 -θ 第三产业 -θ 居民生活

[0085] Taking into account the transformation of new and old kinetic energy, it is assumed that the optimization coefficient of each department's proportion is: 2019-2020, 2021-2025, 2026-2030 primary industry δ第一产业 They are 0.85, 0.75 and 0.70 respectively, and the optimization coefficients of other departments are 1.00 respectively.

[0086] The calculated electricity consumption structure by sector between 2019 and 2030 is listed in the following table:

[0087]

[0088] S5-4: The calculation results of electricity consumption of each department are listed in the following table:

[0089]

Claims

1. A method for predicting power demand, characterized in that: The method comprises: S1, obtain historical GDP data related to electricity and economy for electricity; S2, based on the historical GDP data obtained, determine the impact of electricity demand on the macroeconomy and use the improved Solow growth model to predict the total GDP; S2: Based on the historical GDP data obtained, determine the impact of the macroeconomy and use the improved Solow growth model to predict the total GDP. The specific steps are as follows: S2-1: Based on GDP (Y), real capital stock (K), and employed population (L) data, calculate the change in total factor productivity as follows; Among them, ΔA / A is the rate of change of total factor productivity, ΔY / Y refers to the GDP growth rate, ΔK / K refers to the rate of change of real capital stock, ΔL / L refers to the rate of change of employed population, and α and β are constant terms; S2-2: Based on the changes in fertility and mortality rates, a population growth prediction model is constructed as shown below to achieve the prediction of the total population and the number of employed people; Among them, x is the age group, that is, 0 to 99 years old is divided into 20 age groups; Q t+1 represents the total population in year t+1, P1 ti represents the number of females aged i in year t, Bx is the fertility rate of women in the x-age group, and D 1ti P2 is the mortality rate of female population aged i in year t, ti represents the number of male population aged i in year t, D 2ti represents the mortality rate of male population aged i in year t; Assuming that the employment rate remains constant, the number of employed people can be calculated; Among them, q t is the number of employed people, Q t is the total population, for employment rate; S2-3: Based on the historical rate of change of total factor productivity, taking the change of total factor productivity in the United States as a reference value, select the benchmark level of the rate of change, and use the Solow growth model to predict the future GDP growth Y t , Y t+1 Calculate according to the following formula; Y t ′=A t ′+α*K t ′+β*L t ′ AND t+1 =And t *AND t ′ Among them, Y t ′、A t ′, K t ′, L t ′ are the growth rates of GDP, total factor productivity, capital stock, and labor force in year t, respectively. t+1 is the GDP in the year t+1; S3, based on the historical data of GDP and electricity consumption, calculate the historical value of unit GDP electricity consumption, and predict the future GDP electricity consumption value of this unit through exponential smoothing method; S3: Based on the historical data of GDP and electricity consumption, calculate the historical value of GDP electricity consumption of this unit, and predict the future GDP electricity consumption value of this unit through exponential smoothing method: S3-1: Based on the historical data of GDP and electricity consumption, calculate the historical value of GDP electricity consumption U of this unit according to the following formula; Among them, U is the historical value of electricity consumption per unit GDP, E is the total electricity consumption, and Y is GDP; S3-2: Forecast the future electricity consumption per unit GDP by exponential smoothing method; U t ′=(σ*U t-1 *(1-σ)*U′ it-1 )*r Among them, U t ′ is the predicted value of unit GDP electricity consumption in year t using the first exponential smoothing method, U t-1 is the electricity consumption per unit GDP in year t-1, σ is the smoothing constant (0<σ<1, determined according to the number of historical samples), and ρ is the adjustment coefficient; S4, predict the total future electricity consumption based on GDP and the unit's GDP electricity consumption forecast results; S5, calculate the historical power consumption structure of each department based on the historical power data, calculate the future power consumption structure of each department using the trend extrapolation method, and finally predict the power consumption of each department based on the predicted total power consumption.

2. A method for predicting power demand as claimed in claim 1, characterized in that: The following steps are involved: S1: Obtaining power economic and power historical data including: S1-1: Obtain historical data on the power economy, including total GDP, actual capital stock, total population and gender ratio, fertility rate, mortality rate, employed population, and employment rate; historical economic data comes from the China Statistical Yearbook of the National Bureau of Statistics; S1-2: Obtain the historical electricity data necessary for conducting the research, including total electricity consumption and electricity consumption by sector. The historical electricity data comes from the statistical data of the China Electricity Council.

3. A method for predicting power demand as claimed in claim 1, characterized in that: S4: Based on the forecast results of GDP and electricity consumption per unit of GDP, the total electricity consumption is predicted as follows; Yes t =You t ′*Y t Among them, E t is the predicted total electricity consumption in year t, U t ′ is the predicted value of electricity consumption per unit GDP in year t, and Y is the predicted value of GDP in year t.

4. A method for predicting power demand as claimed in claim 1, characterized in that: S5: Calculate the historical power consumption structure of each department based on the historical power data, calculate the future power consumption structure of each department using the trend extrapolation method, and finally predict the power consumption of each department based on the predicted total power consumption. The specific steps are as follows; S5-1: Based on the acquired historical electricity data, calculate the electricity consumption structure of each department according to the following formula; Among them, θ i is the proportion of electricity consumption of sector i to total electricity consumption; i includes the primary industry, secondary industry, tertiary industry, and residents’ life sectors, e i is the electricity consumption of department i, and E is the total electricity consumption; S5-2: Based on the changes in the proportion of electricity consumption of various sectors in the total electricity consumption in the past three years, the trend extrapolation method is used to predict the proportion of electricity consumption of the primary industry, the tertiary industry, and residents' daily life in the total electricity consumption in the future according to the following formula: i it =δ*mθ it-1 +nθ it-2 +oθ it-3 Among them, θ it is the predicted value of the proportion of electricity consumption of sector i in total electricity consumption in year t, δ is the optimization coefficient of the conversion of new and old kinetic energy, and m, n, and o are constants calculated based on historical data; S5-3: Calculate the proportion of electricity consumption in the secondary industry to total electricity consumption as follows: i 第二产业 =1-θ 第一产业 -θ 第三产业 -θ 居民生活 S5-4: Calculate the electricity consumption of each department according to the following formula: And it =θ it *AND t Among them, e it is the electricity consumption of industry i in year t, θ it is the proportion of electricity consumption of sector i in the total electricity consumption in year t, E t is the total electricity consumption in year t.

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