CN113642778A - Power demand prediction framework considering multiple dimensions and multiple factors - Google Patents

Power demand prediction framework considering multiple dimensions and multiple factors Download PDF

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CN113642778A
CN113642778A CN202110827125.1A CN202110827125A CN113642778A CN 113642778 A CN113642778 A CN 113642778A CN 202110827125 A CN202110827125 A CN 202110827125A CN 113642778 A CN113642778 A CN 113642778A
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薛万磊
鉴庆之
王鹏
赵昕
陈博
刘知凡
李晨辉
史英
李校莹
徐楠
杨雍琦
牛华忠
王振坤
孔德秋
胡桂彬
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Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a multi-dimensional multi-factor power demand prediction framework. The framework determines factors in the aspects of macroscopicity, mesoscopic view, industry, special event impact and the like which influence the power demand growth through analysis, factor analysis is carried out based on methods such as ADF (automatic document surface), regression analysis and the like, dimensionality and factor types which influence the power demand prediction are determined, medium-long term power demand data prediction is carried out through a GRE (generalized green analytical model) and a GMM (generalized moment estimation) model, and finally the predicted data is checked according to night light big data and a neural network algorithm, so that the economic and power consumption trends of regional population, all regional cities and industrial development are judged. The invention effectively constructs a set of complete, concrete and scientific electric power prediction framework considering multi-dimensional and multi-factor, and provides a better system framework theory and practical operation method for improving the electric power prediction precision.

Description

Power demand prediction framework considering multiple dimensions and multiple factors
The technical field is as follows:
the invention belongs to the field of power demand analysis and prediction, and particularly relates to a power demand prediction system framework based on multiple dimensions and multiple factors.
Background art:
the power demand analysis and prediction is an important daily work of each level of scheduling operation departments and power utilization service departments of the power system, and is a main basis for making a power generation plan and a power transmission scheme. The load analysis and prediction technology level is improved, the planned power utilization management is facilitated, the power grid operation mode and the unit maintenance plan are reasonably arranged, the coal saving and oil saving are facilitated, the power generation cost is reduced, the reasonable power supply construction plan is made, and the economic benefit and the social benefit of the power system are improved.
However, research on the action effect and the action mechanism of the electric power and the electric quantity, which are influenced by factors such as economic transformation, important industry development, regional pattern and main body function positioning, new and old kinetic energy conversion, new capital construction, novel urbanization, new rural construction, comprehensive energy and energy management, electric energy substitution, diversified load, air temperature, climate, air conditioning load, new coronary pneumonia epidemic situation and the like, is not specific and complete, and a unified prediction system framework is not provided. Therefore, it is necessary to provide a framework for load prediction based on multiple dimensions and multiple factors, so as to improve the accuracy of regional power demand prediction.
The invention content is as follows:
the invention aims to design a power demand forecasting framework based on multiple dimensions and multiple factors to solve the problems of insufficient consideration of influence factors and lack of a uniform forecasting framework in a power demand forecasting process.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power demand forecasting framework considering multiple dimensions and multiple factors mainly comprises: (1) defining power demand prediction indexes including but not limited to economic and power consumption quantitative prediction and power consumption structure quantitative prediction under the time measure; (2) the dimensionality and factor induction influencing the power demand prediction comprises macroscopic influence factor analysis, mesoscopic influence factor analysis, industry transformation influence factor analysis and special event influence factor analysis; (3) comparing and screening a model and a method based on multi-dimensional multi-factor power demand prediction and a path; (4) and (5) performing empirical analysis and performing robustness check on the predicted data to ensure the prediction accuracy.
Further, the specific power demand indexes mainly comprise medium-long term regional overall economy and power consumption prediction data, regional power consumption prediction data of tertiary industry, power consumption prediction data of a regional city, power consumption prediction data of four major high-energy-consumption industries and resident life power consumption prediction data.
Further, dimension and factor induction analysis influencing power demand prediction are combed, and the relationship between regional macroscopic economic growth and power consumption, the relationship between regional mesoscopic structure factors and power consumption, the relationship between regional industry transformation factors and power consumption and the relationship between special event impact and power consumption are mainly analyzed;
the relationship between the regional macroscopic economic growth and the power consumption is mainly analyzed from long-term coordination and short-term 'unhooking'.
And analyzing the influence of the regional structure factors and the power consumption relation on the power consumption respectively from regional pattern and main body function positioning, new and old kinetic energy conversion, novel urbanization and new rural construction.
And thirdly, the relationship between the regional industry transformation factors and the power consumption is mainly subjected to demand analysis from the development and characteristics of diversified loads.
Fourthly, the relation between the special event impact and the power consumption is mainly subjected to demand analysis by using the power consumption of the new crown epidemic impact whole society.
Further, screening a power demand prediction model and a method based on multiple dimensions and multiple factors, wherein the method mainly comprises the selection of a medium-long term macroscopic economy prediction model and a power consumption model;
the macro-economic prediction model is a macro-economic model and a Computable General Equilibrium (CGE) model.
② the power consumption model is a generalized moment estimation model (GMM Estimate).
Furthermore, empirical analysis is performed, robustness check is performed on the predicted data, and empirical verification is performed by taking regional data of Shandong province as an example. The robustness check based on combined prediction provides check values of corresponding power variable predicted values, and the final predicted values are obtained through cyclic comparison of the reference values and the check values to form a main conclusion. The main idea is as follows: carrying out data innovation checking, and checking the result of the middle-long term power prediction under the multi-dimension and multi-factor interaction view angle based on the night light big data and the neural network algorithm
The invention has scientific theory, reasonable framework and smooth logic, effectively constructs a complete, concrete and scientific electric power prediction framework considering multidimensional and multifactor, and provides a better system framework theory and a practical operation method for improving the electric power prediction precision.
Description of the drawings:
FIG. 1 is a path diagram of the power demand prediction technique of the present invention.
Fig. 2 is a power consumption prediction diagram in consideration of the new coronary pneumonia epidemic situation impact.
The specific implementation mode is as follows:
the technical solutions of the present invention will be described clearly and completely below, and it is obvious that the embodiments described below are some, but not all embodiments of the present invention in the description of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
(1) The method is characterized in that power demand indexes are determined and mainly comprise prediction data of overall economy and power consumption of middle-long-term regions, prediction data of regional power consumption of tertiary industry, prediction data of power consumption of regional-level cities, and prediction data of power consumption of four major high-energy-consumption industries and residential life.
(2) Dimension and factor induction analysis influencing power demand prediction are combed, and the method mainly comprises the following steps:
the relation between regional macroscopic economic growth and power consumption is verified by adopting ADF (automatic document feeder) inspection of a Dicker-Fuller standard, long-term coordination of economic growth and power consumption growth is verified, and short-term 'unhooking' phenomenon of economy and power consumption is verified by using power elasticity and power intensity change.
And secondly, analyzing the influence of the structural factors and the power consumption relation in the region on the power consumption respectively from the region pattern and the main function positioning, new and old kinetic energy conversion, novel urbanization and new rural construction.
And thirdly, the relation between the regional industry transformation factors and the power consumption is mainly subjected to demand analysis from the development and characteristics of diversified loads.
And fourthly, analyzing the impact of the new crown epidemic situation on the power consumption of the whole society based on a DSGE model, wherein the impact of the special event impact and the power consumption relation are mainly based on the DSGE model.
The model mainly comprises a family department, a manufacturer department and a government department, and influences of epidemic situation impact on power requirements under simulation are obtained.
Figure BDA0003174145970000021
(3) Screening a power demand prediction model and a method based on multiple dimensions and multiple factors, wherein the method mainly comprises the selection of a medium-long term macroscopic economy prediction model and a power consumption model;
the macroeconomic prediction model is a macroeconomic model and a Computable General Equilibrium (CGE) model, and the main idea is as follows:
a. measuring GDP growth levels of the whole province and each city in 2021-2050 years and the city in different levels respectively from the perspective of 'three-wheel horse driving' at a consumption side and 'production element' at a supply side based on the measurement regression analysis by using the historical development data of the economic society in the city level in the Shandong province and comparing and verifying the GDP growth levels;
b. on the basis of predicting the GDP growth in the year 2021 + 2050, a CGE model is further used for obtaining the three-time industrial growth level in the year 2021 + 2050.
The power consumption model is a generalized moment estimation model (GMM Estimate), and the main idea is as follows:
adding all influencing factors into the metering model in a stepwise regression mode, carrying out robustness analysis and sensitivity inspection, removing insignificant factors, and carrying out repeated debugging;
adding macroscopic factors and mesoscopic factor variables in the model analysis in sequence to perform regression prediction;
the industrial development factor historical data is less, the patent can predict on the basis of combing related policies and industrial change trends, and further adjust the regression prediction result;
d. and in the process of long-term operation in an economic and electric power system, judging whether special event impact exists in real time, and further evaluating the level and the time effect of the special event on the electric power consumption based on a DSGE model so as to correct and adjust the result.
(4) And (4) performing empirical analysis and robustness check on the predicted data, and performing empirical check by taking regional data of Shandong province as an example. The robustness check based on combined prediction provides check values of corresponding power variable predicted values, and the final predicted values are obtained through cyclic comparison of the reference values and the check values to form a main conclusion.
The method comprises the following steps of predicting the power data of each grade city:
the city level power consumption is used as a dependent variable, the city level light radiation brightness total value is used as an independent variable, a regression equation is established, regression coefficients are obtained, and fitting values of the city power consumption are calculated.
And secondly, predicting the total value of the lamplight radiation brightness of each grade city based on a neural network algorithm.
And thirdly, predicting the power consumption level of each city by using the regression coefficient calculated in the first step and the total value of the lamplight radiation brightness of each city grade calculated in the second step.
The steps of the cyclic ratio of the reference value and the check value are as follows:
by carrying out regression analysis on the nighttime lamplight radiation value of 16 cities and 16 districts in Shandong province and urban power consumption data in 2013, a power function regression equation is established: electric 1.02 light 0.53 (R)2＝0.92)。
And further predicting the predicted values of the light radiation values of 16 cities and township night in the Shandong province in 2025, 2030, 2035 and 2050 by using a neural network algorithm. And predicting the power consumption of 16 districts and cities of the Shandong province in each horizontal year by using the power function regression equation and the NPP/VIIRS night light radiation brightness predicted value of 16 districts and cities of the Shandong province in each horizontal year, and finally checking the correlation between the predicted value (check predicted value) of the power consumption of 16 districts and cities of the Shandong province and the reference predicted value by using a Pearson test. The result shows that the correlation coefficient between the check predicted value and the reference predicted value exceeds 0.95, so that the reference predicted result is more reliable.

Claims (3)

1. The invention provides a power prediction framework considering multi-dimension and multi-factor, which comprises a system framework theory and an actual operation method.
2. The framework theory as claimed in claim 1, characterized by supporting the theoretical logic and structure of the power demand forecasting framework, including the analysis steps and conclusions of macroscopic factors affecting the power demand, mesoscopic factors (regional pattern and main body function positioning, new and old kinetic energy conversion and new urbanization and new rural construction), industry transformation factors (diversified loads) and special event (new crown epidemic situation impact) factors.
3. The method of claim 1, wherein the technical path in the medium-long term power demand forecasting framework logic comprises improved portions, steps, sequences and conclusions of models and methods adopted by various portions of the framework.
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