Method, system and equipment for analyzing influence factors in power supply and demand and medium

Abstract

The invention discloses an analysis method, system and device for influence factors in power supply and demand and a medium, and relates to the field of power dispatching, and the key points of the technical scheme are as follows: obtaining historical data generated by a power system in production and use; wherein the historical data refers to data influencing a power supply-demand relationship; constructing a stock flow diagram of the power supply and demand data according to historical data, and constructing a system dynamics model according to the stock flow diagram; wherein the system dynamics model is a multi-dimensional matrix formed by power supply and demand data of each time step length; taking power supply and demand data output by the system dynamics model as a reference sequence, taking historical data as a comparison sequence, and calculating a grey correlation degree between the reference sequence and the comparison sequence by adopting a correlation degree function; and determining a sorting result of the power supply and demand data according to the grey correlation degree, and updating the weight of the power supply and demand data in the system dynamics model according to the sorting result.

Description

Technical Field

[0001] The present invention relates to the field of power dispatching, and more specifically, to an analysis method, system, equipment and medium for factors affecting power supply and demand. Background Art

[0002] When it comes to electricity supply and demand forecasting, existing technologies struggle to address these complex factors. For example, time series analysis relies on the temporal trends and periodicity of historical data but ignores external adjustments. For example, energy conservation and emission reduction can lead to a decrease in electricity supply and demand. Traditional time series analysis methods, due to their strong reliance on historical data, struggle to adapt quickly to these sudden changes, resulting in significant deviations in forecast results. While regression analysis can account for some influencing factors, it typically assumes a simple linear relationship between variables, making it incapable of accurately capturing the nonlinear connections between electricity supply and demand and numerous complex factors. Machine learning methods such as neural networks offer powerful nonlinear fitting capabilities, but their model training processes are prone to falling into local optima and overfitting. Furthermore, they place very high demands on data volume and quality; even the slightest missing or abnormal data can affect forecast accuracy, leading to unstable results. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, system, equipment and medium for analyzing factors affecting power supply and demand, which solves the problem that the factors affecting power supply and demand cannot be accurately predicted when affected by external adjustment factors.

[0004] A first aspect of the present invention provides a method for analyzing factors affecting power supply and demand, the method comprising:

[0005] Acquire historical data generated by the power system during production and use; wherein the historical data refers to data that affects the relationship between power supply and demand;

[0006] Constructing a stock-flow diagram of power supply and demand data based on historical data, and constructing a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step;

[0007] The power supply and demand data output by the system dynamics model is used as a reference sequence, and the historical data is used as a comparison sequence. The grey correlation degree between the reference sequence and the comparison sequence is calculated using a correlation function.

[0008] The ranking results of the data affecting power supply and demand are determined according to the grey correlation degree, and the weights of the power supply and demand data in the system dynamics model are updated according to the ranking results.

[0009] In one implementation, the electricity supply and demand data includes electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

[0010] In one implementation, the power supply and demand data output by the system dynamics model is used as a reference sequence, and the historical data is used as a comparison sequence. The grey correlation between the reference sequence and the comparison sequence is calculated using a correlation function, specifically:

[0011] Determine the corresponding number of comparison sequences based on the type of historical data;

[0012] The correlation coefficient between the reference sequence and each comparison sequence was calculated using the correlation function;

[0013] The weighted average of each correlation coefficient is used to obtain the grey correlation degree.

[0014] In one implementation, the weighted average expression is: Where m represents the number of power supply and demand data, m = 6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

[0015] A second aspect of the present invention provides a system for analyzing factors affecting power supply and demand, the system comprising:

[0016] A data acquisition module is used to acquire historical data generated by the power system during production and use; wherein the historical data refers to data that affects the power supply and demand relationship;

[0017] a model building module for building a stock-flow diagram of power supply and demand data based on historical data, and building a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step;

[0018] a correlation calculation module for calculating the grey correlation between the reference sequence and the comparison sequence using the power supply and demand data output by the system dynamics model as a reference sequence and the historical data as a comparison sequence using a correlation function;

[0019] The factor analysis module is used to determine the ranking results of factors affecting power supply and demand data according to the grey correlation degree, and update the weights of the power supply and demand data in the system dynamics model according to the ranking results.

[0020] In one implementation, the electricity supply and demand data includes electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

[0021] In one implementation, the correlation calculation module is specifically:

[0022] Determine the corresponding number of comparison sequences based on the type of historical data;

[0023] The correlation coefficient between the reference sequence and each comparison sequence was calculated using the correlation function;

[0024] The weighted average of each correlation coefficient is used to obtain the grey correlation degree.

[0025] In one implementation, the weighted average expression is: Where m represents the number of power supply and demand data, m = 6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

[0026] According to a third aspect of the present invention, an electronic device is provided, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a method for analyzing factors affecting power supply and demand as provided in the first aspect of the present invention are implemented.

[0027] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a method for analyzing factors affecting power supply and demand as provided in the first aspect of the present invention are implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention provides a method for evaluating key factors in medium- and long-term electricity supply and demand forecasts based on a system dynamics model and grey correlation analysis. This method effectively describes the interaction between energy production and socio-economy. The system dynamics model can perform forecasting and deduction on a medium- and long-term scale, thus resolving the problem that traditional methods have difficulty accurately capturing complex collaborative relationships. Through grey correlation analysis, the key factors affecting electricity supply and demand are determined, thereby updating the weights of the influencing factors in the system dynamics model.

[0030] 2. This invention provides a method for evaluating key factors in medium- and long-term electricity supply and demand forecasts based on system dynamics and gray correlation analysis. Traditional time series analysis methods, due to their reliance on historical data, cannot flexibly respond to the impact of sudden policy changes and technological innovations. However, system dynamics models can more flexibly adapt to changes in the external environment by adjusting model parameters and structures. Combined with gray correlation analysis, these methods can quantify the correlation between various influencing factors and electricity supply and demand, helping to identify key factors. This method not only captures the nonlinear connections between electricity supply and demand and numerous complex factors, but also effectively improves the model's forecasting accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0032] Figure 1 A schematic flow chart of a method for analyzing factors affecting power supply and demand provided by an embodiment of the present invention;

[0033] Figure 2 A stock-flow diagram of power system production that affects power supply and demand, provided by an embodiment of the present invention;

[0034] Figure 3 A stock-flow diagram of power system usage affecting power supply and demand provided by an embodiment of the present invention;

[0035] Figure 4 A flow chart of key factors for power supply and demand forecasting based on system dynamics and grey relational analysis;

[0036] Figure 5 A functional block diagram of a system for analyzing factors affecting power supply and demand provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0038] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the claimed function, operation or element, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0039] In various embodiments of the present application, the expression "or" or "at least one of B or / and C" includes any or all combinations of the words listed simultaneously. For example, the expression "B or C" or "at least one of B or / and C" may include B, may include C, or may include both B and C.

[0040] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0041] When it comes to electricity supply and demand forecasting, existing traditional methods struggle to cope with these complex dynamics. For example, traditional time series analysis methods rely on the temporal trends and cyclical nature of historical data, but they overlook factors such as external policies, technological innovation, and sudden economic restructuring. In particular, driven by the "dual carbon" policy, many energy-intensive industries have implemented large-scale energy conservation and emission reduction measures, leading to a sharp decline in electricity supply and demand in these sectors. Traditional time series analysis methods, due to their strong reliance on historical data, struggle to adapt quickly to these sudden changes, resulting in significant bias in forecast results. While regression analysis can account for some influencing factors, it typically assumes simple linear relationships between variables, making it unable to accurately capture the nonlinear connections between electricity supply and demand and numerous complex factors. Machine learning methods such as neural networks offer powerful nonlinear fitting capabilities, but their model training process is prone to falling into local optima and overfitting. Furthermore, they place extremely high demands on data volume and quality; even the slightest missing or abnormal data can affect forecast accuracy, leading to unstable results.

[0042] The technical problem to be solved by the present invention is that the supply and demand of electricity is jointly affected by multiple factors such as economic growth, urbanization, development of emerging industries, and energy transformation driven by the "dual carbon" policy. It is difficult to comprehensively consider these complex factors and their dynamic changes; in addition, traditional system dynamics models can only be deduced in time series and cannot process cyclic structures at a single time node, resulting in inefficient calculation process and difficulty in accurately determining the weights of each influencing factor; the purpose of the present invention is to simulate the dynamic interaction relationship between the various elements of the power system through a system dynamics model, and to quantify the degree of correlation between each influencing factor and electricity supply and demand through gray correlation analysis, screen key factors, overcome the limitations of a single method, and thus more accurately evaluate the factors affecting electricity supply and demand.

[0043] Please refer to Figure 1 , Figure 1 The present invention provides a flow chart of a method for analyzing factors affecting power supply and demand, as shown in FIG. Figure 1 As shown, the method includes:

[0044] S101, obtaining historical data generated by the power system during production and use; wherein the historical data refers to data that affects the power supply and demand relationship.

[0045] In this embodiment, power system production refers to electricity generation, such as the amount of electricity generated by thermal, hydro, and wind power units. Second, power system usage refers to electricity consumption, electricity prices, and industrial structure proportions for industrial, agricultural, and commercial purposes. Therefore, historical data generated by the power system's production and usage is collected annually, using a one-year time step.

[0046] The electricity supply and demand data include electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

[0047] Specifically, electricity consumption refers to the total electricity consumption in a region, and energy installed capacity refers to thermal power capacity, wind power capacity, hydropower capacity, photovoltaic capacity, etc.

[0048] S102, constructing a stock-flow diagram of power supply and demand data based on historical data, and constructing a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step.

[0049] In this embodiment, please refer to Figure 2 and Figure 3 , which is a stock-flow diagram generated based on the historical data of power system production and use each year. Figure 2 and Figure 3 As can be seen from the figure, the boundaries of the system dynamics model constructed using the stock-flow diagram include:

[0050] 1) Energy production and supply: This involves the production capacity and supply security of different types of energy, including key indicators such as output, storage capacity, and investment intention of new energy, traditional hydropower, and thermal power generation.

[0051] 2) Power supply and demand management: Analyze the power supply and demand characteristics of various types of users, such as residential, industrial, and commercial users, and consider the dynamic changes of these demands under factors such as different time periods, price fluctuations, and seasonal changes.

[0052] 3) Impact of the policy environment: Consider how factors such as government policies, incentives, and subsidies in the energy sector affect the planning, operation, and optimization of the power system.

[0053] 4) Economic and market factors: Assess the impact of economic factors such as electricity pricing, market investment, and system operation and maintenance costs on the energy supply chain.

[0054] Construct a stock-flow diagram for production, such as Figure 2 As shown;

[0055] Production focuses on factors related to energy production and power supply. It includes power production, energy mix, development of renewable energy, and power system stability. The core elements of this subsystem include:

[0056] (1) Energy production capacity: The capacity for electricity production includes the power generation capacity of traditional energy and renewable energy. The energy production subsystem simulates the changes in the proportions between these energy sources, the improvement of production capacity, and the substitution effect of renewable energy.

[0057] (2) Energy investment: As energy investment grows, the efficiency and flexibility of electricity production gradually improve, thereby affecting the dynamics of the overall electricity supply.

[0058] (3) Policy and environmental factors: The energy structure adjustment driven by the "dual carbon" policy and the restrictions on high-pollution industries by environmental protection policies will affect the production capacity of traditional energy and the development speed of renewable energy.

[0059] The installed capacity, newly built capacity and retired capacity can be expressed in integral form as follows:

[0060] in, represents the installed capacity of unit i in year y; represents the initial annual installed capacity of unit i; and are the newly built and retired installed capacities of unit i, respectively.

[0061] The total cost of a unit is mainly composed of two parts: operation and maintenance cost and investment cost. The total cost of unit i in year y is It can be expressed as:

[0062]

[0063] in, is the operation and maintenance cost of unit i in year y; is the new investment cost of unit i in year y; is the unit installed capacity operation and maintenance cost of unit i in year y.

[0064] The cost and revenue of a unit determine its profit margin. Changes in profit margin will affect the unit's investment willingness, and thus affect new investment. The profit margin of unit i in year y is γ i,y for:

[0065] Among them, χ i,y is the net profit of unit i in year y; is the total investment cost of unit i in year y.

[0066] In order to better analyze and simulate investors' investment intentions, we can construct a quantitative model to express investment intentions as a piecewise function of profit rate:

[0067] Among them, Y inv,y The investment intention of the unit in year y; represents the lower limit of the profit rate; Indicates the upper limit of profit margin.

[0068] The average annual output of thermal power plants and run-of-river hydropower stations depends mainly on factors such as the installed capacity, operating hours, and utilization efficiency of the units, and can be expressed as:

[0069] Among them, C i represents the installed capacity of thermal power and run-of-river hydropower stations, t i Indicates the number of hours of power generation, η i Indicates the power generation efficiency of the unit, calculated based on 8760 hours per year.

[0070] A reservoir-type hydropower station is a hydroelectric power generation system that uses artificial or natural reservoirs to store water and release it to generate electricity. Many factors must be considered, including the reservoir's regulating function, inflow and outflow at different times, hydraulic head, and turbine and generator efficiency. For a given period of time, t, the output of a reservoir-type hydropower station can be expressed as:

[0071] P sh,t =9.81×Q out,t ×H t ×η sh , where Q out,tis the outbound flow in this period, H t is the water head during this period, η sh is the turbine efficiency, and 9.81 is the product of the acceleration due to gravity and the unit conversion factor.

[0072] In a certain period of time t, the output of the photovoltaic power station can be expressed as:

[0073] P pv,t =A×G t ×η pv ×(1-α(T t -T ref ), where A is the total area of the photovoltaic module, G t is the solar irradiance at time t, η pv is the nominal efficiency of the photovoltaic module, α is the power temperature coefficient of the photovoltaic module, T t is the temperature of the photovoltaic module at time t, T ref is the temperature under standard test conditions.

[0074] In a certain period of time t, the wind power output can be expressed as: Among them, P rated is the rated power of the wind turbine, v t is the wind speed at time t, v rated is the rated wind speed.

[0075] Construct a stock-flow diagram using, e.g. Figure 3 As shown;

[0076] In terms of electricity use, the impact of factors such as population growth, industrial structure, urbanization process, and economic growth on electricity supply and demand is comprehensively considered. Its core elements include:

[0077] (1) Economic growth: Economic growth is generally positively correlated with energy consumption and electricity supply and demand. As the economy expands, industrial production, commercial activities, and residents' lives all put greater pressure on electricity supply and demand.

[0078] (2) Population and urbanization: Changes in population size and urbanization rate directly affect the supply and demand of electricity in areas such as housing, transportation, and education.

[0079] (3) Industrial restructuring: With the advancement of scientific and technological innovation and industrial transformation, many traditional high-energy-consuming industries are being replaced by low-carbon, intelligent industries. For example, the development of emerging industries such as intelligent manufacturing and green buildings may reduce the supply and demand of electricity in some areas, but increase demand in other areas.

[0080] The electricity consumption of industry j in year y is Q ind,y,j for:

[0081] Q ind,y,j =Ij P y,j

[0082] Among them, I j is the electricity intensity of the jth industry; P y,j is the output value of the jth industry in year y; is the added value of the output of industry j in year y0; r ind,y,j is the growth rate of the added value of output of industry j in year y; y0 is the initial year; T is the number of years.

[0083] The expression of residential electricity consumption is:

[0084]

[0085] in, represents the population in year y; represents the number of births in year y; represents the number of deaths in year y; represents the population in year y0; Q pep,y represents the residential electricity consumption in year y; represents the number of urban residents; represents the number of rural residents; represents the average urban residents' electricity consumption per capita in year y; represents the per capita electricity consumption of rural residents in year y; ξ represents the urbanization rate.

[0086] The sum of industrial electricity consumption and residential electricity consumption is the total social electricity consumption. for:

[0087]

[0088] The electricity demand during peak hours can be expressed as:

[0089] Wherein, O represents the peak coefficient of electricity supply and demand.

[0090] Specifically, the system dynamics model can comprehensively simulate the dynamic interactions between various elements in the power system. By constructing causal loops and stock-flow diagrams, it can clearly demonstrate the evolution of power supply and demand under the influence of multiple factors such as economic development, policy changes, and energy structure adjustments. Combined with this, the grey correlation analysis method can accurately quantify the degree of correlation between various influencing factors and power supply and demand, helping to screen out the key factors that have the greatest impact on power supply and demand, thereby further improving the pertinence and accuracy of the forecasting model. The combination of system dynamics and grey correlation analysis not only overcomes the limitations of a single forecasting method, but also better addresses the impact of external factors on power supply and demand.

[0091] S103 , using the power supply and demand data output by the system dynamics model as a reference sequence and the historical data as a comparison sequence, and using a correlation function to calculate the grey correlation between the reference sequence and the comparison sequence.

[0092] For details, please refer to Figure 4 , the expression of the reference sequence is {F}=(Q tot ,ξ,C cap,re ,τ,ψ i ,ψ j ).

[0093] Among them, {F} is the data matrix in the sequence, including the power consumption Q tot , urbanization rate ξ, energy installed capacity C cap,re , industrial structure ratio τ and energy grid price ψ i and industrial electricity prices ψ j There are 6 sets of data in total, and y is the year.

[0094] The supply and demand of electricity is related to a variety of factors. The relevant factors are already included in the relevant data obtained. In order to determine which factor has a greater or lesser impact on power generation output, the gray correlation method is used to rank the importance of the factors. It is expressed as: [F y ]=[F y (1)F y (2)…F y (m)]; [G y ]=[G y (1)G y (2)…G y (m)], where [F y ] is the reference sequence, i.e. the obtained correlation data matrix, which is used as the normalized representation; [G y ] is a comparison sequence, which can represent all the above-mentioned related factors; m is the number of related factors, where m = 6; [G y ] is [F y ]The corresponding grey incidence matrix.

[0095] The correlation coefficient between the reference sequence and each comparison sequence was calculated to reflect the degree of correlation between each comparison sequence and the reference sequence.

[0096] Expressed as: Among them, γ() is the correlation function, |G y -F y | for [G y ] and [F y ] is the absolute error in column m, l is the resolution coefficient, which can be taken as 0.5.

[0097] The weighted average of each correlation coefficient is used to obtain the grey correlation degree, which comprehensively reflects the correlation degree between each comparison sequence and the reference sequence. The equal weighted average method is usually used, that is: Where m represents the number of power supply and demand data, m = 6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

[0098] S104 , determining a ranking result of the data affecting power supply and demand according to the grey correlation degree, and updating the weight of the power supply and demand data in the system dynamics model according to the ranking result.

[0099] In this embodiment, combined with the calculated grey correlation degree, the ranking results of the correlation degrees of electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price are determined, and then the weights of the electricity supply and demand data in the system dynamics model are updated according to the ranking results.

[0100] Please refer to Figure 5 , Figure 5 The principle block diagram of an analysis system for factors affecting power supply and demand provided by an embodiment of the present invention is as follows: Figure 5 As shown, the system includes:

[0101] The data acquisition module 510 is used to acquire historical data generated by the power system during production and use; wherein the historical data refers to data that affects the power supply and demand relationship;

[0102] A model building module 520 is configured to construct a stock-flow diagram of power supply and demand data based on historical data, and to construct a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step;

[0103] A correlation calculation module 530 is configured to use the power supply and demand data output by the system dynamics model as a reference sequence and the historical data as a comparison sequence, and to calculate the grey correlation between the reference sequence and the comparison sequence using a correlation function;

[0104] The factor analysis module 540 is used to determine the ranking results of factors affecting the power supply and demand data according to the grey correlation degree, and update the weights of the power supply and demand data in the system dynamics model according to the ranking results.

[0105] It can be seen that the analysis system for factors affecting power supply and demand provided in this embodiment has the following beneficial effects:

[0106] 1. The present invention provides a method for evaluating key factors in medium- and long-term electricity supply and demand forecasts based on a system dynamics model and grey correlation analysis. This method effectively describes the interaction between energy production and socio-economy. The system dynamics model can perform forecasting and deduction on a medium- and long-term scale, thus resolving the problem that traditional methods have difficulty accurately capturing complex collaborative relationships. Through grey correlation analysis, the key factors affecting electricity supply and demand are determined, thereby updating the weights of the influencing factors in the system dynamics model.

[0107] 2. This invention provides a method for evaluating key factors in medium- and long-term electricity supply and demand forecasts based on system dynamics and gray correlation analysis. Traditional time series analysis methods, due to their reliance on historical data, cannot flexibly respond to the impact of sudden policy changes and technological innovations. However, system dynamics models can more flexibly adapt to changes in the external environment by adjusting model parameters and structures. Combined with gray correlation analysis, these methods can quantify the correlation between various influencing factors and electricity supply and demand, helping to identify key factors. This method not only captures the nonlinear connections between electricity supply and demand and numerous complex factors, but also effectively improves the model's forecasting accuracy.

[0108] In some embodiments, the electricity supply and demand data include electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

[0109] In some embodiments, the correlation calculation module 430 is specifically:

[0110] Determine the corresponding number of comparison sequences based on the type of historical data;

[0111] The correlation coefficient between the reference sequence and each comparison sequence was calculated using the correlation function;

[0112] The weighted average of each correlation coefficient is used to obtain the grey correlation degree.

[0113] In some embodiments, the expression for weighted average is: Where m represents the number of power supply and demand data, m = 6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

[0114] The present application also provides an electronic device. The electronic device includes a processor, a memory, a communication interface, and at least one communication bus for connecting the processor, the memory, and the communication interface. The memory includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (PROM), or a compact disc read-only memory (CD-ROM), and is used for storing relevant instructions and data.

[0115] The communication interface is used to receive and send data. The processor can be one or more CPUs. When the processor is a CPU, the CPU can be a single-core CPU or a multi-core CPU. The processor in the electronic device is used to read one or more programs stored in the memory and perform the following operations: obtaining historical data generated by the power system during production and use; wherein the historical data refers to data that affects the power supply and demand relationship; constructing a stock-flow diagram of the power supply and demand data based on the historical data, and constructing a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of the power supply and demand data at each time step; using the power supply and demand data output by the system dynamics model as a reference sequence and the historical data as a comparison sequence, using a correlation function to calculate the grey correlation between the reference sequence and the comparison sequence; determining a ranking result of the data that affects the power supply and demand based on the grey correlation, and updating the weight of the power supply and demand data in the system dynamics model based on the ranking result.

[0116] It should be noted that the specific implementation of each operation can be the corresponding description of the method embodiment described above. The electronic device can be used to execute a method for analyzing factors affecting power supply and demand in the above method embodiment of the present application, which will not be described in detail here.

[0117] In an embodiment of the present disclosure, a computer-readable storage medium is also provided. The computer-readable storage medium is a memory device in a computer device for storing programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment of the method for analyzing factors affecting power supply and demand. Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing factors affecting power supply and demand, characterized in that: include: Acquire historical data generated by the power system during production and use; wherein the historical data refers to data that affects the relationship between power supply and demand; Constructing a stock-flow diagram of power supply and demand data based on historical data, and constructing a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step; The power supply and demand data output by the system dynamics model is used as a reference sequence, and the historical data is used as a comparison sequence. The grey correlation degree between the reference sequence and the comparison sequence is calculated using a correlation function. The ranking results of the data affecting power supply and demand are determined according to the grey correlation degree, and the weights of the power supply and demand data in the system dynamics model are updated according to the ranking results.

2. The method for analyzing factors affecting power supply and demand according to claim 1, characterized in that: The electricity supply and demand data include electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

3. The method for analyzing factors affecting power supply and demand according to claim 2, characterized in that: The power supply and demand data output by the system dynamics model is used as the reference sequence, and the historical data is used as the comparison sequence. The grey correlation degree between the reference sequence and the comparison sequence is calculated using the correlation function, specifically: Determine the corresponding number of comparison sequences based on the type of historical data; The correlation coefficient between the reference sequence and each comparison sequence was calculated using the correlation function; The weighted average of each correlation coefficient is used to obtain the grey correlation degree.

4. The method for analyzing factors affecting power supply and demand according to claim 3, characterized in that: The expression of weighted average is: Where m represents the number of power supply and demand data, m=6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

5. An analysis system for factors affecting power supply and demand, characterized in that: The system includes: A data acquisition module is used to acquire historical data generated by the power system during production and use; wherein the historical data refers to data that affects the power supply and demand relationship; a model building module for building a stock-flow diagram of power supply and demand data based on historical data, and building a system dynamics model based on the stock-flow diagram; wherein the system dynamics model is a multidimensional matrix composed of power supply and demand data at each time step; a correlation calculation module for calculating the grey correlation between the reference sequence and the comparison sequence using the power supply and demand data output by the system dynamics model as a reference sequence and the historical data as a comparison sequence using a correlation function; The factor analysis module is used to determine the ranking results of factors affecting power supply and demand data according to the grey correlation degree, and update the weights of the power supply and demand data in the system dynamics model according to the ranking results.

6. The system for factors affecting power supply and demand according to claim 5, characterized in that: The electricity supply and demand data include electricity consumption, urbanization rate, energy installed capacity, industrial structure ratio, energy on-grid electricity price and industrial electricity price.

7. The analysis system for factors affecting power supply and demand according to claim 6, characterized in that: The correlation calculation module is as follows: Determine the corresponding number of comparison sequences based on the type of historical data; The correlation coefficient between the reference sequence and each comparison sequence was calculated using the correlation function; The weighted average of each correlation coefficient is used to obtain the grey correlation degree.

8. The analysis system for factors affecting power supply and demand according to claim 7, characterized in that: The expression of weighted average is: Where m represents the number of power supply and demand data, m=6; γ() is the correlation function, F y is the reference sequence, G y For comparison sequences, y represents the time step.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method for analyzing factors affecting power supply and demand according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method for analyzing factors affecting power supply and demand according to any one of claims 1 to 4 are implemented.

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