Electricity consumption prediction method, electronic equipment and storage medium

Abstract

The invention discloses an electricity consumption prediction method, electronic equipment and a storage medium, and relates to the technical field of electricity consumption prediction. The power consumption prediction method comprises the following steps: determining the subject category of each power consumption subject in a power utilization environment according to a power utilization influence index; acquiring a preset electric quantity prediction rule of each power consumption subject according to the subject category; obtaining a historical moment energy consumption related index of each power consumption subject, and determining the predicted power consumption of each power consumption subject based on the preset power prediction rule and the historical moment energy consumption related index; and determining the total predicted electricity consumption of the electricity utilization environment according to the predicted electricity consumption. According to the embodiment of the invention, the predicted electricity consumption of key industries and general industries is determined according to different preset electricity consumption prediction rules, and the accuracy and reliability of the total predicted electricity consumption are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power consumption prediction, and particularly to a method for predicting power consumption, an electronic device, and a storage medium. Background Art

[0002] In recent years, with the continuous optimization of China's power consumption structure following the adjustment of the economic structure, the power consumption growth has shown new laws and characteristics. The applicability of the traditional medium- and long-term power consumption prediction method, the trend extrapolation method, has weakened in some scenarios, and it is necessary to innovate prediction ideas and methods to accurately reflect the new power consumption growth law.

[0003] Currently, medium- and long-term power consumption prediction mainly uses methods such as the elasticity coefficient method, time series analysis method, and deep learning method. Among them, the elasticity coefficient method assumes that there is a stable proportional relationship between power demand and economic development. However, in reality, factors such as economic structure, industrial structure, technical level, and residents' consumption habits will change, resulting in the non-constancy of the power consumption elasticity coefficient, making the prediction results may deviate greatly from the actual situation. The time series analysis method assumes that the data has conditions such as stationarity, but the actual data often cannot fully meet these conditions, which may lead to large deviations in the prediction results. At the same time, when dealing with long-term predictions, as the prediction time span increases, the prediction error may gradually accumulate and the accuracy decreases. The deep learning method requires a large amount of historical data for training. When the data volume is insufficient, it may lead to overfitting or underfitting of the model, affecting the prediction accuracy. Therefore, how to accurately predict medium- and long-term power consumption has become an urgent problem to be solved currently. Summary of the Invention

[0004] The present invention provides a method for predicting power consumption, an electronic device, and a storage medium to solve the problem of inaccurate prediction of medium- and long-term power consumption in the prior art.

[0005] According to one aspect of the present invention, a method for predicting power consumption is provided, wherein the method includes:

[0006] Determine the main body categories of each power consumption main body in the power consumption environment according to the power consumption influence index;

[0007] Obtain the preset power consumption prediction rules of each power consumption main body according to the main body category;

[0008] Obtain the historical moment energy consumption related indicators of each power consumption main body, and determine the predicted power consumption of each power consumption main body based on the preset power consumption prediction rules and the historical moment energy consumption related indicators;

[0009] Determine the total predicted power consumption of the power consumption environment according to the predicted power consumption of each power consumption main body.

[0010] According to another aspect of the present invention, there is provided a power consumption prediction device, wherein the device includes:

[0011] A category determination module, configured to determine the main body category of each power consumption main body in the power consumption environment according to power consumption impact indicators;

[0012] A rule determination module, configured to obtain a preset power consumption prediction rule for each power consumption main body according to the main body category;

[0013] A power consumption prediction module, configured to obtain historical moment energy consumption related indicators of each power consumption main body, and determine the predicted power consumption of each power consumption main body based on the preset power consumption prediction rule and the historical moment energy consumption related indicators;

[0014] A power consumption determination module, configured to determine the total predicted power consumption of the power consumption environment according to the predicted power consumptions of each.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power consumption prediction method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the power consumption prediction method according to any embodiment of the present invention when executed by a processor.

[0020] The technical solution of the embodiment of the present invention determines the main body category of each power consumption main body in the power consumption environment according to power consumption impact indicators, obtains the preset power consumption prediction rule for each power consumption main body according to the main body category, obtains the historical moment energy consumption related indicators of each power consumption main body, determines the predicted power consumption of each power consumption main body based on the preset power consumption prediction rule and the historical moment energy consumption related indicators, and determines the total predicted power consumption of the power consumption environment according to the predicted power consumptions of each, so as to realize determining the predicted power consumption according to different preset power consumption prediction rules for key industries and general industries, and improve the accuracy and reliability of the total predicted power consumption.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 is a flowchart of a power consumption prediction method provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a flowchart of a power consumption prediction method provided according to Embodiment 2 of the present invention;

[0025] Figure 3 is a schematic structural diagram of a power consumption prediction device provided according to Embodiment 4 of the present invention;

[0026] Figure 4 is a schematic structural diagram of an electronic device for implementing the power consumption prediction method of the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0029] Embodiment 1

[0030] Figure 1 It is a flowchart of a power consumption prediction method provided according to Embodiment 1 of the present invention. This embodiment is applicable to the situation of predicting power consumption in the medium- and long-term power consumption environment. This method can be executed by a power consumption prediction device, which can be implemented in the form of hardware and / or software, and the power consumption prediction device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0031] S110. Determine the main body categories of each power consumption entity in the power consumption environment according to the power consumption influence indicators.

[0032] Among them, the power consumption influence indicator can be understood as a quantitative indicator that has an effect on power consumption generation and can be used for the main body category of the power consumption entity. Exemplarily, the power consumption influence indicator can include but is not limited to economic indicators, policy indicators, and power consumption ratio indicators, etc. The power consumption environment can be understood as the sum of various power consumption industries in society. The power consumption entity can be understood as an object that consumes power resources in the power field, and the power consumption entity can include industrial power consumption entities and residential power consumption entities. The main body category can be used to indicate the importance degree of the power consumption entity. Generally speaking, the main body category can include general industries and key industries. In one embodiment, the key industries can include but are not limited to manufacturing, service industries, and residential life.

[0033] In the embodiment, when the power consumption influence indicator is a policy indicator, it can be determined whether each power consumption entity is greatly affected by the policy market. If so, it is determined that the main body category of the power consumption entity is a key industry; if not, it is determined that the main body category of the power consumption entity is a general industry. Or, when the power consumption influence indicator is an economic indicator, it can be determined the power consumption growth rate of each power consumption entity, and determine the growth rate of the Gross Domestic Product (GDP), and determine the coupling degree between the power consumption growth rate and the GDP growth rate. If the coupling degree is low, it is determined that the main body category of the power consumption entity is a key industry; if not, it is determined that the main body category of the power consumption entity is a general industry. When the power consumption influence indicator is a power consumption ratio indicator, it can extract the historical power consumption ratio of each power consumption entity, and determine the ratio change of the power consumption entity according to the historical power consumption ratio. Exemplarily, it can determine the ratio change of the power consumption entity in the past 5 years. When the ratio change is greater than or equal to the preset ratio threshold, it is determined that the main body category of the power consumption entity is a key industry; when the ratio change is less than the preset ratio threshold, it is determined that the main body category of the power consumption entity is a general industry.

[0034] S120. Obtain the preset power consumption prediction rules for each power consumption entity according to the entity category.

[0035] Among them, the preset power consumption prediction rules can be understood as the rules for predicting power consumption preset in advance for each power consumption entity corresponding to each entity category. For the same entity category, the preset power consumption prediction rules may be different. Generally speaking, the preset power consumption prediction rules for each power consumption entity in general industries are the same; the preset power consumption prediction rules for each power consumption entity in key industries can be different.

[0036] In the embodiment, the candidate preset power consumption prediction rules can be extracted first according to the entity category, then the name of each power consumption entity is determined, and the preset power consumption prediction rule corresponding to the power consumption entity is determined from the candidate preset power consumption prediction rules according to the name.

[0037] S130. Obtain the historical moment energy consumption related indicators of each power consumption entity, and determine the predicted power consumption of each power consumption entity based on the preset power consumption prediction rules and the historical moment energy consumption related indicators.

[0038] Among them, the historical moment energy consumption related indicators can be understood as the energy consumption parameters at the historical moment. Exemplarily, the historical moment energy consumption related indicators can include power consumption type, entity scale, and unit power consumption intensity. For different power consumption types, the corresponding parameters of the entity scale of the power consumption type can be different. Exemplarily, the entity scale of the power consumption type can include product output, population quantity, and the number of power consumption entities, etc.

[0039] In the embodiment, the historical moment energy consumption related indicators of each power consumption entity can be extracted, and the predicted power consumption of each power consumption entity is determined according to the preset power consumption prediction rules and the historical moment energy consumption related indicators. In the actual operation process, the preset power consumption prediction rules corresponding to general industries can be the same. Exemplarily, the preset power consumption prediction rules corresponding to general industries can be to calculate the predicted power consumption of the power consumption entity based on the preset growth rate method according to the power consumption growth rate and the historical moment power consumption. In practical applications, the historical moment power consumption can be determined, for example, the power consumption in the previous 5 years, the power consumption growth rate is determined according to the power consumption in the previous 5 years, and the predicted power consumption of the power consumption entity is determined according to the growth rate method. When it is determined that the entity category is a key industry, the industry category of the key industry can be determined, and the predicted power consumption of the power consumption entity is predicted according to the preset power consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators.

[0040] S140. Determine the total predicted power consumption of the power consumption environment according to the predicted power consumption of each entity.

[0041] Among them, the total predicted power consumption can be understood as the total predicted power consumption of the whole society.

[0042] In an embodiment, after determining the predicted power consumption, the sum of the predicted power consumptions can be determined, and the sum of the predicted power consumptions is used as the total predicted power consumption of the power consumption environment.

[0043] In an embodiment of the present invention, by determining the main body categories of each power consumption main body in the power consumption environment according to the power consumption influence index, obtaining the preset power consumption prediction rules of each power consumption main body according to the main body categories, obtaining the energy consumption related indexes of each power consumption main body at historical moments, determining the predicted power consumption of each power consumption main body based on the preset power consumption prediction rules and the energy consumption related indexes at historical moments, and determining the total predicted power consumption of the power consumption environment according to the predicted power consumptions, it is realized that the key industries and general industries determine the predicted power consumption according to different preset power consumption prediction rules, improving the accuracy and reliability of the total predicted power consumption.

[0044] Embodiment 2

[0045] Figure 2 It is a flowchart of a power consumption prediction method provided according to Embodiment 2 of the present invention. This embodiment is further optimized and extended based on the above embodiment and can be combined with each optional technical solution in the above embodiment. As Figure 2 shown, the method includes:

[0046] S210. Extract the historical power consumption ratio of each power consumption main body, determine the change in the ratio of the power consumption main body according to the historical power consumption ratio, and determine the absolute value of the change in the ratio.

[0047] Among them, the historical power consumption ratio can be understood as the ratio of the power consumption of a power consumption main body to the total power consumption of all power consumption main bodies in the power consumption environment. The historical power consumption ratio can be at least two. The historical power consumption ratio can be determined for the power consumption of each year; or, it can be determined according to the power consumption of each month, and this is not limited. The historical power consumption ratio can be the power consumption ratios at multiple moments, that is, the number of the historical power consumption ratios can be multiple. The change in the ratio can be understood as the change in the historical power consumption ratio of the power consumption main body. The change in the ratio can be a positive value or a negative value.

[0048] In an embodiment, the historical power consumption ratio of the power consumption main body can be determined. In the actual operation process, the historical power consumption ratios of the power consumption main body in multiple historical years can be determined, and the change in the historical power consumption ratio of the power consumption main body is determined as the change in the ratio according to the time sequence. Since the change in the ratio can be a positive value or a negative value, for the convenience of calculation, the absolute value of the change in the ratio can be determined.

[0049] S220. Determine that the main body category of the power consumption main body whose absolute value is greater than or equal to the preset ratio threshold is a key industry.

[0050] Among them, the preset proportion threshold can be a proportion critical value preset according to user needs, and is used to determine the main body category of the electricity consumption main body. The key industries can be understood as the industries that need to be focused on during the electricity consumption prediction process.

[0051] In the embodiment, when the absolute value is greater than or equal to the preset proportion threshold, it can be determined that the main body category of the electricity consumption main body is a key industry.

[0052] S230. Determine that the main body category of the electricity consumption main body with an absolute value less than the preset proportion threshold is a general industry.

[0053] Among them, the general industry can be understood as an industry with stable electricity consumption fluctuations, and the general industry does not need to be focused on during the electricity consumption prediction process.

[0054] In the embodiment, when the absolute value is less than the preset proportion threshold, it can be determined that the main body category of the electricity consumption main body is a general industry.

[0055] S240. Obtain the preset electricity consumption prediction rules of each electricity consumption main body according to the main body category.

[0056] S250. Extract the product output, population quantity, and / or main body quantity of each electricity consumption type main body in each electricity consumption main body at the historical moment, and use the product output, population quantity, and main body quantity as the scale of the electricity consumption type main body.

[0057] Among them, the electricity consumption type main body can be understood as the electricity consumption object included in each electricity consumption main body. There can be multiple electricity consumption type main bodies included in each electricity consumption main body. The product output can be understood as the production situation of products within the historical moment; the population quantity can be understood as the number of the population at the historical moment; the main body quantity can be understood as the number of the main bodies of products within the historical moment. In the actual operation process, the main body can include but is not limited to different types of computing power, different types of vehicles, and different types of institutions, etc.

[0058] In the embodiment, it can be determined the type of the scale of the electricity consumption type main body included in each electricity consumption type main body, extract the corresponding product output, population quantity, and / or main body quantity at the historical moment according to the type of the scale of the electricity consumption type main body, and use the extracted product output, population quantity, and main body quantity as the scale of the electricity consumption type main body. Exemplarily, for the manufacturing industry, the scale of the electricity consumption type main body can include the product output; for the service industry, the scale of the electricity consumption type main body can include the main body quantity; for residential life, the scale of the electricity consumption type main body can include the population quantity.

[0059] S260. Determine the unit electricity consumption intensity of the scale of each electricity consumption type main body, and use the scale of the electricity consumption type main body and the unit electricity consumption intensity as the energy consumption related indicators at the historical moment.

[0060] Among them, the unit electricity consumption intensity can be understood as the electricity consumption intensity of each type of electricity consumption object at the unit time. For different scales of the main body of electricity consumption types, the corresponding unit electricity consumption intensity can be different.

[0061] In the embodiment, the unit electricity consumption intensity of the scale of the main body of the electricity consumption type at the historical moment can be extracted, and the scale of the main body of the electricity consumption type and the unit electricity consumption intensity are used as the historical moment energy consumption related indicators.

[0062] S270. When it is determined that the main body category is the general industry, based on the preset electricity quantity prediction rule, determine the sum of the products of the scale of the main body of the electricity consumption type and the unit electricity consumption intensity of the same electricity consumption main body at the preset number of historical moments, use the sum as the electricity consumption at the historical moment, and determine the predicted electricity consumption of the electricity consumption main body according to the electricity consumption at the historical moment.

[0063] Among them, the preset number of historical moments refers to the preset number of historical moments. Exemplarily, the preset number of historical moments can include the past 5 years, the past 3 years, or the past 8 years, etc.

[0064] In the embodiment, when the main body category is the general industry, it can be determined that it belongs to the same electricity consumption main body, and the scale of the main body of the electricity consumption type and the unit electricity consumption intensity of the electricity consumption main body at the preset number of historical moments can be determined. That is to say, the scale of the main body of the electricity consumption type and the unit electricity consumption intensity at multiple historical moments can be determined. Respectively determine the product of each scale of the main body of the electricity consumption type and the unit electricity consumption intensity, use the sum of the products as the electricity consumption at the historical moment, and the predicted electricity consumption of the electricity consumption main body can be determined according to the electricity consumption at the historical moment.

[0065] In an embodiment, determining the predicted electricity consumption of the electricity consumption main body according to the electricity consumption at the historical moment includes:

[0066] Determine the electricity consumption growth rate of the electricity consumption main body according to the electricity consumption at the historical moment;

[0067] Based on the preset growth rate method, calculate the predicted electricity consumption of the electricity consumption main body according to the electricity consumption growth rate and the electricity consumption at the historical moment.

[0068] Among them, the electricity consumption growth rate refers to the ratio of the growth amplitude of the electricity consumption to the electricity consumption in the base period within a certain period. The preset growth rate method can be understood as a method of predicting the predicted electricity consumption of the electricity consumption main body according to the electricity consumption growth rate set in advance. Exemplarily, the preset growth rate method can be the compound growth rate method.

[0069] In an embodiment, the electricity consumption at historical moments can be arranged in chronological order, and the growth of the electricity consumption at each historical moment based on the electricity consumption at the previous historical moment can be determined in sequence. The ratio of each growth to the electricity consumption at the previous historical moment is determined as the electricity consumption growth rate at each moment, and the average value of the electricity consumption growth rates is used as the electricity consumption growth rate, that is

[0070]

[0071] Alternatively, the ratio of the electricity consumption at the last historical moment to the electricity consumption at the first historical moment can be determined, and the time interval (usually in years, that is, the number of growth periods) between the electricity consumption at the last historical moment and the electricity consumption at the first historical moment can be determined. The nth root of the ratio is determined, and the nth root of the ratio minus 1 is used as the electricity consumption growth rate, that is Wherein, n is the number of growth periods, that is, the time interval between the electricity consumption at the last historical moment and the electricity consumption at the first historical moment. Exemplarily, when the electricity consumption at the historical moment is determined once a year and the quantity is 5, the electricity consumption at the historical moment can be arranged in chronological order, and the growth of the electricity consumption each year can be determined in turn. That is, the electricity consumption at the historical moment in the 5th year - the electricity consumption at the historical moment in the 4th year is used as the growth in the 5th year, the electricity consumption at the historical moment in the 4th year - the electricity consumption at the historical moment in the 3rd year is used as the growth in the 4th year, the electricity consumption at the historical moment in the 3rd year - the electricity consumption at the historical moment in the 2nd year is used as the growth in the 3rd year, and the electricity consumption at the historical moment in the 2nd year - the electricity consumption at the historical moment in the 1st year is used as the growth in the 2nd year. Divide each growth by the electricity consumption at the corresponding historical moment in the previous year to determine the electricity consumption growth rate for each year. That is, the growth in the 5th year divided by the electricity consumption at the historical moment in the 5th year is used as the electricity consumption growth rate in the 5th year, the growth in the 4th year divided by the electricity consumption at the historical moment in the 4th year is used as the electricity consumption growth rate in the 4th year, the growth in the 3rd year divided by the electricity consumption at the historical moment in the 3rd year is used as the electricity consumption growth rate in the 3rd year, and the growth in the 2nd year divided by the electricity consumption at the historical moment in the 2nd year is used as the electricity consumption growth rate in the 2nd year. That is, determine the average value of the electricity consumption growth rates in the 5th year, the 4th year, the 3rd year, and the 2nd year as the electricity consumption growth rate. Alternatively, determine the ratio of the electricity consumption at the historical moment in the fifth year to the electricity consumption at the historical moment in the first year, determine the fourth root of the ratio, and use the fourth root of the ratio - 1 as the electricity consumption growth rate. Then, through the preset growth rate method, predict the electricity consumption of the electricity consumption subject according to the electricity consumption at the historical moment and the electricity consumption growth rate. Specifically, the electricity consumption at the historical moment in the current year can be determined, and the product of the electricity consumption growth rate and the electricity consumption at the historical moment in the current year is used as the electricity consumption increment. The sum of the electricity consumption increment and the electricity consumption at the historical moment in the current year is used as the predicted electricity consumption in the next year. When the predicted year is n years later, the sum of 1 and the electricity consumption growth rate can be determined, the nth power of the sum can be determined, and the product of the nth power of the sum and the electricity consumption at the historical moment in the current year is used as the predicted electricity consumption n years later. That is, the predicted electricity consumption = the electricity consumption at the historical moment in the current year × (1 + the electricity consumption growth rate) n , wherein, n is the number of years from the current year to the predicted year.

[0072] S280. When it is determined that the subject category is a key industry, determine the industry category of the key industry, and determine the predicted electricity consumption of the electricity consumption subject according to the preset electricity consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators.

[0073] Among them, the industry categories at least include: manufacturing, service industry, and residential life.

[0074] In an embodiment, when the main body category is a key industry, the industry category of the key industry can be used to determine the corresponding preset power consumption prediction rule according to the industry category, and the predicted power consumption of the power consumption subject can be determined according to the corresponding preset power consumption prediction rule and the energy consumption related indicators at the historical moment.

[0075] In one embodiment, when the industry category is manufacturing, determining the predicted power consumption of the power consumption subject according to the preset power consumption prediction rule corresponding to each industry category and the energy consumption related indicators at the historical moment includes:

[0076] Based on the preset power consumption prediction rule, determine the predicted product output, predicted unit power consumption intensity, and production method proportion of each consumption object in the power consumption subject according to the industry business requirements and the energy consumption related indicators at the historical moment;

[0077] Determine the product of the predicted production method proportion and the predicted unit power consumption intensity of each consumption object belonging to the same power consumption subject as the first product, and take the sum of the first products as the manufacturing power consumption intensity;

[0078] Determine the sum of the predicted product outputs of each consumption object belonging to the same power consumption subject as the first output, and determine the product of the first output and the manufacturing power consumption intensity as the predicted power consumption of the power consumption subject.

[0079] Among them, the industry business requirements can be understood as various requirements generated by the industry to achieve its own business goals, meet market demands, maintain normal operations, and pursue development. The industry business requirements may include the required product output and the proportion of production methods.

[0080] In an embodiment, the predicted product output, predicted unit power consumption intensity, and production method proportion of each consumption object in the power consumption subject can be predicted and determined according to the preset power consumption prediction rule, the industry business requirements, and the energy consumption related indicators at the historical moment. Extract the predicted production method proportion and the predicted unit power consumption intensity of each consumption object belonging to the same power consumption subject, determine the product of each predicted production method proportion and the predicted unit power consumption intensity as the first product, and take the sum of all the first products as the manufacturing power consumption intensity. Then determine the sum of the predicted product outputs of each consumption object belonging to the same power consumption subject, take the sum as the first output, and determine the product of the first output and the manufacturing power consumption intensity as the predicted power consumption of the power consumption subject. In actual applications, the predicted power consumption of the power consumption subject can be: y t = ∑ i Q i,t × ∑ j (P i,j,t × I i,j,t ), where i is the main product (i.e., the consumption object); j is the product production method; t is the moment; y tis the electricity consumption of the industry at time t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total output of consumer object i at time t (predicted product output). In one embodiment, the predicted product output can be based on industry development policy objectives, international trade situation, forecasts by authoritative institutions, market size measurement results, etc.; P i,j,t is the proportion of production method j of main product i at time t (predicted production method proportion). Exemplarily, the predicted production method proportion can be based on energy conservation and carbon reduction policy objectives in key areas, etc.; I i,j,t is the electricity consumption intensity of production method j of main product i at time t (predicted unit electricity consumption intensity). The predicted unit electricity consumption intensity can be based on calculation results of historical data, benchmark energy consumption in policy documents, etc.

[0081] In one embodiment, when the industry category is the service industry, the predicted electricity consumption of the electricity consumption entity is determined according to the preset electricity consumption prediction rules corresponding to each industry category and the energy consumption related indicators at historical times, including:

[0082] Based on the preset electricity consumption prediction rules, the predicted service product output, predicted unit service electricity consumption intensity and energy consumption efficiency of each consumer object in the electricity consumption entity are determined according to the service demand and the energy consumption related indicators at historical times;

[0083] Determine the product of the predicted service product output and the predicted unit service electricity consumption intensity of each consumer object belonging to the same electricity consumption entity as the second product, and take the sum of the second products as the initial service predicted electricity consumption;

[0084] Determine the product of the initial service electricity consumption and the energy consumption efficiency as the predicted electricity consumption of the electricity consumption entity.

[0085] Among them, the service demand can be understood as the demand generated by the industry to meet the services in the electricity consumption environment. The service demand can include the output of the demanded service products and the energy consumption efficiency. The energy consumption efficiency refers to the energy efficiency utilization rate.

[0086] In the embodiment, the predicted service product output, predicted unit service electricity consumption intensity and energy consumption efficiency of each consumer object in the electricity consumption entity can be determined according to the preset electricity consumption prediction rules according to the service demand and the energy consumption related indicators at historical times. Extract the predicted service product output and the predicted unit service electricity consumption intensity of each consumer object belonging to the same electricity consumption entity, and respectively determine the product of the predicted service product output and the predicted unit service electricity consumption intensity of each consumer object as the second product. Take the sum of the second products as the initial service predicted electricity consumption, and then take the product of the determined initial service electricity consumption and the energy consumption efficiency as the predicted electricity consumption of the electricity consumption entity.

[0087] In actual applications, when the industry category is the service industry, the electricity consumption entities can be data centers, new energy vehicle charging piles, public service and management organizations, etc. When the electricity consumption entity is a data center, the predicted electricity consumption can be: y t = ∑ i Q i,t × I i,t × PUE i,t ; where i is the computing power type (consumption object); t is the moment; y t is the industry electricity consumption at moment t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of the i computing power type at moment t (predicted service product output), and the basis for predicting the service product output can be industry development policy objectives, authoritative agency predictions, market scale measurement results, etc.; I i,t is the unit computing power electricity consumption intensity of the i computing power type at moment t (predicted unit service electricity consumption intensity), which can be determined based on the calculation results of historical data of major chip products, etc.; PUE i,t is the energy efficiency utilization rate (energy consumption efficiency) of the i computing power type at moment t, mainly based on the calculation results of historical data, policy document requirements, etc.

[0088] In an embodiment, when the electricity consumption entity is a new energy vehicle charging pile, the predicted electricity consumption can be: y t = (∑ i Q i,t × I i,t ) / (1 - loss%) t ; where i is the vehicle type (consumption object); t is moment t; y t is the industry electricity consumption at moment t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of the i vehicle type at moment t (predicted service product output), which can be determined based on industry development policy objectives, authoritative agency predictions, market scale measurement results, etc.; I i,t - the electricity consumption intensity of the i vehicle type at moment t (predicted unit service electricity consumption intensity), mainly based on the calculation results of historical data of major vehicle models, product performance prediction data after technological route changes, etc. loss t is the single-pile point power loss level of the charging pile at moment t, mainly based on industry association research data, etc. 1 / (1 - loss%) t is the energy consumption efficiency.

[0089] In an embodiment, when the electricity consumption entity is a public service and management organization, the predicted electricity consumption can be: y t = ∑ i Q i,t × I i,t ; where i is the organization type (consumption object); t is moment t; y tis the electricity consumption of the industry at time t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of the i - type organization at time t (predicted output of service products), which can be determined according to the expected change in the corresponding population, the calculation results of historical data, etc. I i,t is the electricity consumption intensity of the i - type organization at time t (predicted electricity consumption intensity per unit of service), which can be determined according to the calculation results of historical data, etc. When the electricity consumption entity is a public service and management organization, the energy efficiency utilization rate can be considered as 1.

[0090] In one embodiment, when the industry category is residential life, the predicted electricity consumption of the electricity consumption entity is determined according to the preset electricity consumption prediction rules corresponding to each industry category and the energy consumption related indicators at historical times, including:

[0091] Based on the preset electricity consumption prediction rules, determine the predicted total population, predicted unit electricity consumption level, and temperature influence value of each consumption object in the electricity consumption entity according to the residential life needs and the energy consumption related indicators at historical times;

[0092] Determine the product of the predicted total population and the predicted unit electricity consumption level of each consumption object belonging to the same electricity consumption entity as the third product, and take the sum of the third products as the initial predicted electricity consumption of residents;

[0093] Determine the product of the initial predicted electricity consumption of residents and the temperature influence value as the predicted electricity consumption of the electricity consumption entity.

[0094] Among them, the residential life needs can be understood as the needs generated for residential life, and the residential life needs can include population growth needs and temperature influence values.

[0095] In the embodiment, the predicted total population, predicted unit electricity consumption level, and temperature influence value of each consumption object in the electricity consumption entity can be determined according to the residential life needs and the energy consumption related indicators at historical times through the preset electricity consumption prediction rules. Extract the predicted total population and predicted unit electricity consumption level of each consumption object belonging to the same electricity consumption entity, respectively determine the product of the predicted total population and the predicted unit electricity consumption level of each consumption object as the third product, and take the sum of the third products as the initial predicted electricity consumption of residents. Then determine the product of the initial predicted electricity consumption of residents and the temperature influence value, and take the product as the predicted electricity consumption of the electricity consumption entity.

[0096] In actual application, the predicted electricity consumption of the electricity consumption entity can be: y t =(∑ i Q i,t ×I i,t )×(1 + a·ΔT t ); where i is the type of resident (urban or rural) (consumption object); t is time t, yt is the electricity consumption of the industry at time t (predicted electricity consumption of the main electricity consumers); Q i,t is the total population of the i-th resident type at time t (predicted output of service products), mainly based on the predicted values of the total population and urbanization rate by authoritative institutions, etc.; I i,t is the per capita living electricity consumption level of the i-th resident type at time t (predicted electricity consumption intensity per unit of service), mainly based on the results of historical data calculation, the development goal of per capita GDP and international experience, the electricity consumption level and popularity of new household equipment such as robots, etc. a is the temperature change coefficient, ΔT t is the change value of the average temperature at time t, mainly based on the national temperature prediction results of authoritative institutions, historical temperature change trends, etc.; (1 + a·ΔT t ) is the temperature influence value.

[0097] S290. Determine the sum of the predicted electricity consumptions, and use the sum of the predicted electricity consumptions as the total predicted electricity consumption of the electricity environment.

[0098] In the embodiment, after determining the predicted electricity consumption of each main electricity consumer, the sum of each predicted electricity consumption can be determined, and the sum of the predicted electricity consumptions is used as the total predicted electricity consumption of the electricity environment.

[0099] In an embodiment, the historical electricity consumption ratios of each electricity consumption entity are extracted, the ratio changes of the electricity consumption entities are determined according to the historical electricity consumption ratios, and the absolute values of the ratio changes are determined. The entity categories of the electricity consumption entities with absolute values greater than or equal to a preset ratio threshold are determined as key industries, and the entity categories of the electricity consumption entities with absolute values less than the preset ratio threshold are determined as general industries, which is convenient for targeted prediction for different types of industries. By obtaining the preset electricity consumption prediction rules of each electricity consumption entity according to the entity category, the product output, population quantity, and / or entity quantity of each electricity consumption type entity at a historical moment in each electricity consumption entity are extracted, and the product output, population quantity, and entity quantity are used as the scales of the electricity consumption type entities. The unit electricity consumption intensity of each electricity consumption type entity scale is determined, and the electricity consumption type entity scale and the unit electricity consumption intensity are used as the energy consumption related indicators at the historical moment. When it is determined that the entity category is a general industry, the sum of the products of the electricity consumption type entity scale and the unit electricity consumption intensity belonging to the same electricity consumption entity at a preset number of historical moments is determined based on the preset electricity consumption prediction rules, and the sum is used as the electricity consumption at the historical moment. The predicted electricity consumption of the electricity consumption entity is determined according to the electricity consumption at the historical moment. When it is determined that the entity category is a key industry, the industry category of the key industry is determined, and the predicted electricity consumption of the electricity consumption entity is determined according to the preset electricity consumption prediction rules corresponding to each industry category and the energy consumption related indicators at the historical moment. The sum of the predicted electricity consumptions is determined, and the sum of the predicted electricity consumptions is used as the total predicted electricity consumption of the electricity consumption environment, realizing the separate prediction of the electricity consumption of key industries, making targeted predictions of electricity consumption for different industries, and improving the accuracy of predicted electricity consumption.

[0100] Embodiment 3

[0101] In one embodiment, this embodiment further illustrates a method for predicting electricity consumption by taking the industry categories of key industries including manufacturing, service industries, and residential life, and the service industries including data centers, new energy vehicle charging piles, and public service and management organizations as examples.

[0102] Step 1: Divide key industries and general industries according to the life cycle stage of the industry and the historical electricity consumption changes. In one embodiment, industries that are greatly affected by the policy market environment, have a low coupling degree with GDP, and have large changes in electricity consumption ratios in the past 5 years can be classified as key industries, and industries in a stable development stage, with a high coupling degree with GDP, and with small changes in electricity consumption ratios in the past 5 years can be classified as general industries. Exemplarily, a low coupling degree with GDP can include that the absolute value of the correlation coefficient between the growth rate of industry electricity consumption and the GDP growth rate is below 0.4, and large changes in electricity consumption ratios in the past 5 years can include that the absolute value of the change in electricity consumption ratio is greater than 0.4 percentage points.

[0103] Step 2: Second, conduct electricity consumption prediction for key industries. Decompose the requirements for changes in electricity demand from aspects such as the economic development environment, industrial development cycle, and industrial transmission relationships, and use industry scale and electricity consumption intensity as the main variables to evaluate and predict electricity consumption. Generally speaking, industry electricity consumption t = ∑ i Industry scale i,t × Electricity consumption intensity i,t ; where i is the main product (consumer object); t is the time point t.

[0104] When the electricity consumption entity is the manufacturing industry, the predicted electricity consumption of the electricity consumption entity can be: y t = ∑ i Q i,t × ∑ j (P i,j,t × I i,j,t ), where i is the main product (i.e., consumer object); j is the product production method; t is the time point; y t is the industry electricity consumption at time t (the predicted electricity consumption of the electricity consumption entity); Q i,t is the total output of consumer object i at time t (predicted product output). In one embodiment, the predicted product output can be based on industry development policy goals, international trade situations, authoritative agency predictions, market scale measurement results, etc.; P i,j,t is the proportion of production method j of main product i at time t (predicted production method proportion). Exemplarily, the predicted production method proportion can be based on energy conservation and carbon reduction policy goals in key areas, etc.; I i,j,t is the electricity consumption intensity per unit of production method j of main product i at time t (predicted unit electricity consumption intensity). The predicted unit electricity consumption intensity can be based on historical data calculation results, benchmark energy consumption in policy documents, etc.

[0105] When the electricity consumption entity is a data center in the service industry, the predicted electricity consumption can be: y t = ∑ i Q i,t × I i,t × PUE i,t ; where i is the computing power type (consumer object); t is the time point; y t is the industry electricity consumption at time t (the predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of computing power type i at time t (predicted service product output). The predicted service product output can be based on industry development policy goals, authoritative agency predictions, market scale measurement results, etc.; I i,t is the electricity consumption intensity per unit of computing power of computing power type i at time t (predicted unit service electricity consumption intensity), which can be determined based on the historical data calculation results of main chip products, etc.; PUE i,tis the energy efficiency utilization rate (energy consumption efficiency) of the i-th computing power type at time t, mainly based on the calculation results of historical data, policy document requirements, etc.

[0106] In one embodiment, when the electricity consumption entity is a new energy vehicle charging pile in the service industry, the predicted electricity consumption can be: y t =(∑ i Q i,t ×I i,t ) / (1-loss%) t ; where i is the vehicle type (consumption object); t is the time t; y t is the industry electricity consumption at time t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of the i-th vehicle type at time t (predicted service product output), which can be determined according to industry development policy objectives, authoritative agency predictions, market scale measurement results, etc.; I i,t - the electricity consumption intensity of the i-th vehicle type at time t (predicted unit service electricity consumption intensity), mainly based on the calculation results of historical data of main vehicle models, product performance estimation data after technological route changes, etc. loss t is the single-pile point electrical energy loss level of the charging pile at time t, mainly based on industry association research data, etc. 1 / (1-loss%) t is the energy consumption efficiency.

[0107] In one embodiment, when the electricity consumption entity is a public service and management organization in the service industry, the predicted electricity consumption can be: y t =∑ i Q i,t ×I i,t ; where i is the organization type (consumption object); t is the time t; y t is the industry electricity consumption at time t (predicted electricity consumption of the electricity consumption entity); Q i,t is the total amount of the i-th organization type at time t (predicted service product output), which can be determined according to the corresponding population change expectations, historical data calculation results, etc. I i,t is the electricity consumption intensity of the i-th organization type at time t (predicted unit service electricity consumption intensity), which can be determined according to the historical data calculation results, etc. When the electricity consumption entity is a public service and management organization, the energy efficiency utilization rate can be considered as 1.

[0108] When the electricity consumption entity is residential life, the predicted electricity consumption can be: y t =(∑ i Q i,t ×I i,t )×(1 + a·ΔT t ); where i is the resident type (urban or rural) (consumption object); t is the time t, y tis the electricity consumption of the industry at a certain moment (the predicted electricity consumption of the electricity consumption subject); Q i,t is the total population of the i-th resident type at time t (the predicted output of service products), and the main basis is the predicted values of the total population and urbanization rate by authoritative institutions, etc.; I i,t is the per capita living electricity consumption level of the i-th resident type at time t (the predicted electricity consumption intensity per unit of service), and the main basis is the calculation result of historical data, the development goal of per capita GDP and international experience, the electricity consumption level and popularization degree of new household equipment such as robots, etc. a is the temperature change coefficient, ΔT t is the change value of the average temperature at time t, and the main basis is the national temperature prediction result of authoritative institutions, the historical temperature change trend, etc.; (1 + a·ΔT t ) is the temperature influence value.

[0109] Step 3: Conduct electricity consumption prediction for general industries. Based on the compound growth rate of the industry's electricity consumption in the previous 5 years as the benchmark, comprehensively consider the changes in future GDP and industrial growth expectations under various scenarios, and use the time series method and growth rate method to estimate the average annual growth rate and electricity consumption of general industries.

[0110] Step 4: Form the predicted result of the total electricity consumption of the whole society and conduct result verification.

[0111] Embodiment 4

[0112] Figure 3 is a schematic structural diagram of an electricity consumption prediction device provided according to Embodiment 4 of the present invention. As Figure 3 shown, the device includes: a category determination module 31, a rule determination module 32, an electricity consumption prediction module 33, and an electricity consumption determination module 34.

[0113] Among them, the category determination module 31 is used to determine the main category of each electricity consumption subject in the electricity consumption environment according to the electricity consumption influence index;

[0114] The rule determination module 32 is used to obtain the preset electricity consumption prediction rules of each electricity consumption subject according to the main category;

[0115] The electricity consumption prediction module 33 is used to obtain the historical moment energy consumption related indexes of each electricity consumption subject, and determine the predicted electricity consumption of each electricity consumption subject based on the preset electricity consumption prediction rules and the historical moment energy consumption related indexes;

[0116] The electricity consumption determination module 34 is used to determine the total predicted electricity consumption of the electricity consumption environment according to the predicted electricity consumption of each subject.

[0117] In the technical solution of the embodiment of the present invention, the category determination module determines the main body category of each power consumption main body in the power consumption environment according to the power consumption influence index. The rule determination module obtains the preset power consumption prediction rules of each power consumption main body according to the main body category. The power consumption prediction module obtains the historical moment energy consumption related indexes of each power consumption main body, and determines the predicted power consumption of each power consumption main body based on the preset power consumption prediction rules and the historical moment energy consumption related indexes. The power consumption determination module determines the total predicted power consumption of the power consumption environment according to the predicted power consumptions of each power consumption main body, realizing determining the predicted power consumption according to different preset power consumption prediction rules for key industries and general industries, and improving the accuracy and reliability of the total predicted power consumption.

[0118] In one embodiment, the category determination module 31 includes:

[0119] The absolute value determination unit is used to extract the historical power consumption ratio of each power consumption main body, determine the ratio change of the power consumption main body according to the historical power consumption ratio, and determine the absolute value of the ratio change;

[0120] The key industry determination unit is used to determine that the main body category of the power consumption main body whose absolute value is greater than or equal to the preset ratio threshold is a key industry;

[0121] The general industry determination unit is used to determine that the main body category of the power consumption main body whose absolute value is less than the preset ratio threshold is a general industry.

[0122] In one embodiment, the power consumption prediction module 33 includes:

[0123] The scale determination unit is used to extract the product output, population quantity, and / or main body quantity of each power consumption type main body in each power consumption main body at the historical moment, and use the product output, population quantity, and main body quantity as the scale of the power consumption type main body;

[0124] The index determination unit is used to determine the unit power consumption intensity of the scale of each power consumption type main body, and use the scale of the power consumption type main body and the unit power consumption intensity as the historical moment energy consumption related indexes;

[0125] The first power consumption determination unit is used to, when determining that the main body category is a general industry, determine the sum of the products of the scale of the power consumption type main body and the unit power consumption intensity belonging to the same power consumption main body at a preset number of historical moments based on the preset power consumption prediction rules, use the sum as the power consumption at the historical moment, and determine the predicted power consumption of the power consumption main body according to the power consumption at the historical moment;

[0126] The second power consumption determination unit is used to, when determining that the main body category is a key industry, determine the industry category of the key industry, and determine the predicted power consumption of the power consumption main body according to the preset power consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indexes;

[0127] Among them, the industry categories at least include: manufacturing, service industry, and residential life.

[0128] In one embodiment, the first power consumption determination unit is specifically configured to:

[0129] Determine the power consumption growth rate of the power consumption subject according to the power consumption at historical moments;

[0130] Calculate the predicted power consumption of the power consumption subject based on the preset growth rate method according to the power consumption growth rate and the power consumption at historical moments.

[0131] In one embodiment, the second power consumption determination unit is specifically configured to:

[0132] Based on the preset power consumption prediction rule, determine the predicted product output, predicted unit power consumption intensity, and production method proportion of each consumption object in the power consumption subject according to the industry business demand and the historical moment energy consumption related indicators;

[0133] Determine the product of the predicted production method proportion and the predicted unit power consumption intensity of each consumption object belonging to the same power consumption subject as the first product, and use the sum of the first products as the manufacturing power consumption intensity;

[0134] Determine the sum of the predicted product outputs of each consumption object belonging to the same power consumption subject as the first output, and determine the product of the first output and the manufacturing power consumption intensity as the predicted power consumption of the power consumption subject.

[0135] In one embodiment, the second power consumption determination unit is specifically configured to:

[0136] Based on the preset power consumption prediction rule, determine the predicted service product output, predicted unit service power consumption intensity, and energy consumption efficiency of each consumption object in the power consumption subject according to the service demand and the historical moment energy consumption related indicators;

[0137] Determine the product of the predicted service product output and the predicted unit service power consumption intensity of each consumption object belonging to the same power consumption subject as the second product, and use the sum of the second products as the initial service predicted power consumption;

[0138] Determine the product of the initial service power consumption and the energy consumption efficiency as the predicted power consumption of the power consumption subject.

[0139] In one embodiment, the second power consumption determination unit is specifically configured to:

[0140] Based on the preset power consumption prediction rule, determine the predicted total population, predicted unit power consumption level, and temperature influence value of each consumption object in the power consumption subject according to the residential life demand and the historical moment energy consumption related indicators;

[0141] Determine the product of the predicted total population and the predicted unit power consumption level of each consumption object belonging to the same electricity consumption entity as the third product, and use the sum of the third products as the initial predicted electricity consumption of residents;

[0142] Determine the product of the initial predicted electricity consumption of residents and the temperature influence value as the predicted electricity consumption of the electricity consumption entity.

[0143] In one embodiment, the electricity consumption determination module 34 includes:

[0144] The electricity consumption determination unit is used to determine the sum of each predicted electricity consumption, and use the sum of the predicted electricity consumption as the total predicted electricity consumption of the electricity consumption environment.

[0145] The electricity consumption prediction device provided by the embodiments of the present invention can execute the electricity consumption prediction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0146] Embodiment Five

[0147] Figure 4 It is a schematic structural diagram of an electronic device for implementing an electricity consumption prediction method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0148] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0149] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0150] The processor 11 can be various general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power consumption prediction method.

[0151] In some embodiments, the power consumption prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power consumption prediction method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the power consumption prediction method by any other suitable means (e.g., by means of firmware).

[0152] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partly on the machine, partly on the machine as an independent software package and partly on a remote machine, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected with each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0157] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0158] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0159] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting power consumption, characterized in that, Including: Determine the entity categories of each power consumption entity in the power consumption environment according to the power consumption impact index; Obtain the preset power consumption prediction rules of each power consumption entity according to the entity categories; Obtain the historical moment energy consumption related indicators of each power consumption entity, and determine the predicted power consumption of each power consumption entity based on the preset power consumption prediction rules and the historical moment energy consumption related indicators; Determine the total predicted power consumption of the power consumption environment according to the predicted power consumption of each entity.

2. The method according to claim 1, wherein The determining the entity categories of each power consumption entity in the power consumption environment according to the power consumption impact index includes: Extract the historical power consumption ratio of each power consumption entity, determine the change in the ratio of the power consumption entity according to the historical power consumption ratio, and determine the absolute value of the change in the ratio; Determine that the entity category of the power consumption entity with the absolute value greater than or equal to the preset ratio threshold is the key industry; Determine that the entity category of the power consumption entity with the absolute value less than the preset ratio threshold is the general industry.

3. The method according to claim 1, wherein The obtaining the historical moment energy consumption related indicators of each power consumption entity and determining the predicted power consumption of each power consumption entity based on the preset power consumption prediction rules and the historical moment energy consumption related indicators includes: Extract the product output, population quantity, and / or entity quantity of each power consumption type entity of each power consumption entity at the historical moment, and use the product output, population quantity, and entity quantity as the scale of the power consumption type entity; Determine the unit power consumption intensity of each power consumption type entity scale, and use the power consumption type entity scale and the unit power consumption intensity as the historical moment energy consumption related indicators; When it is determined that the entity category is the general industry, based on the preset power consumption prediction rules, determine the sum of the products of the power consumption type entity scale and the unit power consumption intensity of the same power consumption entity at the preset number of historical moments, use the sum as the historical moment power consumption, and determine the predicted power consumption of the power consumption entity according to the historical moment power consumption; When it is determined that the entity category is the key industry, determine the industry category of the key industry, and determine the predicted power consumption of the power consumption entity according to the preset power consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators; Among them, the industry categories at least include: manufacturing, service industry, and residential life.

4. The method according to claim 3, wherein The determining the predicted power consumption of the power consumption entity according to the historical moment power consumption includes: Determine the power consumption growth rate of the power consumption entity according to the historical moment power consumption; Based on the preset growth rate method, calculate the predicted power consumption of the power consumption entity according to the power consumption growth rate and the historical moment power consumption.

5. The method according to claim 3, characterized in that When the industry category is manufacturing, the determining the predicted power consumption of the power consumption entity according to the preset power consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators includes: Based on the preset power consumption prediction rules, determine the predicted product output, predicted unit power consumption intensity, and production method ratio of each consumption object in the power consumption entity according to the industry business requirements and the historical moment energy consumption related indicators; Determine the first product of the predicted production method ratio and the predicted unit electricity consumption intensity of each consumption object belonging to the same electricity consumption subject, and use the sum of the first products as the manufacturing electricity consumption intensity; Determine the sum of the predicted product outputs of each consumption object belonging to the same electricity consumption subject as the first output, and determine the product of the first output and the manufacturing electricity consumption intensity as the predicted electricity consumption of the electricity consumption subject.

6. The method according to claim 3, wherein When the industry category is the service industry, determining the predicted electricity consumption of the electricity consumption subject according to the preset electricity consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators includes: Based on the preset electricity consumption prediction rules, determine the predicted service product output, predicted unit service electricity consumption intensity, and energy consumption efficiency of each consumption object in the electricity consumption subject according to the service demand and the historical moment energy consumption related indicators; Determine the product of the predicted service product output and the predicted unit service electricity consumption intensity of each consumption object belonging to the same electricity consumption subject as the second product, and use the sum of the second products as the initial service predicted electricity consumption; Determine the product of the initial service electricity consumption and the energy consumption efficiency as the predicted electricity consumption of the electricity consumption subject.

7. The method according to claim 3, wherein When the industry category is residential life, determining the predicted electricity consumption of the electricity consumption subject according to the preset electricity consumption prediction rules corresponding to each industry category and the historical moment energy consumption related indicators includes: Based on the preset electricity consumption prediction rules, determine the predicted total population, predicted unit electricity consumption level, and temperature influence value of each consumption object in the electricity consumption subject according to the residential life demand and the historical moment energy consumption related indicators; Determine the product of the predicted total population and the predicted unit electricity consumption level of each consumption object belonging to the same electricity consumption subject as the third product, and use the sum of the third products as the initial residential predicted electricity consumption; Determine the product of the initial residential predicted electricity consumption and the temperature influence value as the predicted electricity consumption of the electricity consumption subject.

8. The method according to claim 1, characterized in that, The determining the total predicted electricity consumption of the electricity consumption environment according to each of the predicted electricity consumptions includes: Determine the sum of each of the predicted electricity consumptions, and use the sum of the predicted electricity consumptions as the total predicted electricity consumption of the electricity consumption environment.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the electricity consumption prediction method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement the electricity consumption prediction method according to any one of claims 1-8 when executed.

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