Electricity consumption prediction method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses an electricity consumption prediction method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: respectively acquiring respective electricity consumption of different industries in a plurality of areas in a historical time period, and obtaining historical electricity consumption time sequence data; according to the historical electricity consumption time sequence data, determining historical electricity consumption total amount time sequence data of a total area formed by the plurality of areas; according to the historical electricity consumption time series data, obtaining historical proportion time series data of the target industry in the target area; according to the historical electricity consumption time sequence data, obtaining a predicted total electricity consumption amount; according to the historical proportion time sequence data of the target industry in the target area, predicting the proportion of the target industry in the target area in the target time period to obtain a predicted electric quantity proportion; and predicting the power consumption of the target industry in the target area in the time period according to the predicted power consumption ratio and the predicted total power consumption. The technical problem that the future electricity consumption of a specific region cannot be accurately predicted in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power engineering technology, and in particular to a method and device for predicting power consumption, a non-volatile storage medium and an electronic device. Background Art

[0002] According to the comparative advantages of relevant industries in a region and the changes in the proportion of electricity consumption over a period of time, the situation of industry transfer out or in can be judged. And the scale of industry transfer out and in can be calculated according to the proportion of core indicators. Therefore, it is very important to accurately predict the electricity consumption of a specific industry in a specific region in a certain period of time in the future. The traditional electricity consumption forecasting model mainly considers factors such as population, economic growth, and industrial structure adjustment, but does not consider the impact of industrial transfer between regions, which leads to a deviation between the forecast of electricity consumption of a specific industry in a specific region in a certain period of time in the future and the actual situation.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, non-volatile storage medium and electronic device for predicting power consumption, so as to at least solve the technical problem that the prior art cannot accurately predict the future power consumption of a specific area.

[0005] According to one aspect of an embodiment of the present invention, a method for predicting electricity consumption is provided, including: respectively obtaining the electricity consumption of different industries in multiple regions within a historical period to obtain historical electricity consumption time series data; determining the historical total electricity consumption time series data of a total region composed of multiple regions based on the historical electricity consumption time series data; determining the proportion of electricity consumption of a target industry in a target region to the electricity consumption of the total region based on the historical electricity consumption time series data, to obtain historical proportion time series data of the target industry in the target region, wherein the multiple regions include the target region, and the different industries include the target industry; predicting the electricity consumption of the total region within the target period based on the historical electricity consumption time series data to obtain a predicted total electricity consumption; predicting the proportion of the target industry within the target region within the target period based on the historical proportion time series data to obtain a predicted electricity proportion; predicting the electricity consumption of the target industry within the target region within the period based on the predicted electricity proportion and the predicted total electricity consumption.

[0006] Optionally, based on the historical electricity consumption time series data, the electricity consumption of the total area in the target period is predicted to obtain the predicted total electricity consumption, including: using the least squares method to construct an electricity consumption function for the total area based on the historical total electricity consumption time series data, wherein the electricity consumption function for the total area is used to describe the change in electricity consumption for the total area over time; based on the electricity consumption function for the total area, the electricity consumption of the total area in the target period is predicted to obtain the predicted total electricity consumption.

[0007] Optionally, the least squares method is used to construct an electricity consumption function for the total area based on the time series data of historical total electricity consumption, including: determining the changing trend of the historical total electricity consumption based on the time series data of historical total electricity consumption; selecting a basic function that matches the changing trend of the historical total electricity consumption; and using the least squares method to adjust the values ​​of the parameters in the basic function based on the time series data of historical total electricity consumption to construct an electricity consumption function for searching the total area.

[0008] Optionally, based on the time series data of historical total electricity consumption, determining the changing trend of the historical total electricity consumption includes: selecting multiple points in the time series data of historical total electricity consumption at preset time intervals; determining the difference between the multiple points; and determining, based on the difference between the multiple points, whether the changing trend of the historical total electricity consumption is a linear change or an exponential change.

[0009] Optionally, a basic function that matches the changing trend of the historical total electricity consumption is selected, including: when the changing trend of the historical total electricity consumption is a linear change, determining a linear regression function as the basic function; or, when the changing trend of the historical total electricity consumption is an exponential change, determining an exponential function as the basic function.

[0010] Optionally, based on the historical proportion time series data of the target industry in the target area, the proportion of the target industry in the target area within the target period is predicted to obtain the predicted electricity proportion, including: determining the proportion of electricity consumption of different industries in multiple regions in the total area based on the historical electricity consumption time series data, and predicting the proportion of different industries in multiple regions to obtain the predicted proportion of different industries in multiple regions; normalizing the predicted proportions of different industries in multiple regions to obtain the predicted electricity proportion of the target industry in the target area within the target period.

[0011] According to another aspect of an embodiment of the present invention, there is provided a power consumption prediction device, including: an acquisition module, used to respectively acquire the power consumption of each of different industries in multiple regions within a historical period, and obtain historical power consumption time series data; a first determination module, used to determine the historical total power consumption time series data of a total region composed of multiple regions based on the historical power consumption time series data; a second determination module, used to determine the proportion of power consumption of a target industry in a target region in the power consumption of the total region based on the historical power consumption time series data, and obtain the historical proportion time series data of the target industry in the target region, wherein the multiple regions include the target region, and the different industries include the target industry; a first prediction module, used to predict the power consumption of the total region within a target period based on the historical power consumption time series data, and obtain the predicted total power consumption; a second prediction module, used to predict the proportion of the target industry in the target region within a target period based on the historical proportion time series data of the target industry in the target region, and obtain the predicted power proportion; a third prediction module, used to predict the power consumption of the target industry in the target region within the period based on the predicted power proportion and the predicted total power consumption.

[0012] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the power consumption prediction methods.

[0013] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the power consumption prediction methods.

[0014] According to another aspect of the embodiments of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the computer program implements any one of the above-mentioned power consumption prediction methods.

[0015] In an embodiment of the present invention, historical electricity consumption time series data is obtained by respectively obtaining the electricity consumption of different industries in multiple regions within a historical period; based on the historical electricity consumption time series data, the historical total electricity consumption time series data of the total region composed of multiple regions is determined; based on the historical electricity consumption time series data, the proportion of electricity consumption of the target industry in the target region in the electricity consumption of the total region is determined, and the historical proportion time series data of the target industry in the target region is obtained, wherein the multiple regions include the target region, and the different industries include the target industry; based on the historical electricity consumption time series data, the electricity consumption of the total region within the target period is predicted to obtain the predicted total electricity consumption; based on the historical proportion time series data of the target industry within the target region, the proportion of the target industry within the target region within the target period is predicted to obtain the predicted electricity proportion; based on the predicted electricity proportion and the predicted total electricity consumption, the electricity consumption of the target industry in the target region within the period is predicted, which solves the technical problem that the prior art cannot accurately predict the future electricity consumption of a specific region, achieves the purpose of accurately predicting the future electricity consumption of a specific region, and further realizes the technical effect of improving the accuracy of prediction of future electricity consumption in a specific region. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a method for predicting power consumption provided according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a power consumption prediction device provided according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] 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 the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of electricity consumption prediction is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] Figure 1 is a flow chart of a method for predicting power consumption according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0024] Step S102, respectively obtaining the power consumption of different industries in multiple regions within a historical period to obtain historical power consumption time series data;

[0025] In this step, we collect and organize electricity consumption data of different provinces in historical time periods (such as the past five years) and various industries (such as manufacturing, service industry, agriculture, etc.). These electricity consumption data can be used to analyze electricity consumption patterns, trends and cyclical changes to find out the changing patterns of electricity consumption in certain industries in certain provinces in specific seasons or economic cycles, so as to more accurately predict future electricity demand. express, is the electricity consumption of the i-th province, the j-th industry, and the n-th year. Where j∈[1,47] is the industry sector with electricity demand.

[0026] Step S104, determining the total historical electricity consumption time series data of a total area consisting of multiple areas according to the historical electricity consumption time series data;

[0027] In this step, after collecting the time series data of electricity consumption in different provinces respectively, the time series data of these individual provinces are aggregated to obtain the historical total electricity consumption of all provinces. The historical total electricity consumption of all provinces can be represented by , representing the industry category, and representing the year.

[0028] In an optional embodiment, the electricity consumption of the total area in the target period is predicted based on the historical electricity consumption time series data to obtain the predicted total electricity consumption, including: using the least squares method to construct an electricity consumption function for the total area based on the historical total electricity consumption time series data, wherein the electricity consumption function for the total area is used to describe the change in electricity consumption for the total area over time; based on the electricity consumption function for the total area, the electricity consumption of the total area in the target period is predicted to obtain the predicted total electricity consumption.

[0029] Optionally, select the least squares method as the tool to construct the electricity consumption forecast function. The least squares method is a statistical method used to find the best fit between the data and the forecast model. Its core is to minimize the sum of the squared errors between the model prediction value and the actual observation value. Based on the historical electricity consumption time series data, the least squares method is used to construct the total area electricity consumption function. This function will describe the law of total electricity consumption over time, which may include long-term trends, seasonal changes, and other cyclical factors.

[0030] In an optional embodiment, the least squares method is used to construct an electricity consumption function of the total area based on the historical total electricity consumption time series data, including: determining the changing trend of the historical total electricity consumption according to the historical total electricity consumption time series data; selecting a basic function that matches the changing trend of the historical total electricity consumption; and using the least squares method to adjust the values ​​of the parameters in the basic function according to the historical total electricity consumption time series data to construct an electricity consumption function for searching the total area.

[0031] Optionally, based on the historical total electricity consumption time series data, analyze the change trend of these data over time, which may include long-term growth, seasonal fluctuations or cyclical changes. According to the identified trend of change in the historical total electricity consumption, select an appropriate basic function. The basic function can be a linear, polynomial, exponential or a function with a periodic component. It should be able to reflect and simulate the power demand pattern obtained in the trend analysis. The least squares method, a statistical method, is used to adjust the parameters in the selected basic function to ensure that the function fits the historical total electricity consumption data most accurately.

[0032] In an optional embodiment, the changing trend of the historical total electricity consumption is determined based on the historical total electricity consumption time series data, including: selecting multiple points in the historical total electricity consumption time series data at preset time intervals; determining the difference between the multiple points; and determining, based on the difference between the multiple points, whether the changing trend of the historical total electricity consumption is a linear change or an exponential change.

[0033] Optionally, multiple data points are selected from the historical total electricity consumption time series data at preset time intervals (such as every year or every quarter). These points represent the total electricity consumption at different time points, and the difference between the selected data points is calculated. The difference can be calculated as the difference between adjacent points or the average difference within a specific time window. By analyzing these differences, it is determined whether the change trend of the historical total electricity consumption is linear or exponential. A linear trend indicates that electricity consumption increases or decreases evenly over time, while an exponential trend indicates that the rate of increase or decrease accelerates or slows down over time.

[0034] In an optional embodiment, a basic function that matches the changing trend of the historical total electricity consumption is selected, including: when the changing trend of the historical total electricity consumption is a linear change, determining a linear regression function as the basic function; or, when the changing trend of the historical total electricity consumption is an exponential change, determining an exponential function as the basic function.

[0035] Optionally, based on the historical electricity consumption data, analyze its changing trend over time to determine whether the trend is linear or exponential. A linear trend means that the rate of increase or decrease in electricity consumption is constant, while an exponential trend means that the rate of increase or decrease changes over time, usually showing accelerated growth or decrease. According to the results of the above trend analysis, select a suitable basic function. If the trend of the total historical electricity consumption is linear, then select the linear regression function as the basic function; on the contrary, if the trend is exponential, select the exponential function as the basic function.

[0036] In an optional embodiment, based on the historical proportion time series data of the target industry in the target area, the proportion of the target industry in the target area within the target period is predicted to obtain the predicted electricity proportion, including: determining the proportion of electricity consumption of different industries in multiple regions in the total area based on the historical electricity consumption time series data, and predicting the proportion of different industries in multiple regions to obtain the predicted proportion of different industries in multiple regions; normalizing the predicted proportion of different industries in multiple regions to obtain the predicted electricity proportion of the target industry in the target area within the target period.

[0037] Optionally, based on historical electricity consumption time series data, calculate the proportion of electricity consumption of different industries in multiple provinces to the total provincial electricity consumption, analyze and predict these historical proportion data, so as to estimate the proportion of electricity demand of different industries in each province during the target period, normalize the predicted proportions of all industries to ensure that the sum of the proportions of all industries is equal to 1, so as to obtain the accurate predicted electricity proportion of a specific industry in a specific province.

[0038] Step S106, determining the proportion of the power consumption of the target industry in the target area to the power consumption of the total area according to the historical power consumption time series data, and obtaining the historical proportion time series data of the target industry in the target area, wherein multiple areas include the target area, and different industries include the target industry;

[0039] In this step, the proportion of electricity consumption of the target industry (such as manufacturing) in the target province (such as Province A) to the total electricity consumption of all provinces in the historical period is calculated. The formula for the proportion of electricity consumption of a certain industry in a certain province is: is the proportion of electricity consumption in the i-th province, the j-th industry, and the n-th year.

[0040] Step S108, predicting the total area power consumption within the target period based on the historical power consumption time series data to obtain the predicted total power consumption;

[0041] In this step, based on the time series data of historical electricity consumption, taking into account various factors such as population and economic growth, the total electricity consumption of all provinces in the target time period (such as the next two years) is predicted. The total electricity consumption of all provinces in the Mth year can be predicted using E M express.

[0042] Step S110, predicting the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area, and obtaining the predicted electricity proportion;

[0043] In this step, based on the historical proportion time series data of a specific industry in a specific province, the proportion of the specific industry in the specific province in the future electricity consumption to the specific industry in all provinces is predicted. For example, assuming that the specific province is Province A and the specific industry is manufacturing, it can be predicted that in a specific time period in the future (such as the next two years), the proportion of the electricity consumption of the manufacturing industry in Province A to the total electricity consumption of the manufacturing industry in all provinces can be predicted. The proportion of the electricity demand standard of a certain industry in a certain province in the Mth year can be used Indicates, i represents province, j represents industry, and n represents year. Based on the historical time series, the proportion of electricity demand in the predicted year is obtained by the least squares method. like The standard ratio like The standard proportion is: This correction ensures that the total sum of the proportions is 1.

[0044] Step S112, predicting the electricity consumption of the target industry in the target area within the time period according to the predicted electricity proportion and the predicted total electricity consumption.

[0045] In this step, the power consumption of a certain industry in a certain province can be obtained based on the predicted proportion of the power consumption of a certain industry in a certain province to the total power consumption of all provinces and the total power consumption of all provinces. represents the proportion of the jth industry in the i-th province in the Mth year, E jM Represents the electricity consumption of the jth industry in the Mth year.

[0046] Through the above-mentioned method, the electricity consumption of the total area in the target time period is predicted according to the historical electricity consumption time series data to obtain the predicted total electricity consumption; according to the historical proportion time series data of the target industry in the target area, the proportion of the target industry in the target area in the target period is predicted to obtain the predicted electricity proportion; according to the historical electricity consumption time series data, the proportion of the electricity consumption of the target industry in the target area in the total area is determined to obtain the historical proportion time series data of the target industry in the target area, wherein multiple areas include the target area, and different industries include the target industry; according to the historical electricity consumption time series data, the historical total electricity consumption time series data of the total area composed of multiple areas is determined; the electricity consumption of different industries in multiple areas in the historical period is obtained respectively to obtain the historical electricity consumption time series data; according to the predicted electricity proportion and the predicted total electricity consumption, the electricity consumption of the target industry in the target area in the target period is predicted, which solves the technical problem that the existing technology cannot accurately predict the future electricity consumption of a specific area, achieves the purpose of accurately predicting the future electricity consumption of a specific area, and then realizes the technical effect of improving the accuracy of predicting the future electricity consumption of a specific area.

[0047] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation mode:

[0048] Step S1, respectively obtaining the power consumption of different industries in multiple regions within a historical period to obtain historical power consumption time series data;

[0049] In this step, we collect and organize electricity consumption data of different provinces in historical time periods (such as the past five years) and various industries (such as manufacturing, service industry, agriculture, etc.). These electricity consumption data can be used to analyze electricity consumption patterns, trends and cyclical changes to find out the changing patterns of electricity consumption in certain industries in certain provinces in specific seasons or economic cycles, so as to more accurately predict future electricity demand. express, is the electricity consumption of the i-th province, the j-th industry, and the n-th year. Where j∈[1,47] is the industry sector with electricity demand.

[0050] Step S2, determining the total historical electricity consumption time series data of a total area consisting of multiple areas according to the historical electricity consumption time series data;

[0051] In this step, after collecting the time series data of electricity consumption in different provinces, the time series data of these individual provinces are aggregated to obtain the total historical electricity consumption of all provinces. The total historical electricity consumption of all provinces can be obtained using represents, j represents industry category, and n represents year.

[0052] Step S21, predicting the electricity consumption of the total area in the target period according to the historical electricity consumption time series data to obtain the predicted total electricity consumption, including: using the least squares method to construct an electricity consumption function of the total area based on the historical total electricity consumption time series data, wherein the electricity consumption function of the total area is used to describe the change of electricity consumption of the total area over time; predicting the electricity consumption of the total area in the target period according to the electricity consumption function of the total area to obtain the predicted total electricity consumption.

[0053] Optionally, select the least squares method as the tool to construct the electricity consumption forecast function. The least squares method is a statistical method used to find the best fit between the data and the forecast model. Its core is to minimize the sum of the squared errors between the model prediction value and the actual observation value. Based on the historical electricity consumption time series data, the least squares method is used to construct the total area electricity consumption function. This function will describe the law of total electricity consumption over time, which may include long-term trends, seasonal changes, and other cyclical factors.

[0054] Step S22, using the least squares method, based on the historical total electricity consumption time series data, constructing the electricity consumption function of the total area, including: determining the changing trend of the historical total electricity consumption according to the historical total electricity consumption time series data; selecting a basic function that matches the changing trend of the historical total electricity consumption; using the least squares method, according to the historical total electricity consumption time series data, adjusting the values ​​of the parameters in the basic function, and constructing the electricity consumption function of the search total area.

[0055] Optionally, based on the historical total electricity consumption time series data, analyze the change trend of these data over time, which may include long-term growth, seasonal fluctuations or cyclical changes. According to the identified trend of change in the historical total electricity consumption, select an appropriate basic function. The basic function can be a linear, polynomial, exponential or a function with a periodic component. It should be able to reflect and simulate the power demand pattern obtained in the trend analysis. The least squares method, a statistical method, is used to adjust the parameters in the selected basic function to ensure that the function fits the historical total electricity consumption data most accurately.

[0056] Step S23, determining the changing trend of the historical total electricity consumption based on the historical total electricity consumption time series data, including: selecting multiple points in the historical total electricity consumption time series data at preset time intervals; determining the difference between the multiple points; and determining whether the changing trend of the historical total electricity consumption is a linear change or an exponential change based on the difference between the multiple points.

[0057] Optionally, multiple data points are selected from the historical total electricity consumption time series data at preset time intervals (such as every year or every quarter). These points represent the total electricity consumption at different time points, and the difference between the selected data points is calculated. The difference can be calculated as the difference between adjacent points or the average difference within a specific time window. By analyzing these differences, it is determined whether the change trend of the historical total electricity consumption is linear or exponential. A linear trend indicates that electricity consumption increases or decreases evenly over time, while an exponential trend indicates that the rate of increase or decrease accelerates or slows down over time.

[0058] Step S24, selecting a basic function that matches the changing trend of the historical total electricity consumption, including: when the changing trend of the historical total electricity consumption is a linear change, determining a linear regression function as the basic function; or when the changing trend of the historical total electricity consumption is an exponential change, determining an exponential function as the basic function.

[0059] Optionally, based on the historical electricity consumption data, analyze its changing trend over time to determine whether the trend is linear or exponential. A linear trend means that the rate of increase or decrease in electricity consumption is constant, while an exponential trend means that the rate of increase or decrease changes over time, usually showing accelerated growth or decrease. According to the results of the above trend analysis, select a suitable basic function. If the trend of the total historical electricity consumption is linear, then select the linear regression function as the basic function; on the contrary, if the trend is exponential, select the exponential function as the basic function.

[0060] Step S25, predicting the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area, and obtaining the predicted electricity proportion, including: determining the proportion of electricity consumption of different industries in multiple regions in the total area according to the historical electricity consumption time series data, and predicting the proportion of different industries in multiple regions to obtain the predicted proportion of different industries in multiple regions; normalizing the predicted proportion of different industries in multiple regions to obtain the predicted electricity proportion of the target industry in the target area within the target period.

[0061] Optionally, based on historical electricity consumption time series data, calculate the proportion of electricity consumption of different industries in multiple provinces to the total provincial electricity consumption, analyze and predict these historical proportion data, so as to estimate the proportion of electricity demand of different industries in each province during the target period, normalize the predicted proportions of all industries to ensure that the sum of the proportions of all industries is equal to 1, so as to obtain the accurate predicted electricity proportion of a specific industry in a specific province.

[0062] Step S3, determining the proportion of the power consumption of the target industry in the target area to the power consumption of the total area according to the historical power consumption time series data, and obtaining the historical proportion time series data of the target industry in the target area, wherein multiple areas include the target area, and different industries include the target industry;

[0063] In this step, the proportion of electricity consumption of the target industry (such as manufacturing) in the target province (such as Province A) to the total electricity consumption of all provinces in the historical period is calculated. The formula for the proportion of electricity consumption of a certain industry in a certain province is: is the proportion of electricity consumption in the i-th province, the j-th industry, and the n-th year.

[0064] Step S4, predicting the total area power consumption within the target period based on the historical power consumption time series data to obtain the predicted total power consumption;

[0065] In this step, based on the time series data of historical electricity consumption, taking into account various factors such as population and economic growth, the total electricity consumption of all provinces in the target time period (such as the next two years) is predicted. The total electricity consumption of all provinces in the Mth year can be predicted using E M express.

[0066] Step S5, predicting the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area, and obtaining the predicted electricity proportion;

[0067] In this step, based on the historical proportion time series data of a specific industry in a specific province, the proportion of the specific industry in the specific province in the future electricity consumption to the specific industry in all provinces is predicted. For example, assuming that the specific province is Province A and the specific industry is manufacturing, it can be predicted that in a specific time period in the future (such as the next two years), the proportion of the electricity consumption of the manufacturing industry in Province A to the total electricity consumption of the manufacturing industry in all provinces can be predicted. The proportion of the electricity demand standard of a certain industry in a certain province in the Mth year can be used Indicates, i represents province, j represents industry, and n represents year. Based on the historical time series, the proportion of electricity demand in the predicted year P is obtained by the least squares method. ijM .like The standard ratio like The standard ratio This correction ensures that the total sum of the proportions is 1.

[0068] Step S6, predicting the electricity consumption of the target industry in the target area within the time period according to the predicted electricity proportion and the predicted total electricity consumption.

[0069] In this step, the power consumption of a certain industry in a certain province can be obtained based on the predicted proportion of the power consumption of a certain industry in a certain province to the total power consumption of all provinces and the total power consumption of all provinces. represents the proportion of the jth industry in the i-th province in the Mth year, E jM Represents the electricity consumption of the jth industry in the Mth year.

[0070] The above optional implementation method achieves at least the following effects: it solves the technical problem that the prior art cannot accurately predict the future electricity consumption in a specific area, achieves the purpose of accurately predicting the future electricity consumption in a specific area, and further achieves the technical effect of improving the accuracy of predicting the future electricity consumption in a specific area.

[0071] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0072] In this embodiment, a power consumption prediction device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0073] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing a method for predicting power consumption. Figure 2 is a schematic diagram of a power consumption prediction device according to an embodiment of the present invention. Figure 2 As shown, the above-mentioned power consumption prediction device includes an acquisition module 21, a first determination module 22, a second determination module 23, a first prediction module 24, a second prediction module 25, and a third prediction module 26. The device is described below.

[0074] An acquisition module 21 is used to respectively acquire the power consumption of different industries in multiple regions within a historical period to obtain historical power consumption time series data;

[0075] A first determination module 22, connected to the acquisition module 21, is used to determine the historical total power consumption time series data of a total area composed of multiple areas according to the historical power consumption time series data;

[0076] The second determination module 23 is connected to the first determination module 22, and is used to determine the proportion of the power consumption of the target industry in the target area to the power consumption of the total area according to the historical power consumption time series data, and obtain the historical proportion time series data of the target industry in the target area, wherein the multiple areas include the target area, and different industries include the target industry;

[0077] The first prediction module 24 is connected to the second determination module 23 and is used to predict the total power consumption of the area within the target period according to the historical power consumption time series data to obtain the predicted total power consumption;

[0078] The second prediction module 25 is connected to the first prediction module 24 and is used to predict the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area, so as to obtain the predicted electricity proportion;

[0079] The third prediction module 26 is connected to the second prediction module 25, and is used to predict the electricity consumption of the target industry in the target area within the time period according to the predicted electricity proportion and the predicted total electricity consumption.

[0080] In a power consumption prediction device provided by an embodiment of the present invention, an acquisition module is set to respectively acquire the power consumption of different industries in multiple regions within a historical period to obtain historical power consumption time series data; a first determination module is used to determine the historical total power consumption time series data of a total region composed of multiple regions based on the historical power consumption time series data; a second determination module is used to determine the proportion of power consumption of a target industry in a target region in the power consumption of the total region based on the historical power consumption time series data to obtain the historical proportion time series data of the target industry in the target region, wherein the multiple regions include the target region, and the different industries include the target industry; a first prediction module is used to predict the power consumption of the total region within the target period based on the historical power consumption time series data to obtain the predicted total power consumption; a second prediction module is used to predict the proportion of the target industry in the target region within the target period based on the historical proportion time series data of the target industry in the target region to obtain the predicted power proportion; a third prediction module is used to predict the power consumption of the target industry in the target region within the period based on the predicted power proportion and the predicted total power consumption, thereby solving the technical problem that the prior art cannot accurately predict the future power consumption of a specific region, achieving the purpose of accurately predicting the future power consumption of a specific region, and further achieving the technical effect of improving the accuracy of predicting the future power consumption of a specific region.

[0081] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0082] It should be noted that the acquisition module 21, the first determination module 22, the second determination module 23, the first prediction module 24, the second prediction module 25, and the third prediction module 26 correspond to steps S102 to S112 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the modules as part of the device can be run in a computer terminal.

[0083] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0084] The above-mentioned power consumption prediction device may also include a processor and a memory. The acquisition module 21, the first determination module 22, the second determination module 23, the first prediction module 24, the second prediction module 25, the third prediction module 26, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0085] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0086] An embodiment of the present invention provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a method for predicting power consumption is implemented.

[0087] like Figure 3 As shown, an embodiment of the present invention provides an electronic device, the electronic device 10 includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: the memory is used to store a computer program, wherein when the computer program is executed by the processor, the processor implements the above-mentioned power consumption prediction method. The device in this article may be a server, a PC, etc.

[0088] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: computer instructions are executed by a processor to perform the above-mentioned power consumption prediction method.

[0089] 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 a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 code.

[0090] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0095] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0097] It will be appreciated by those skilled in the art 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 a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, 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.

[0098] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for predicting power consumption, characterized in that: include: Obtain the electricity consumption of different industries in multiple regions during the historical period to obtain the historical electricity consumption time series data; Determine the total amount of historical electricity consumption time series data of the total area composed of the multiple areas according to the historical electricity consumption time series data; According to the historical power consumption time series data, determine the proportion of power consumption of a target industry in a target area to the power consumption of the total area, and obtain the historical proportion time series data of the target industry in the target area, wherein the multiple areas include the target area, and the different industries include the target industry; According to the historical power consumption time series data, the power consumption of the total area in the target period is predicted to obtain the predicted total power consumption; According to the historical proportion time series data of the target industry in the target area, the proportion of the target industry in the target area within the target period is predicted to obtain the predicted electricity proportion; The electricity consumption of the target industry in the target area within the time period is predicted based on the predicted electricity proportion and the predicted total electricity consumption.

2. The method according to claim 1, characterized in that According to the historical power consumption time series data, The power consumption of the total area in the target period is predicted to obtain the predicted total power consumption, including: Using the least square method, based on the historical total electricity consumption time series data, constructing the electricity consumption function of the total area, wherein the electricity consumption function of the total area is used to describe the change of electricity consumption of the total area over time; The power consumption of the total area in the target period is predicted according to the power consumption function of the total area to obtain the predicted total power consumption.

3. The method according to claim 2, characterized in that The least square method is used to construct the power consumption function of the total area based on the historical total power consumption time series data, including: Determine the change trend of the historical total electricity consumption according to the historical total electricity consumption time series data; Selecting a basic function that matches the changing trend of the historical total electricity consumption; The least square method is adopted to adjust the values ​​of the parameters in the basic function according to the historical total electricity consumption time series data, and to construct the electricity consumption function of the total search area.

4. The method according to claim 3, characterized in that Determining the change trend of the historical total electricity consumption according to the historical total electricity consumption time series data includes: According to a preset time interval, multiple points are selected from the historical total electricity consumption time series data; determining differences between the plurality of points; According to the differences between the multiple points, it is determined whether the change trend of the historical total electricity consumption is a linear change or an exponential change.

5. The method according to claim 4, characterized in that The selecting of a basic function that matches the changing trend of the historical total electricity consumption includes: In the case where the change trend of the historical total electricity consumption is a linear change, determining a linear regression function as the basic function; Or, when the change trend of the historical total electricity consumption is an exponential change, an exponential function is determined as the basic function.

6. The method according to any one of claims 1 to 5, characterized in that The predicting the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area to obtain the predicted electricity proportion includes: According to the historical power consumption time series data, respectively determine the proportion of power consumption of the different industries in the multiple regions to the power consumption of the total region, and predict the proportion of the different industries in the multiple regions to obtain the predicted proportion of each of the different industries in the multiple regions; The predicted proportions of the different industries in the multiple regions are normalized and adjusted to obtain the predicted electricity proportion of the target industry in the target region during the target period.

7. A power consumption prediction device, characterized in that: include: An acquisition module is used to respectively acquire the power consumption of different industries in multiple regions within a historical period and obtain the historical power consumption time series data; A first determination module is used to determine the historical total power consumption time series data of the total area composed of the multiple areas according to the historical power consumption time series data; A second determination module is used to determine the proportion of the power consumption of the target industry in the target area to the power consumption of the total area according to the historical power consumption time series data, and obtain the historical proportion time series data of the target industry in the target area, wherein the multiple areas include the target area, and the different industries include the target industry; A first prediction module is used to predict the power consumption of the total area within the target period according to the historical power consumption time series data to obtain the predicted total power consumption; A second prediction module is used to predict the proportion of the target industry in the target area within the target period according to the historical proportion time series data of the target industry in the target area, so as to obtain the predicted electricity proportion; The third prediction module is used to predict the electricity consumption of the target industry in the target area within the time period according to the predicted electricity proportion and the predicted total electricity consumption.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the power consumption prediction method described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power consumption prediction method described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions are executed by the processor to implement the power consumption prediction method described in any one of claims 1 to 6.

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