A method for monitoring electric energy consumption

Abstract

The present application discloses a method for monitoring power energy consumption, which relates to the technical field of electric power monitoring. The method for monitoring power energy consumption includes: obtaining historical power consumption data based on a regional power box; obtaining a historical power consumption load curve based on the historical power consumption data; obtaining a future power consumption load curve based on the historical power consumption load curve, historical weather, and future weather; and obtaining peak power consumption periods and off-peak power consumption periods in the future based on the future power consumption load curve. By analyzing historical power consumption data to calculate real-time active power, establishing a historical power consumption load curve, and then predicting the power consumption load in a future period according to the change trend of the real-time power consumption load within a historical period and weather environment factors, the present application improves the accuracy of load prediction, that is, reduces the lag of mutation prediction.

Description

Technical Field

[0001] This application relates to the technical field of electric power monitoring, and specifically to a method for monitoring electric power energy consumption. Background Art

[0002] Modern electric power monitoring methods use big data technology to mine a large amount of electric power data, identify energy efficiency problems, and optimize electric power usage patterns. This not only improves the accuracy and efficiency of monitoring but also enables the automatic identification, prediction, and optimization of electricity consumption patterns, providing specific energy-saving solutions. For example, optimizing electric power load, reasonably allocating energy usage, and reducing electricity consumption during peak hours. These solutions help users minimize energy waste to the greatest extent while ensuring normal production or life, assist users in achieving energy-saving goals, and optimize electric power management.

[0003] Currently, during the long-term monitoring of electric power energy, there are generally sudden changes between peak and trough hours of electricity consumption. Excessive load pressure on the power system during peak hours can lead to unstable grid operation; while during trough hours, the electricity demand is relatively low. If the grid still maintains a high operating state, it will cause energy waste. Existing technologies have a lag in responding to the sudden changes between peak and trough hours of electricity consumption through real-time monitoring of electric power load, increasing the risk of power supply. Summary of the Invention

[0004] The purpose of this application is to provide a method for monitoring electric power energy consumption to solve the technical problem of the lag in the existing method for monitoring electric power energy consumption in response to sudden changes between peak and trough hours of electricity consumption.

[0005] To achieve the above purpose, this application provides the following technical solutions:

[0006] A method for monitoring electric power energy consumption, including:

[0007] Based on the regional power box, obtain historical electricity consumption data; the historical electricity consumption data includes at least historical current value, historical voltage value, and historical electric power value; the regional power box is the power box in any region;

[0008] Based on the historical electricity consumption data, obtain the historical electric power consumption load curve;

[0009] Based on the historical electric power consumption load curve, historical weather, and future weather, obtain the future electric power consumption load curve; the historical weather and the future weather are obtained in advance; the weather includes at least outdoor temperature and air quality;

[0010] Based on the future electric power consumption load curve, obtain the future peak hours and trough hours of electricity consumption;

[0011] Obtaining a future power consumption load curve based on the historical power consumption load curve, historical weather, and future weather includes:

[0012] Dividing the historical power consumption load curve into multiple historical curve segments based on a preset period;

[0013] Obtaining a moment trend value based on each historical curve segment; the moment trend value is at least used to characterize the change trend of the instantaneous active power at the corresponding moment in each historical curve segment;

[0014] Obtaining the instantaneous active power at a future moment based on the moment trend value;

[0015] Obtaining an adjustment coefficient based on the future weather and the historical weather; the adjustment coefficient is at least used to characterize the change amplitude between the future weather and the historical weather;

[0016] Obtaining a corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient;

[0017] Obtaining a future power consumption load curve based on the corrected instantaneous active power;

[0018] The calculation formula for obtaining the moment trend value based on each historical curve segment is as follows:

[0019]

[0020] Wherein, represents the moment trend value at the th sampling moment in the future period; represents the number of historical curve segments; represents the th historical curve segment and the th sampling moment of the instantaneous active power; represents the +1th historical curve segment and the th sampling moment of the instantaneous active power; represents a normalization function for normalizing the value within the brackets to the range of [-1, 1];

[0021] The calculation formula for obtaining the instantaneous active power at a future moment based on the moment trend value is as follows:

[0022]

[0023] Wherein, represents the instantaneous active power at the th sampling moment in the future period; represents the The instantaneous active power at a sampling moment; Indicates the moment trend value at the th sampling moment within the future period;

[0024] Obtaining an adjustment coefficient based on the future weather and the historical weather includes:

[0025] Based on the future weather, obtaining a first outdoor temperature; the first outdoor temperature is the average outdoor temperature within the future period;

[0026] Based on the historical weather, obtaining a plurality of second outdoor temperatures; the second outdoor temperature is the average outdoor temperature within the period corresponding to any historical curve segment;

[0027] Based on the first outdoor temperature and each second outdoor temperature, obtaining a first coefficient;

[0028] Based on the first coefficient, obtaining an adjustment coefficient;

[0029] The calculation formula for obtaining the first coefficient based on the first outdoor temperature and each second outdoor temperature is as follows:

[0030]

[0031] Wherein, G represents the first coefficient; Indicates the number of historical curve segments; Indicates the first outdoor temperature; Indicates the second outdoor temperature corresponding to the xth historical curve segment; Indicates a normalization function for normalizing the value within the brackets to the range of [0, 1].

[0032] As a specific solution in the technical solution of this application, obtaining the adjustment coefficient based on the first coefficient includes:

[0033] Based on the historical weather, obtaining a plurality of first air qualities; the first air quality is the air quality corresponding to any historical curve segment;

[0034] Based on each first air quality and the corresponding historical curve segment, obtaining a linear function; the ordinate of the linear function is the instantaneous active power, and the abscissa is the air quality;

[0035] Based on the future weather, obtaining a second air quality;

[0036] Based on the second air quality and the linear function, obtaining an adjustment constant;

[0037] Based on the first coefficient and the adjustment constant, obtaining the adjustment coefficient.

[0038] As a specific solution in the technical solution of this application, the calculation formula for obtaining the corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient is as follows:

[0039]

[0040]

[0041] Wherein, represents the corrected instantaneous active power at the -th sampling moment within the future period; represents the instantaneous active power at the -th sampling moment within the future period; G represents the first coefficient; represents the adjustment constant; represents the function value obtained by substituting the second air quality into the linear function; represents the function value obtained by substituting the air quality closest to the future period time sequence into the linear function.

[0042] As a specific solution in the technical solution of this application, obtaining the future peak electricity consumption period and valley electricity consumption period based on the future power consumption load curve includes:

[0043] Based on the iterative self-organizing data analysis algorithm, adaptively clustering the future power consumption load curve to obtain multiple time periods;

[0044] Based on each time period, obtaining the corresponding fluctuation degree value; the fluctuation degree value is at least used to characterize the change degree of the future power consumption load curve within the corresponding time period;

[0045] If the fluctuation degree value of a certain data segment is greater than the preset value, then this data segment is used as the peak electricity consumption period or the valley electricity consumption period.

[0046] Compared with the prior art, the beneficial effects of this application are:

[0047] This application calculates the real-time active power by analyzing historical electricity consumption data, establishes a historical power consumption load curve, and then predicts the power consumption load in the future period according to the change trend of the real-time power consumption load within the historical period and the weather environment factors, improving the accuracy of the predicted load, that is, reducing the lag of the mutation prediction. Then, the peak and valley electricity consumption periods are identified, so as to optimize the power consumption scheduling and demand response in advance based on the predicted peak and valley load periods of electricity consumption, improving the stability of the power grid and the energy utilization rate. Description of the Drawings

[0048] Figure 1Schematic flowchart of a power energy consumption monitoring method proposed in an embodiment of the present application;

[0049] Figure 2 Schematic diagram of a historical power consumption load curve proposed in an embodiment of the present application;

[0050] Figure 3 Schematic diagram of dividing a historical power consumption load curve into multiple historical curve segments proposed in an embodiment of the present application. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0052] Terms such as "first" and "second" in the specification of the embodiments of the present application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. For example, the first outdoor temperature and the second outdoor temperature mentioned below belong to different outdoor temperatures. It should be understood that such outdoor temperatures can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. 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 modules does not necessarily have to be limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. The division of modules in the embodiments of the present application is only a logical division. In actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces. The indirect coupling or communication connection between modules may be electrical or other similar forms, which are not limited in the embodiments of the present application. And the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed to multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0053] To solve the technical problem of the lag in the existing power energy consumption monitoring method in the background art during the mutation between peak and trough electricity consumption periods, an embodiment of a power energy consumption monitoring method is proposed in this application, as Figure 1 shown. This power energy consumption monitoring method includes steps S100 to S400.

[0054] Step S100: Obtain historical electricity consumption data based on the regional power box.

[0055] In this embodiment, the historical electricity consumption data at least includes historical current value, historical voltage value, and historical electric power value. The regional power box is a power box in any region. A power box (or distribution box or control box) is the core facility for connecting and distributing power equipment in the power system. By installing various monitoring sensors and devices in the power box, the specific situation of power consumption can be effectively monitored. Then, in order to predict the consumption load in the future period, it is necessary to obtain data such as real-time current, voltage, and electric power of multiple historical periods in this region through the power box. In this embodiment, the period can be any time, for example: the period can be one day, one week, or one month. To facilitate the understanding of the power energy consumption monitoring method proposed in this application, subsequent examples of the embodiments of this application will be given with one week as the period, which does not mean that the power energy consumption monitoring method proposed in this application is only applicable to the application scenario with a period of one week.

[0056] In this embodiment, the region can be a community, or a certain household in the community, or all communities in a certain town, etc. That is to say, in this embodiment, no restrictions are imposed on the size range of the region.

[0057] Step S200: Obtain the historical power consumption load curve based on the historical electricity consumption data.

[0058] In this embodiment, the historical power consumption load curve refers to the curve of the instantaneous active power of this region changing with time. That is to say, in this embodiment, as Figure 2 shown, the ordinate of the historical power consumption load curve is the instantaneous active power, and the abscissa is time. In the embodiment of this application, any reasonable method can be used to obtain the historical power consumption load curve based on the historical electricity consumption data. In a specific embodiment of this application, step S200, obtaining the historical power consumption load curve based on the historical electricity consumption data, includes steps S210 to S240.

[0059] Step S210: Obtain the apparent power based on the historical electricity consumption data.

[0060] It should be clear that apparent power consists of two parts: active power and reactive power. Active power refers to the part of power that directly does work, such as making a lamp shine or a motor rotate; while reactive power is the part of power stored in the circuit but not directly doing work. Specifically, in step S210, based on the historical electricity consumption data, the calculation formula for apparent power is obtained as follows:

[0061]

[0062] Wherein, represents the apparent power at the i-th moment; represents the voltage at the i-th moment in the historical electricity consumption data; represents the current at the i-th moment in the historical electricity consumption data.

[0063] Step S220: Based on the apparent power, obtain the power factor.

[0064] It should be clear that the power factor reflects the efficiency of converting electrical energy into useful work energy. If the power factor is larger, it means that most of the electrical energy in the power system is effectively converted into actual work; while if the power factor is smaller, it means that most of the electrical energy is wasted on reactive power and the system efficiency is low. Specifically, in step S220, based on the apparent power, the calculation formula for the power factor is obtained as follows:

[0065]

[0066] Wherein, represents the power factor at the i-th moment; represents the apparent power of the th phase at the corresponding moment; represents the apparent power of the th phase at the corresponding moment; represents the phase angle of the th phase at the corresponding moment; represents the phase angle of the th phase at the corresponding moment.

[0067] Step S230: Based on the power factor, obtain the instantaneous active power.

[0068] It should be clear that the instantaneous active power refers to the active power actually consumed in the circuit at a certain moment. It represents the energy consumed by resistive loads in the circuit at a specific moment, that is, the energy effectively converted into actual work. Specifically, in step S230, based on the power factor, the calculation formula for the instantaneous active power is obtained as follows:

[0069]

[0070] Among them, represents the instantaneous active power at the i-th moment; represents the apparent power at the i-th moment; represents the power factor at the i-th moment.

[0071] Step S240: Based on the instantaneous active power, obtain the historical power consumption load curve.

[0072] As can be seen from the foregoing, the historical power consumption load curve refers to the curve of the instantaneous active power in this area changing with time. If the instantaneous active power at the i-th moment can be calculated , then the historical power consumption load curve can be obtained. The obtained historical power consumption load curve is as Figure 2 shown.

[0073] Step S300: Based on the historical power consumption load curve, historical weather and future weather, obtain the future power consumption load curve.

[0074] In this embodiment, the historical weather and the future weather are obtained in advance, and the weather includes at least the outdoor temperature and air quality. It should be clear that obtaining the future weather is a mature technology (for example, obtaining the future weather based on weather forecasts), which will not be elaborated here. The power energy consumption in most areas is closely related to the outdoor temperature. For example, if the temperature suddenly rises or drops, the power energy consumption will increase. Based on this, in this embodiment, step S300, based on the historical power consumption load curve, historical weather and future weather, to obtain the future power consumption load curve, includes steps S310 to S360.

[0075] Step S310: Divide the historical power consumption load curve into multiple historical curve segments based on a preset period.

[0076] As can be seen from the foregoing, in the embodiments of this application, the preset period can be one day, one week or one month, etc. For the sake of understanding, as Figure 2 shown, one week (i.e., 168 hours) is used as the preset period. In this embodiment, based on the preset period (for example: one week) to divide the historical power consumption load curve into multiple historical curve segments, then each historical curve segment is as Figure 3 shown. Since there is a certain periodicity in the power consumption load curve, for example: the power consumption is more during the day and less at night, so the power consumption load of each day shows a "U" - shaped trend, and the power consumption load curves of multiple historical curve segments show the same cosine fluctuation characteristics. As Figure 3As shown in the figure, for the convenience of observation, the power consumption load curves of multiple cycles are translated correspondingly here. It can be seen that the cosine fluctuation characteristics of each historical curve segment are basically similar. In this embodiment, when predicting the power consumption situation in the future cycle based on the cosine fluctuation characteristics of each historical curve segment and combining with the actual weather, the prediction result can be made more accurate.

[0077] Step S320: Based on each historical curve segment, obtain the moment trend value.

[0078] In this embodiment, the moment trend value is at least used to characterize the instantaneous active power change trend at the corresponding moment in each historical curve segment. It should be clear that since there is a certain trend in the power consumption load curve (i.e., each historical curve segment) within each cycle over time. Then based on the above principle, for a certain sampling moment in the future cycle, the instantaneous active power change trend corresponding to the same sampling moment in each historical cycle can be analyzed longitudinally for prediction. Specifically, in step S320, the calculation formula for obtaining the moment trend value based on each historical curve segment is as follows:

[0079]

[0080] Wherein, represents the moment trend value at the th sampling moment in the future cycle; represents the number of historical curve segments; represents the th historical curve segment and the th sampling moment's instantaneous active power; represents the +1th historical curve segment and the th sampling moment's instantaneous active power; represents the normalization function, which is used to normalize the value in the parentheses to the range of [-1, 1].

[0081] Step S330: Based on the moment trend value, obtain the instantaneous active power at the future moment.

[0082] It should be clear that if the moment trend value is positive, it means that the instantaneous active power is likely to increase at the corresponding moment in the future cycle. If the moment trend value is negative, it means that the instantaneous active power is likely to decrease at the corresponding moment in the future cycle. In this embodiment, in step S330, the calculation formula for obtaining the instantaneous active power at the future moment based on the moment trend value is as follows:

[0083]

[0084] Wherein, represents the th sampling moment in the future cycleThe instantaneous active power at a sampling moment; Denote the instantaneous active power at the sampling moment in the historical curve segment closest to the future cycle time series; Denote the trend value of the moment at the sampling moment in the future cycle.

[0085] It should be noted that since the actual electricity consumption in the region is greatly affected by the weather environment, the future instantaneous active power predicted simply by the historical instantaneous active power does not take into account the influence of the weather environment. Here, the relationship between the historical temperature change and / or historical air quality and the instantaneous active power can be used to correct the instantaneous active power in the future cycle to improve its accuracy.

[0086] Step S340: Obtain an adjustment coefficient based on the future weather and the historical weather.

[0087] In this embodiment, the adjustment coefficient is at least used to characterize the change amplitude of the future weather and the historical weather. Generally, whether the temperature rises or falls, it will be accompanied by an increase in the power consumption load (i.e., the instantaneous active power). Therefore, the adjustment coefficient can be determined by the outdoor temperature in the future cycle relative to the outdoor temperature in the historical cycle. Specifically, step S340, obtaining an adjustment coefficient based on the future weather and the historical weather, includes steps S341 to S344.

[0088] Step S341: Obtain a first outdoor temperature based on the future weather.

[0089] In this embodiment, the first outdoor temperature is the average outdoor temperature in the future cycle. Obtaining the average temperature value (i.e., the first outdoor temperature) from multiple temperature values (i.e., the outdoor temperatures at each moment in the future cycle) is a mature technology and will not be elaborated here.

[0090] Step S342: Obtain multiple second outdoor temperatures based on the historical weather.

[0091] In this embodiment, the second outdoor temperature is the average outdoor temperature in the cycle corresponding to any historical curve segment. Obtaining the average temperature value (i.e., the second outdoor temperature) from multiple temperature values (i.e., the outdoor temperatures at each moment in the historical cycle corresponding to a certain historical curve segment) is a mature technology and will not be elaborated here.

[0092] Step S343: Obtain a first coefficient based on the first outdoor temperature and each second outdoor temperature.

[0093] It should be clear that the magnitude of the temperature change is positively correlated with the instantaneous active power. Therefore, a first coefficient can be obtained based on the first outdoor temperature and each second outdoor temperature. Specifically, in step S343, the calculation formula for obtaining the first coefficient based on the first outdoor temperature and each second outdoor temperature is as follows:

[0094]

[0095] where G represents the first coefficient; represents the number of historical curve segments; represents the first outdoor temperature; represents the second outdoor temperature corresponding to the x-th historical curve segment; represents a normalization function for normalizing the value within the brackets to the range of [0, 1].

[0096] Step S344: Obtain an adjustment coefficient based on the first coefficient.

[0097] In the embodiments of the present application, the first coefficient can be used as the adjustment coefficient to further correct the instantaneous active power and obtain the corrected instantaneous active power. It should be noted that the reduction of air quality will also affect the power consumption load of the region. If the air contains a large amount of pollutants or dust, the workload of equipment such as air conditioners and air purifiers will increase. Since the change in air quality and power consumption show a simple linear correlation, the relationship between the average PM2.5 content and the average instantaneous active power in multiple historical periods can be fitted by a linear regression model to obtain a linear function, and then the average predicted value of the PM2.5 content in the future period is substituted to obtain the simulated average instantaneous active power in the future period In another embodiment of the present application, in order to more accurately correct the instantaneous active power. Step S344, obtaining an adjustment coefficient based on the first coefficient, includes steps S345 to S349.

[0098] Step S345: Obtain multiple first air qualities based on historical weather.

[0099] In this embodiment, the first air quality is the air quality corresponding to any historical curve segment.

[0100] Step S346: Obtain a linear function based on each first air quality and the corresponding historical curve segment.

[0101] In this embodiment, the ordinate of the linear function is the instantaneous active power, and the abscissa is the air quality.

[0102] Step S347: Obtain a second air quality based on the future weather.

[0103] Step S348: Obtain an adjustment constant based on the second air quality and the linear function.

[0104] In this embodiment, the calculation formula for obtaining the adjustment constant based on the second air quality and the linear function is as follows:

[0105]

[0106] Where, represents the function value obtained by substituting the second air quality into the linear function; represents the function value obtained by substituting the air quality closest to the future cycle time series into the linear function.

[0107] Step S349: Obtain the adjustment coefficient based on the first coefficient and the adjustment constant.

[0108] In this embodiment, the calculation formula for obtaining the adjustment coefficient based on the first coefficient and the adjustment constant is as follows:

[0109] /

[0110] Where, represents the adjustment coefficient; represents the adjustment constant; G represents the first coefficient; represents the instantaneous active power at the th sampling moment within the future cycle.

[0111] Step S350: Obtain the corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient.

[0112] In this embodiment, the calculation formula for obtaining the corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient is as follows:

[0113]

[0114] Where, represents the corrected instantaneous active power at the th sampling moment within the future cycle; represents the instantaneous active power at the th sampling moment within the future cycle; G represents the first coefficient; represents the adjustment constant; represents the adjustment coefficient.

[0115] Step S360: Obtain the future power consumption load curve based on the corrected instantaneous active power.

[0116] It should be noted that if the instantaneous active power at each moment in the future period (i.e., the corrected instantaneous active power in the above text) can be obtained, then the future power consumption load curve can be obtained.

[0117] Step S400: Based on the future power consumption load curve, obtain the future peak power consumption period and the off-peak power consumption period.

[0118] In the embodiments of the present application, the future peak power consumption period and the off-peak power consumption period can be identified by visual observation. In order to automatically obtain the future peak power consumption period and the off-peak power consumption period based on the future power consumption load curve, in an embodiment of the present application, step S400, based on the future power consumption load curve, obtaining the future peak power consumption period and the off-peak power consumption period, includes steps S410 to S430.

[0119] Step S410: Based on the iterative self-organizing data analysis algorithm, perform adaptive clustering on the future power consumption load curve to obtain multiple time periods.

[0120] It should be clear that the iterative self-organizing data analysis algorithm (Iterative Self-Organizing Data Analysis Technique Algorithm, ISODATA) is a clustering algorithm that adds two operations of "merging" and "splitting" to the clustering results and sets the algorithm operation control parameters on the basis of the k-means algorithm. And dividing the future power consumption load curve into multiple time periods by the iterative self-organizing data analysis algorithm is a mature technology.

[0121] Step S420: Based on each time period, obtain the corresponding fluctuation degree value.

[0122] In this embodiment, the fluctuation degree value is at least used to characterize the change degree of the future power consumption load curve in the corresponding time period. Since the peak power consumption period and the off-peak power consumption period are accompanied by the up and down fluctuations of the corresponding values of the curve (i.e., the corrected instantaneous active power), using the ISODATA algorithm to perform adaptive clustering on the prediction curve here, then each clustering result cluster represents a time period with similar fluctuation degree. Then for any time period r of the clustering result, there is:

[0123]

[0124] Wherein, represents the fluctuation degree value of the time period r; represents the average value of all instantaneous active powers in the clustering result time period r; represents the average value of the instantaneous active powers of the entire future power consumption load curve; represents the maximum instantaneous active power in the clustering result time period r; represents the minimum instantaneous active power in the clustering result time period r. Therefore represents the average instantaneous active power difference between the time period r and the entire future power consumption load curve. The larger the value, the greater the corresponding difference and the greater the degree of fluctuation. represents the range of instantaneous active power in the time period r. The larger the range, the greater the degree of fluctuation, and it is more likely to be a peak power consumption period or a valley power consumption period.

[0125] Step S430: If the degree of fluctuation value of a certain data segment is greater than the preset value, then regard this data segment as a peak power consumption period or a valley power consumption period.

[0126] In the embodiments of the present application, the degree of fluctuation value can be normalized so that the degree of fluctuation value is within the range of [0, 1]. After multiple verifications, it is found that if the preset value is taken as 0.88, the selected time period can accurately represent the peak power consumption period or the valley power consumption period. Of course, in other embodiments of the present application, the preset value can also be set according to other requirements. For example, the preset value can be 0.85 or 0.90, etc.

[0127] It should be noted that during the peak power demand period, it is necessary to rely on high-response-capability generator sets (such as natural gas units or coal-fired units, etc.) to quickly supplement the grid load. At this time, low-cost power sources (such as nuclear energy, wind power, solar energy) may not be sufficient to meet the demand. Therefore, it is necessary to appropriately dispatch generator sets with higher fuel costs. During the load valley period, low-cost or renewable energy power generation (such as wind power, solar energy, nuclear energy, etc.) should be given priority, and at the same time, energy storage devices should be fully utilized to store the excess electric energy during the valley period for use during the peak load period. The power company can adjust the operation sequence of the generator sets according to the electricity price and load prediction to reduce the power generation cost during the peak period and avoid over-reliance on high-cost generator sets. To achieve the purpose of reducing the peak load pressure and reducing energy waste.

[0128] The power energy consumption monitoring method proposed in the present application calculates the real-time active power by analyzing historical power consumption data, establishes a historical power consumption load curve, and then predicts the power consumption load in the future period according to the change trend of the real-time power consumption load within the historical period and the weather environment factors, improving the accuracy of the predicted load, that is, reducing the lag of the mutation prediction. Then identify the peak and valley power consumption periods, so as to optimize the power consumption scheduling and demand response in advance based on the predicted peak and valley load periods, improving the stability of the power grid and the energy utilization rate.

[0129] It should be clear that the computer-readable storage medium in this application includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The 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, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory, or other memory technologies, compact disc read-only memory, digital versatile disc, or other optical storage, magnetic cassette tapes, disk storage, or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media such as modulated data signals and carrier waves.

[0130] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described methods, devices, and equipment can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0132] In the several embodiments provided by the embodiments of this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or modules can be in electrical, mechanical, or other forms.

[0133] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, in each embodiment of the present application, each functional module can be integrated into a processing module, can exist separately physically for each module, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0135] In the above embodiment, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product.

[0136] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital video disc), or a semiconductor medium (for example, a solid-state drive (SSD)).

[0137] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles of the present application.

Claims

1. A method for monitoring power energy consumption, characterized in that, Including: Obtain historical power consumption data based on the regional power box; The historical power consumption data at least includes historical current value, historical voltage value, and historical electric power value; The regional power box is a power box in any region; Obtain the historical power consumption load curve based on the historical power consumption data; Obtain the future power consumption load curve based on the historical power consumption load curve, historical weather, and future weather; the historical weather and the future weather are obtained in advance; the weather at least includes outdoor temperature and air quality; Obtain the future peak power consumption period and off-peak power consumption period based on the future power consumption load curve; The obtaining of the future power consumption load curve based on the historical power consumption load curve, historical weather, and future weather includes: Divide the historical power consumption load curve into multiple historical curve segments based on a preset period; Obtain the moment trend value based on each historical curve segment; the moment trend value is at least used to characterize the instantaneous active power change trend at the corresponding moment in each historical curve segment; Obtain the instantaneous active power at a future moment based on the moment trend value; Obtain an adjustment coefficient based on the future weather and the historical weather; the adjustment coefficient is at least used to characterize the change amplitude between the future weather and the historical weather; Obtain the corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient; Obtain the future power consumption load curve based on the corrected instantaneous active power; The calculation formula for obtaining the moment trend value based on each historical curve segment is as follows: Among them, represents the moment trend value at the th sampling moment in the future period; represents the number of historical curve segments; represents the th instantaneous active power at the th sampling moment in the th historical curve segment; The th instantaneous active power at the th sampling moment in the +1th historical curve segment; represents a normalization function used to normalize the value within the parentheses to the range of [-1, 1]; The calculation formula for obtaining the instantaneous active power at a future moment based on the moment trend value is as follows: Among them, represents the instantaneous active power at the -th sampling moment in the future period; represents the instantaneous active power at the -th sampling moment in the historical curve segment closest to the future period timing; represents the moment trend value at the -th sampling moment in the future period; The obtaining of the adjustment coefficient based on the future weather and the historical weather includes: Obtain the first outdoor temperature based on the future weather; the first outdoor temperature is the average outdoor temperature within a future period; Obtain multiple second outdoor temperatures based on the historical weather; the second outdoor temperatures are the average outdoor temperatures within the periods corresponding to any historical curve segments; Obtain the first coefficient based on the first outdoor temperature and each second outdoor temperature; Obtain the adjustment coefficient based on the first coefficient; The calculation formula for obtaining the first coefficient based on the first outdoor temperature and each second outdoor temperature is as follows: Among them, G represents the first coefficient; represents the number of historical curve segments; represents the first outdoor temperature; represents the second outdoor temperature corresponding to the x-th historical curve segment; represents a normalization function used to normalize the value within the brackets to the range of [0, 1].

2. The power energy consumption monitoring method according to claim 1, characterized in that, The obtaining of the adjustment coefficient based on the first coefficient includes: Obtain multiple first air qualities based on the historical weather; the first air qualities are the air qualities corresponding to any historical curve segments; Obtain a linear function based on each first air quality and the corresponding historical curve segment; the ordinate of the linear function is the instantaneous active power, and the abscissa is the air quality; Obtain the second air quality based on the future weather; Obtain the adjustment constant based on the second air quality and the linear function; Obtain the adjustment coefficient based on the first coefficient and the adjustment constant.

3. The power energy consumption monitoring method according to claim 2, characterized in that The calculation formula for obtaining the corrected instantaneous active power based on the instantaneous active power at the future moment and the adjustment coefficient is as follows: Among them, represents the corrected instantaneous active power at the -th sampling moment in the future period; represents the instantaneous active power at the -th sampling moment in the future period; G represents the first coefficient; represents the adjustment constant; represents the function value obtained by substituting the second air quality into the linear function; represents the function value obtained by substituting the air quality closest to the time sequence of the future period into the linear function.

4. The power energy consumption monitoring method according to claim 1, wherein The obtaining of the future peak power consumption period and off-peak power consumption period based on the future power consumption load curve includes: Based on the iterative self-organizing data analysis algorithm, adaptively cluster the future power consumption load curve to obtain multiple time periods; Based on each time period, obtain the corresponding fluctuation degree value; the fluctuation degree value is at least used to characterize the change degree of the future power consumption load curve within the corresponding time period; If the fluctuation degree value of a certain data segment is greater than the preset value, then regard this data segment as a peak power consumption period or a valley power consumption period.

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