Power utilization adjustment effect prediction method and device and nonvolatile storage medium

Abstract

The invention discloses a power utilization adjustment effect prediction method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring historical power consumption data corresponding to a target area; based on the historical power consumption data, predicting future power consumption data corresponding to the target area by adopting a time sequence model; determining a power utilization change trend corresponding to the target area based on the historical power utilization data; adjusting the future power consumption data based on the power consumption change trend to obtain target future power consumption data of the target area; and determining the power utilization adjustment effect of the target area according to the target future power utilization data and the historical power utilization data. The method solves the technical problem that the prediction of the power utilization adjustment effect is not accurate enough because the prediction of future power utilization data does not consider the actual situation of the region.

Description

Technical Field

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

[0002] In today's context of tight energy resources, in order to make more efficient use of electricity resources, many countries and regions have introduced time-of-use electricity pricing policies to guide electricity consumption behavior. The time-of-use electricity pricing system encourages users to concentrate electricity consumption during low-load periods by raising electricity prices during peak periods and lowering electricity prices during off-peak periods, so as to achieve the purpose of peak shaving and valley filling.

[0003] Most current research focuses on analyzing the impact of time-of-use electricity prices on user electricity consumption behavior, as well as electricity price prediction models based on time series or machine learning methods. However, since the load transfer during peak and valley periods on the user side is affected by many factors, and considering the possibility of multiple causes and the same effect, it is impossible to simply explain the role and effectiveness of time-of-use electricity prices in peak load shaving and valley filling from a data perspective.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device and non-volatile storage medium for predicting power consumption adjustment effects, so as to at least solve the technical problem that the prediction of future power consumption data does not take into account the actual situation of the region, resulting in inaccurate prediction of power consumption adjustment effects.

[0006] According to one aspect of an embodiment of the present invention, a method for predicting the effect of electricity consumption adjustment is provided, including: obtaining historical electricity consumption data corresponding to a target area; based on the historical electricity consumption data, using a time series model to predict future electricity consumption data corresponding to the target area; based on the historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area; based on the electricity consumption change trend, adjusting the future electricity consumption data to obtain target future electricity consumption data for the target area; and determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data.

[0007] Optionally, obtaining historical electricity consumption data corresponding to the target area includes: obtaining initial historical electricity consumption data corresponding to the target area; and deleting abnormal information in the historical electricity consumption data to obtain the historical electricity consumption data.

[0008] Optionally, before using the time series model to predict future electricity consumption data corresponding to the target area based on historical electricity consumption data, it includes: establishing an original time series model; initializing particle swarm optimization parameters and fitness function; based on the particle swarm optimization parameters and fitness function, adjusting model parameters in the original time series model to obtain a time series model.

[0009] Optionally, based on historical electricity consumption data, determine the electricity consumption change trend corresponding to the target area, including: determining the historical peak time period, historical peak electricity quantity, historical valley time period and historical valley electricity quantity according to the historical electricity consumption data; determining the electricity consumption change trend according to the historical peak time period, historical peak electricity quantity, historical valley time period and historical valley electricity quantity.

[0010] Optionally, based on the electricity consumption trend, future electricity consumption data is adjusted to obtain target future electricity consumption data for the target area, including: inputting the electricity consumption trend and future electricity consumption data into a target neural network model to obtain a target future electricity consumption trend, wherein the target neural network model is obtained by training the original neural network model based on historical electricity consumption data.

[0011] Optionally, the electricity consumption adjustment effect of the target area is determined based on the target future electricity consumption data and the historical electricity consumption data, including: determining a first conduction coefficient and a second conduction coefficient based on the historical electricity consumption data, wherein the first conduction coefficient represents the probability of the electricity in the valley period being transferred to other periods, and the second conduction coefficient represents the probability of the electricity in the peak period being transferred to other periods; determining the future electricity peak value and the future electricity valley value in the target future electricity consumption data; predicting the future peak period electricity transfer value and the future valley period electricity transfer value based on the future electricity peak value, the future valley value, the first conduction coefficient and the second conduction coefficient; determining the electricity consumption adjustment effect of the target area based on the future peak period electricity transfer value and the future valley period electricity transfer value.

[0012] According to another aspect of an embodiment of the present invention, a device for predicting electricity consumption adjustment effect is also provided, including: an acquisition module for acquiring historical electricity consumption data corresponding to a target area; a prediction module for predicting future electricity consumption data corresponding to the target area using a time series model based on the historical electricity consumption data; a first determination module for determining the electricity consumption change trend corresponding to the target area based on the historical electricity consumption data; an adjustment module for adjusting the future electricity consumption data based on the electricity consumption change trend to obtain target future electricity consumption data of the target area; and a second determination module for determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data.

[0013] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned power consumption adjustment effect prediction methods.

[0014] According to another aspect of the embodiments of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned power consumption adjustment effect prediction methods is executed.

[0015] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above-mentioned methods for predicting the effect of electricity adjustment is implemented.

[0016] In an embodiment of the present invention, a method for predicting the effect of electricity consumption adjustment is adopted, by obtaining historical electricity consumption data corresponding to a target area; based on the historical electricity consumption data, using a time series model to predict future electricity consumption data corresponding to the target area; based on the historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area; based on the electricity consumption change trend, adjusting the future electricity consumption data to obtain target future electricity consumption data for the target area; determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data, thereby achieving the purpose of improving the accuracy of the prediction of future electricity consumption data, thereby achieving the technical effect of improving the accuracy of the prediction of electricity consumption adjustment effect, and further solving the technical problem that the prediction of future electricity consumption data does not take into account the actual situation of the region, resulting in inaccurate prediction of the electricity consumption adjustment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for predicting the effect of power consumption adjustment is shown;

[0019] Figure 2 is a flow chart of a method for predicting power consumption adjustment effect provided in accordance with an embodiment of the present invention;

[0020] Figure 3 It is a schematic diagram of a future electricity consumption data prediction process in a method for predicting an electricity consumption adjustment effect according to an optional embodiment of the present invention;

[0021] Figure 4 is a flow chart of an implementation method for predicting the effect of power consumption adjustment provided in an optional embodiment of the present invention;

[0022] Figure 5 It is a structural block diagram of a power consumption adjustment effect prediction device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] 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.

[0025] According to an embodiment of the present invention, a method embodiment of a method for predicting the effect of electricity adjustment 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.

[0026] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for predicting the effect of power consumption adjustment. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power adjustment effect prediction method in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the power adjustment effect prediction method of the above-mentioned application program is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0030] Most current research focuses on analyzing the impact of time-of-use electricity prices on user electricity consumption behavior, as well as electricity price prediction models based on time series or machine learning methods. However, since the load transfer during peak and valley periods on the user side is affected by multiple factors, there is the possibility of multiple causes and the same effect, which leads to inaccurate prediction of future electricity consumption data, and thus makes it impossible to predict the role and effectiveness of time-of-use electricity prices in peak shaving and valley filling. In response to the above technical problems, the present invention proposes an embodiment of a method for predicting the effect of electricity adjustment. Figure 2 FIG. 1 is a flow chart of a method for predicting the effect of power consumption adjustment according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0031] Step S202: Acquire historical electricity consumption data corresponding to the target area.

[0032] In this step, the power consumption data may include load data, power consumption equipment information, power consumption time, etc. in the target area. For example, sensor equipment may be used to collect information including load data, weather data, time information data, power consumption equipment information, etc. in the target area. It is also possible to collect power data of retail users in various provinces and bundle and analyze the user data.

[0033] Step S204: Based on the historical electricity consumption data, a time series model is used to predict the future electricity consumption data corresponding to the target area.

[0034] In this step, based on the collected historical electricity consumption data, the electricity consumption data for future periods in the target area can be predicted. A time series model can be used to do this in one fell swoop. For example, an LSTM time series model or an improved time series model PSO-LSTM is used for prediction. Because electricity consumption data changes over time, for example, electricity consumption at night will be less than that during the day, and electricity consumption in summer may be more than in spring. Therefore, the use of a time series model can better and more accurately predict future electricity consumption data. For example, an LSTM model can be used to determine the model parameters in the LSTM model through the PSO (particle swarm optimization) algorithm, and then the historical electricity consumption data can be input into the model to determine the future electricity consumption data of the target area.

[0035] Step S206: determining the electricity consumption change trend corresponding to the target area based on the historical electricity consumption data.

[0036] In this step, the electricity consumption trend corresponding to the target area can be determined based on the historical electricity consumption data. Because different regions may have different electricity consumption characteristics, determining the electricity consumption trend of the target area based on the historical electricity consumption data of the target area can reflect the electricity consumption characteristics of the area, which can be used to subsequently correct the previously predicted future electricity consumption data to make it more accurate.

[0037] Step S208: Based on the electricity consumption change trend, the future electricity consumption data is adjusted to obtain target future electricity consumption data of the target area.

[0038] In this step, the future electricity consumption data can be adjusted based on the electricity consumption trend to make it more accurate. Specifically, the artificial network neural model (BP neural network) can be used to predict and correct it according to the peak-to-valley power ratio and the power change curve trend in previous years to obtain the target future electricity consumption data.

[0039] Step S210, determining the power consumption adjustment effect of the target area according to the target future power consumption data and the historical power consumption data.

[0040] In this step, in order to balance the electricity consumption time, some methods are usually used to guide users to use electricity, such as setting the electricity price higher during the peak electricity consumption period to guide users to use electricity during the valley electricity consumption period. Based on the target future electricity consumption data and historical electricity consumption data, it can be determined whether the adjustment effect of electricity consumption guidance in the target area is effective.

[0041] Through the above steps, the purpose of improving the accuracy of future electricity consumption data prediction can be achieved, thereby achieving the technical effect of improving the accuracy of electricity adjustment effect prediction, and further solving the technical problem that the prediction of future electricity consumption data does not take into account the actual regional situation, resulting in inaccurate prediction of electricity adjustment effect.

[0042] As an optional embodiment, obtaining historical electricity consumption data corresponding to the target area includes: obtaining initial historical electricity consumption data corresponding to the target area; and deleting abnormal information in the historical electricity consumption data to obtain the historical electricity consumption data.

[0043] Optionally, there may be some abnormal data in the data collected. Therefore, the data can be preprocessed first to remove abnormal information in the historical power consumption data to avoid affecting subsequent data prediction. For example, collect data, use sensor equipment to collect load data, weather data, time information data, power equipment information, etc., and then preprocess the data, that is, remove data with obvious deviations and problems, normalize the data, and scale it to the same range. Among them, the normalized data processing can use the formula Among them, X max and X min Respectively represent the maximum and minimum values ​​of the indicator x; x N It represents the normalized load data, with a value between 0 and 1. This is to eliminate the influence of dimension.

[0044] As an optional embodiment, before using a time series model to predict future electricity consumption data corresponding to a target area based on historical electricity consumption data, it includes: establishing an original time series model; initializing particle swarm optimization parameters and a fitness function; and adjusting model parameters in the original time series model based on the particle swarm optimization parameters and the fitness function to obtain a time series model.

[0045] Figure 3 is a schematic diagram of a future power consumption data prediction process in a power consumption adjustment effect prediction method provided in an optional embodiment of the present invention, such as Figure 3As shown, an LSTM model, i.e., the original time series model, can be established. Then, the model parameters that need to be optimized need to be determined, and a particle swarm optimization algorithm, i.e., a PSO algorithm, can be used. Among them, the model parameters may include the number of neurons L1, the learning rate ε, and the number of training iterations k. The PSO parameters can be initialized first and the fitness function of the particles can be determined, wherein the PSO parameters may include the initial velocity and position of the particles, the learning weights, the number of training times and the scale, etc.; the MAPE value (mean absolute percentage error) of the prediction model is used as the fitness function of the particles to find the optimal model parameters. The fitness values ​​of the particles under different parameters can be compared. Find the individual optimal position and the global optimal position, and update the optimal fitness value. Then it can be determined whether the number of training times in the PSO parameters has been reached. If reached, the obtained optimal parameters are passed to the LSTM model, and then the LSTM model is used for prediction. If the requirements are not met, continue to find the optimal parameters. Specifically, the three hyperparameters of the LSTM model can be used as the parameters to be optimized for PSO, and the fitness function is set to: F(x) = min(MAPE), where the mean absolute percentage error (MAPE) refers to the average value of the percentage error between the actual value and the predicted value of electricity consumption. Among them, Y a Indicates the actual value of the load, Y p represents the predicted value of the load, and n represents the number of predicted load points.

[0046] As an optional embodiment, based on historical electricity consumption data, the electricity consumption change trend corresponding to the target area is determined, including: determining the historical peak time period, historical peak electricity quantity, historical valley time period and historical valley electricity quantity according to the historical electricity consumption data; determining the electricity consumption change trend according to the historical peak time period, historical peak electricity quantity, historical valley time period and historical valley electricity quantity.

[0047] Optionally, based on historical electricity consumption data, peak time periods, valley time periods, peak electricity consumption, and valley electricity consumption are determined. With these data, the electricity consumption trend in the target area can be determined. The electricity consumption trend can reflect the electricity consumption characteristics of the area, making it easier to subsequently correct the predicted future electricity consumption data.

[0048] As an optional embodiment, based on the electricity consumption trend, future electricity consumption data is adjusted to obtain target future electricity consumption data for the target area, including: inputting the electricity consumption trend and future electricity consumption data into a target neural network model to obtain a target future electricity consumption trend, wherein the target neural network model is obtained by training the original neural network model based on historical electricity consumption data.

[0049] Optionally, based on the peak-to-valley electricity ratio in previous years and the trend of the market electricity change curve, a neural network model (bp neural network) is used to predict and correct future electricity consumption data; based on the data collected in previous surveys, that is, the historical electricity consumption data is used as the hidden layer, data training is carried out to obtain appropriate weights, that is, the parameters in the neural network model, and the electricity consumption change trend and future electricity consumption data are used as the input layer to finally calculate the target future electricity consumption data.

[0050] For example, the BP neural network is used to correct the previous prediction and predict the load demand for the next year. The implementation steps of the BP neural network model are as follows: (1) Initialization: process the input data into data between (-1, 1) and set relevant values; (2) Input sample data and the corresponding expected output, learn each sample, and calculate the actual output and the output of the hidden layer node based on the input sample; (3) Calculate the error: calculate the difference between the actual output and the expected output to calculate the error between the output layer and the hidden layer; (4) Make corrections based on the obtained error and the input connection weights of each neuron; (5) Calculate the global error E and determine whether E reaches the preset accuracy or number of learning times; if so, end the algorithm; otherwise, return to the second step and continue learning.

[0051] Use the empirical formula to determine the number of hidden layer neurons: Among them, n is the number of hidden layer neurons, n is the number of input layer nodes, m is the number of output layer nodes, and a is an integer between 1 and 10. The tanh activation function of the bp neural network is:

[0052] As an optional embodiment, the power consumption adjustment effect of the target area is determined according to the target future power consumption data and the historical power consumption data, including: determining a first conduction coefficient and a second conduction coefficient according to the historical power consumption data, wherein the first conduction coefficient represents the probability of the power in the valley period being transferred to other periods, and the second conduction coefficient represents the probability of the power in the peak period being transferred to other periods; determining the future power peak value and the future power valley value in the target future power consumption data; predicting the future peak power transfer value and the future valley power transfer value according to the future power peak value, the future valley power value, the first conduction coefficient and the second conduction coefficient; determining the power consumption adjustment effect of the target area according to the future peak power transfer value and the future valley power transfer value.

[0053] Optionally, the first transmission coefficient and the second transmission coefficient may be determined according to the formula λ 削峰 =λ1-λ2,

[0054] λ 填谷 =1 / λ3-1 / λ4, where

[0055]

[0056] Among them, λ 削峰 The transfer coefficient of electricity from peak hours to other hours, i.e. the second transfer coefficient; λ 填谷 It represents the conduction coefficient of electricity in other time periods transferred to the valley and deep valley periods, that is, the first conduction coefficient; λ1 and λ2 represent the ratio of the peak and peak periods of electricity in the Nth year and the N+1th year to the electricity in other time periods; λ3 and λ4 represent the ratio of the peak, peak and flat periods of electricity in the Nth year and the N+1th year to the electricity in other time periods; Q 尖，N , Q 峰，N , Q 平，N , Q 谷，N , Q 深谷，N They represent the peak, high, flat, low and deep valley periods of electricity in the Nth year respectively; Q 尖，N+1 , Q 峰，N+1 , Q 平，N+1 , Q 谷，N+1 , Q 深谷，N+1 They represent the peak, high, flat, low and deep valley electricity in the N+1 year respectively. Based on the transfer coefficient of the peak and valley electricity to other periods, and considering the actual factors of the target area, the future peak and valley electricity is predicted, and the calculation formula for the effect of shifting peak and filling valley is set: Q 转移,峰 =λ 削峰 *(Q 预测,平 +Q 预测,谷 +Q 预测,深谷 ), Q 转移,谷 =λ 填谷 *(Q 预测,尖 +Q 预测,峰 +Q 预测,平 ), where Q 转移,峰 , Q 转移,谷 Indicates the predicted future peak and valley period power transfer value; Q 预测,尖 , Q 预测,峰 , Q 预测,平 , Q 预测,谷 , Q 预测,深谷 Indicates the estimated power consumption value for each future period. Based on the above predicted value, the power consumption adjustment effect can be determined. For example, if the peak power consumption in the predicted value is lower than the peak power consumption in the historical power consumption data, it can be considered that the power consumption effect of guiding users is better.

[0057] Figure 4 is a flowchart of a method for predicting the effect of power consumption adjustment according to an optional embodiment of the present invention. Figure 4As shown in the figure, the peak and valley load power data of the province in previous years can be obtained, and other information including load data, weather data, time information data, etc. can be collected and preprocessed, and then the data is bundled for analysis, that is, based on the preprocessed data, the user's load data is summed to obtain the total load curve. Then, the improved time series prediction model is used to analyze the future load demand in the region, that is, the particle swarm algorithm (PSO) is first used to optimize the three hyperparameters of the LSTM model, namely the number of neurons, learning rate, and number of training times, and the optimized hyperparameter data is substituted into the LSTM model to predict the green electricity demand based on the load bundling data, time information, weather information, etc. Then, the peak and valley load data of previous years are used as the training set to train the artificial neural network, and the trained model is used to predict the proportion of peak and valley electricity consumption, and then the peak and valley load data of the target area in the next year is determined. That is, according to the peak-valley electricity ratio and the trend of the market electricity change curve in previous years, the artificial network neural model (BP neural network) is used to predict and correct it; the data collected in the previous survey is used as the hidden layer, and data training is performed based on the data of the same period. After obtaining the appropriate weight, the peak-valley electricity change ratio of the same period before and after the time-sharing transaction in previous years is used as the input layer, and finally the peak-valley load demand of users during the next peak summer period is calculated. Then the effect of electricity guidance is determined.

[0058] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0059] Through the description of the above implementation methods, those skilled in the art can clearly understand that the power consumption adjustment effect prediction method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0060] According to an embodiment of the present invention, a device for implementing the above method is also provided. Figure 5 is a structural block diagram of a power consumption adjustment effect prediction device provided according to an embodiment of the present invention. Figure 5 As shown, the device includes: an acquisition module 502, a prediction module 504, a first determination module 506, an adjustment module 508 and a second determination module 510. The device is described below.

[0061] The acquisition module 502 is used to acquire the historical electricity consumption data corresponding to the target area.

[0062] The prediction module 504 is connected to the acquisition module 502 and is used to predict the future power consumption data corresponding to the target area based on the historical power consumption data by using a time series model.

[0063] The first determination module 506 is connected to the prediction module 504 and is used to determine the electricity consumption change trend corresponding to the target area based on the historical electricity consumption data.

[0064] The adjustment module 508 is connected to the first determination module 506 and is used to adjust the future electricity consumption data based on the electricity consumption change trend to obtain the target future electricity consumption data of the target area.

[0065] The second determination module 510 is connected to the adjustment module 508 and is used to determine the power consumption adjustment effect of the target area according to the target future power consumption data and the historical power consumption data.

[0066] It should be noted that the acquisition module 502, prediction module 504, first determination module 506, adjustment module 508 and second determination module 510 correspond to steps S202 to S210 in the embodiment, and the examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0067] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0068] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power consumption adjustment effect prediction method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned power consumption adjustment effect prediction method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0069] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the historical electricity consumption data corresponding to the target area; based on the historical electricity consumption data, use the time series model to predict the future electricity consumption data corresponding to the target area; based on the historical electricity consumption data, determine the electricity consumption change trend corresponding to the target area; based on the electricity consumption change trend, adjust the future electricity consumption data to obtain the target future electricity consumption data of the target area; determine the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data.

[0070] Optionally, the processor may also execute program code of the following steps: obtaining historical electricity consumption data corresponding to the target area, including: obtaining initial historical electricity consumption data corresponding to the target area; deleting abnormal information in the historical electricity consumption data to obtain the historical electricity consumption data.

[0071] Optionally, the processor may also execute program code of the following steps: before using a time series model to predict future electricity consumption data corresponding to a target area based on historical electricity consumption data, the steps include: establishing an original time series model; initializing particle swarm optimization parameters and a fitness function; and adjusting model parameters in the original time series model based on the particle swarm optimization parameters and the fitness function to obtain a time series model.

[0072] Optionally, the processor may also execute program code for the following steps: based on historical electricity consumption data, determine the electricity consumption change trend corresponding to the target area, including: determining the historical peak time period, historical peak power, historical valley time period and historical valley power according to the historical electricity consumption data; determining the electricity consumption change trend according to the historical peak time period, historical peak power, historical valley time period and historical valley power.

[0073] Optionally, the processor may also execute program code for the following steps: based on the electricity consumption change trend, adjusting future electricity consumption data to obtain target future electricity consumption data for the target area, including: inputting the electricity consumption change trend and future electricity consumption data into a target neural network model to obtain a target future electricity consumption trend, wherein the target neural network model is obtained by training the original neural network model based on historical electricity consumption data.

[0074] Optionally, the processor may also execute program code for the following steps: determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data, including: determining a first conduction coefficient and a second conduction coefficient based on the historical electricity consumption data, wherein the first conduction coefficient represents the probability of the electricity in the valley period being transferred to other periods, and the second conduction coefficient represents the probability of the electricity in the peak period being transferred to other periods; determining the future electricity peak value and the future electricity valley value in the target future electricity consumption data; predicting the future peak period electricity transfer value and the future valley period electricity transfer value based on the future electricity peak value, the future valley value, the first conduction coefficient and the second conduction coefficient; determining the electricity consumption adjustment effect of the target area based on the future peak period electricity transfer value and the future valley period electricity transfer value.

[0075] By adopting the embodiment of the present invention, a method for predicting the effect of electricity consumption adjustment is provided, which comprises obtaining historical electricity consumption data corresponding to a target area; predicting future electricity consumption data corresponding to the target area based on the historical electricity consumption data by adopting a time series model; determining the electricity consumption change trend corresponding to the target area based on the historical electricity consumption data; adjusting the future electricity consumption data based on the electricity change trend to obtain target future electricity consumption data for the target area; determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data, thereby achieving the purpose of improving the accuracy of the prediction of the future electricity consumption data, thereby realizing the technical effect of improving the accuracy of the prediction of the electricity consumption adjustment effect, and further solving the technical problem that the prediction of the future electricity consumption data does not take the actual situation of the region into consideration, resulting in the prediction of the electricity consumption adjustment effect being inaccurate.

[0076] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0077] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the power consumption adjustment effect prediction method provided in the above embodiment.

[0078] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0079] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining historical electricity consumption data corresponding to the target area; based on the historical electricity consumption data, using a time series model to predict future electricity consumption data corresponding to the target area; based on the historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area; based on the electricity consumption change trend, adjusting the future electricity consumption data to obtain target future electricity consumption data for the target area; and determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data.

[0080] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining historical electricity consumption data corresponding to the target area, including: obtaining initial historical electricity consumption data corresponding to the target area; deleting abnormal information in the historical electricity consumption data to obtain historical electricity consumption data.

[0081] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: before using a time series model to predict future electricity consumption data corresponding to a target area based on historical electricity consumption data, including: establishing an original time series model; initializing particle swarm optimization parameters and a fitness function; based on the particle swarm optimization parameters and the fitness function, adjusting the model parameters in the original time series model to obtain a time series model.

[0082] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: based on historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area, including: determining the historical peak time period, historical peak power, historical valley time period and historical valley power according to the historical electricity consumption data; determining the electricity consumption change trend according to the historical peak time period, historical peak power, historical valley time period and historical valley power.

[0083] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: based on the electricity consumption change trend, adjusting the future electricity consumption data to obtain target future electricity consumption data for the target area, including: inputting the electricity consumption change trend and the future electricity consumption data into a target neural network model to obtain a target future electricity consumption trend, wherein the target neural network model is obtained by training the original neural network model based on historical electricity consumption data.

[0084] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining the power consumption adjustment effect of the target area based on the target future power consumption data and the historical power consumption data, including: determining a first conduction coefficient and a second conduction coefficient based on the historical power consumption data, wherein the first conduction coefficient represents the probability of the power in the valley period being transferred to other periods, and the second conduction coefficient represents the probability of the power in the peak period being transferred to other periods; determining the future power peak value and the future power valley value in the target future power consumption data; predicting the future peak period power transfer value and the future valley period power transfer value based on the future power peak value, the future valley value, the first conduction coefficient and the second conduction coefficient; determining the power consumption adjustment effect of the target area based on the future peak period power transfer value and the future valley period power transfer value.

[0085] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: obtaining historical electricity consumption data corresponding to the target area; based on the historical electricity consumption data, using a time series model to predict future electricity consumption data corresponding to the target area; based on the historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area; based on the electricity consumption change trend, adjusting the future electricity consumption data to obtain target future electricity consumption data for the target area; determining the electricity consumption adjustment effect of the target area based on the target future electricity consumption data and the historical electricity consumption data.

[0086] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0087] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting the effect of electricity adjustment, characterized in that: include: Obtain historical electricity consumption data corresponding to the target area; Based on the historical electricity consumption data, a time series model is used to predict future electricity consumption data corresponding to the target area; Based on the historical electricity consumption data, determining the electricity consumption change trend corresponding to the target area; Based on the electricity consumption change trend, the future electricity consumption data is adjusted to obtain target future electricity consumption data for the target area; The power consumption adjustment effect of the target area is determined according to the target future power consumption data and the historical power consumption data.

2. The method according to claim 1, characterized in that The obtaining of historical electricity consumption data corresponding to the target area includes: Acquire initial historical electricity consumption data corresponding to the target area; The abnormal information in the historical electricity usage data is deleted to obtain the historical electricity usage data.

3. The method according to claim 1, characterized in that Before predicting the future electricity consumption data corresponding to the target area based on the historical electricity consumption data using a time series model, the method includes: Build the original time series model; Initialize particle swarm optimization parameters and fitness function; Based on the particle swarm optimization parameters and the fitness function, the model parameters in the original time series model are adjusted to obtain the time series model.

4. The method according to claim 1, characterized in that The determining, based on the historical electricity consumption data, the electricity consumption change trend corresponding to the target area includes: Determine the historical peak time period, historical peak power consumption, historical valley time period and historical valley power consumption according to the historical power consumption data; The power consumption change trend is determined according to the historical peak time period, the historical peak power consumption, the historical valley time period and the historical valley power consumption.

5. The method according to claim 1, characterized in that: The adjusting the future electricity consumption data based on the electricity consumption change trend to obtain the target future electricity consumption data of the target area includes: The electricity consumption change trend and the future electricity consumption data are input into a target neural network model to obtain the target future electricity consumption trend, wherein the target neural network model is obtained by training an original neural network model based on the historical electricity consumption data.

6. The method according to any one of claims 1 to 5, characterized in that: The determining the power consumption adjustment effect of the target area according to the target future power consumption data and the historical power consumption data includes: Determine a first conduction coefficient and a second conduction coefficient according to the historical electricity consumption data, wherein the first conduction coefficient represents the probability of the electricity in the valley period being transferred to other periods, and the second conduction coefficient represents the probability of the electricity in the peak period being transferred to other periods; Determine a future peak value and a future valley value of electricity consumption in the target future electricity consumption data; Predicting a future peak power transfer value and a future valley power transfer value according to the future power peak value, the future valley power value, the first conduction coefficient, and the second conduction coefficient; The power consumption adjustment effect of the target area is determined according to the future peak period power transfer value and the future valley period power transfer value.

7. A device for predicting the effect of power consumption adjustment, characterized in that: include: An acquisition module is used to acquire historical electricity consumption data corresponding to a target area; A prediction module, configured to predict future electricity consumption data corresponding to the target area using a time series model based on the historical electricity consumption data; A first determination module, configured to determine a power consumption change trend corresponding to the target area based on the historical power consumption data; An adjustment module, configured to adjust the future electricity consumption data based on the electricity consumption change trend to obtain target future electricity consumption data for the target area; The second determination module is used to determine the power consumption adjustment effect of the target area according to the target future power consumption data and the historical power consumption data.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the power consumption adjustment effect prediction method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the power consumption adjustment effect prediction method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the effect of electricity consumption adjustment described in any one of claims 1 to 6 is implemented.

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