Electricity consumption data generation method, household electricity consumption load prediction method, device, equipment and storage medium

Abstract

The invention discloses a power consumption data generation method, a household power consumption load prediction method and device, equipment and a storage medium. The power consumption data generation method comprises the steps of obtaining target data influencing power consumption of a simulated electric appliance; target information is generated, electric appliance action information is determined by using a preset model according to the target information and the target data, and the target information comprises an event jump probability and an action occurrence probability when an event occurs; and outputting electrical load data by using the analog electrical appliance and the electrical appliance action information. According to the technical scheme provided by the embodiment of the invention, the preset model is utilized to simulate the action of the electric appliance, and then the simulated electric appliance is utilized to reasonably generate the electrical load data of various types of electric appliances according to the action of the electric appliance, so that valuable data support and reference information are provided for an artificial intelligence system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computers, and in particular to a power consumption data generation method, a household power consumption load prediction method, device and equipment, and a storage medium. BACKGROUND

[0002] In today's era of rapid development of intelligent technology, artificial intelligence (AI) systems play an increasingly important role in the field of household power consumption. Based on historical data of household power consumption, AI systems can use machine learning and data mining techniques to predict future household power consumption demand and optimize power consumption plans. By analyzing the rules and trends in historical data, AI systems can provide more reasonable and efficient power consumption plans for households to help them save energy costs.

[0003] However, when collecting historical data of household power consumption, sensitive information such as household power consumption load data may be collected, including power consumption habits and device usage. Users may have reservations about providing household power consumption data due to personal privacy and data security concerns. AI systems usually need a large amount of data to train and optimize models to achieve accurate predictions and intelligent decision-making. Traditional power consumption load data generation methods generate poor quality data, which severely affects the generalization ability and application effect of AI systems. SUMMARY

[0004] The present application provides a power consumption data generation method, a household power consumption load prediction method, device and equipment, and a storage medium to solve the problem of lack of power consumption load data and poor quality.

[0005] In a first aspect, the present application provides a power consumption data generation method, comprising:

[0006] Obtaining target data that affects the power consumption of a simulated electrical appliance;

[0007] Generating target information and using a preset model to determine electrical appliance action information based on the target information and the target data, wherein the target information includes event jump probability and action occurrence probability at the time of event occurrence;

[0008] Using the simulated electrical appliance and the electrical appliance action information to output power consumption load data.

[0009] In a second aspect, the present application provides a power consumption data generation method, comprising:

[0010] Obtaining target power consumption load data, which includes power consumption load data obtained by the power consumption data generation method of the first aspect;

[0011] The target electricity load data and the preset load prediction model are used to determine the household electricity load prediction data for future periods.

[0012] Thirdly, this application provides an electricity consumption data generation device, comprising:

[0013] The data acquisition module is used to acquire target data that affects the electricity consumption of analog electrical appliances;

[0014] An appliance action information determination module is used to generate target information and determine appliance action information based on the target information and target data using a preset model. The target information includes the event jump probability and the action occurrence probability when the event occurs.

[0015] The power consumption data output module is used to output power load data using the analog electrical appliance and the appliance's operating information.

[0016] Fourthly, this application provides a household electricity load prediction device, comprising:

[0017] The power load acquisition module is used to acquire target power load data, wherein the target power load data includes power load data obtained by the power data generation method described in the first aspect;

[0018] The data prediction module is used to determine the household electricity load prediction data for future periods using the target electricity load data and a preset load prediction model.

[0019] Fifthly, this application provides an electronic device comprising:

[0020] At least one processor;

[0021] and memory that is communicatively connected to at least one processor;

[0022] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the electricity data generation method of the first aspect and / or the household electricity load forecasting method of the second aspect.

[0023] In a sixth aspect, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the electricity data generation method of the first aspect and / or the household electricity load prediction method of the second aspect.

[0024] The power consumption data generation scheme provided in the application obtains target data affecting the power consumption of a simulated electrical appliance, generates target information, and determines electrical appliance action information according to the target information and the target data by using a preset model, wherein the target information includes event jump probability and action occurrence probability at the time of event occurrence, and the power consumption load data is output by using the simulated electrical appliance and the electrical appliance action information. By adopting the technical scheme, the preset model is used to simulate the electrical appliance action, and the simulated electrical appliance reasonably generates the power consumption load data of various types of electrical appliances according to the electrical appliance action, thereby providing valuable data support and reference information for an artificial intelligence system.

[0025] The household power consumption load prediction scheme provided in the application obtains target power consumption load data, the target power consumption load data including the power consumption load data obtained by the power consumption data generation method in the first aspect, and determines household power consumption load prediction data of a household in a future period by using the target power consumption load data and a preset load prediction model. By adopting the technical scheme, the preset load prediction model is used to accurately predict the household power consumption load according to the target power consumption load data.

[0026] It should be understood that the content described in this part is not intended to identify key or important features of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 is a flowchart of a power consumption data generation method according to an embodiment of the application;

[0029] Figure 2 is a flowchart of a power consumption data generation method according to an embodiment of the application;

[0030] Figure 3 is a schematic diagram of a power consumption data generation system according to an embodiment of the application;

[0031] Figure 4 is a flowchart of a household power consumption load prediction method according to an embodiment of the application;

[0032] Figure 5 is a structural schematic diagram of a power consumption data generation device according to an embodiment of the application;

[0033] Figure 6 is a structural schematic diagram of a household electricity load prediction device according to Embodiment Five of the present application;

[0034] Figure 7 is a structural schematic diagram of an electronic device according to Embodiment Six of the present application. DETAILED DESCRIPTION

[0035] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the 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 persons skilled in the art without creative work should fall within the scope of protection of the present application.

[0036] It should be noted that the terms “first”, “second”, and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In the description of the present application, “a plurality of” means two or more, unless otherwise specified. “And / or”, which describes the association relationship between objects, means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. The character “ / ” generally represents an “or” relationship between the associated objects. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0037] Embodiment One

[0038] Figure 1 A flowchart of a power consumption data generation method is provided for Embodiment One of the present application. The present embodiment can be applicable to the case of generating power consumption data. The method can be executed by a power consumption data generation device, which can be realized in the form of hardware and / or software. The power consumption data generation device can be configured in an electronic device, which can be composed of two or more physical entities or one physical entity.

[0039] As Figure 1As shown, the power consumption data generation method provided by the embodiment of the present application specifically comprises the following steps:

[0040] S101, target data affecting power consumption of a simulated electrical appliance is acquired.

[0041] In the embodiment, the power consumption load data can be generated by simulating the virtual electrical appliance. First, the target data affecting power consumption of the simulated electrical appliance can be acquired, which can include weather data, temperature data, time data, etc.

[0042] S102, target information is generated, and an electrical appliance action information is determined according to the target information and the target data by using a preset model, wherein the target information includes event jump probability and action occurrence probability at event occurrence.

[0043] In the embodiment, the target information and the target data can be input into the preset model, so that the electrical appliance action information can be obtained. The electrical appliance action information includes turning on, turning off and adjusting gears of the electrical appliance, and the preset model includes a large language model and a reinforcement learning model.

[0044] S103, power consumption load data is output by using the simulated electrical appliance and the electrical appliance action information.

[0045] In the embodiment, the power consumption load data can be output by using the simulated electrical appliance according to the electrical appliance action information. For example, if the electrical appliance action information is turning on of a lamp, the power consumption load data of the lamp is output by using the simulated lamp.

[0046] The power consumption data generation method provided by the embodiment of the present application acquires target data affecting power consumption of a simulated electrical appliance, generates target information, and determines electrical appliance action information according to the target information and the target data by using a preset model, wherein the target information includes event jump probability and action occurrence probability at event occurrence. The power consumption load data is output by using the simulated electrical appliance and the electrical appliance action information. The technical solution of the embodiment of the present application simulates the electrical appliance action by using the preset model, and then generates the power consumption load data of various types of electrical appliances by using the simulated electrical appliance according to the electrical appliance action, thereby providing valuable data support and reference information for an artificial intelligence system.

[0047] Optionally, the step of determining the electrical appliance action information according to the target information and the target data by using the preset model comprises: determining a next event of a current event and an action in the next event according to a target matrix and the target data by using a preset sequential decision model, so as to obtain the electrical appliance action information, wherein the target information includes the target matrix, the target matrix includes a state transition probability matrix and an action selection probability matrix, the event jump probability is included in the state transition probability matrix, and the action occurrence probability at event occurrence is included in the action selection probability matrix.

[0048] Specifically, the target matrix can be generated according to different scenarios, such as various types of families, including single working family, three-person family with working parents and school-going children, and retired couple family, and the like. The state transition probability matrix can be used to describe the probability of entering the next event from the current event. The probability in the matrix can not be a fixed value, but a distribution, which will be different at different times and target data. Taking the example of entering the midnight event from the evening activity event, if the next day is a weekday, the probability of entering the midnight activity before 23 o'clock from the evening activity will be larger, and if the next day is a weekend, the probability of entering the midnight activity before 23 o'clock from the evening activity will be smaller. The action selection probability matrix can be used to describe the probability of selecting each action under the current event. Similarly, the probability in the matrix can also be a distribution, which is affected by time and target data. For example, if the current event is midnight activity and the target data shows that the next day is a weekday, the later the time, the greater the probability of selecting the sleep action. The preset sequential decision model of each type of family can be different from each other. The preset sequential decision model can be used to determine the next event of the current event and the action in the next event according to the target matrix, the target data and the time, so as to obtain the electric appliance action information. The action includes an electricity-using action and a non-electricity-using action. The event can be understood as a life event. Each time period can be matched with a corresponding event. Each event can correspond to at least one action. The time period range corresponding to each event is not strictly fixed, and can be expanded or reduced to a certain extent due to environmental factors and social factors. The events and actions can be shown in the following Table 1 event action table:

[0049] Table 1 event action table

[0050]

[0051] Embodiment Two

[0052] Figure 2 A flowchart of a power consumption data generation method provided for Embodiment Two of the present application is shown. The technical solution of the present application is further optimized on the basis of the above-mentioned optional technical solutions, and a specific way of generating power consumption data is given.

[0053] Optionally, the determining the next event of the current event and the action in the next event according to the target matrix and the target data by using the preset sequential decision model to obtain the appliance action information comprises: determining whether to enter the next event according to the target data and the current event at the current time by using a Markov random decision process; if yes, determining a first target probability from the state transition probability matrix, and determining an event corresponding to the first target probability as the next event of the current event; determining a second target probability from the action selection probability matrix according to the target data by using the Markov random decision process, and determining an action corresponding to the second target probability as the action in the next event; and outputting the appliance action information, wherein the appliance action information comprises the action and a target appliance involved in the action. In this way, the action in the event is determined by using the Markov random decision process, and reasonable estimation of appliance power consumption is realized.

[0054] Optionally, the outputting the power consumption load data by using the simulated appliance and the appliance action information comprises: simulating at least one of turning on, turning off and adjusting a gear of the corresponding simulated appliance according to the appliance action information to output the power consumption load data.

[0055] Optionally, after the outputting the power consumption load data by using the simulated appliance and the appliance action information, the method further comprises: updating a current state of the simulated appliance to the target data. In this way, the current state of the simulated appliance is updated to the target data, and the rationality of the appliance action information can be further ensured.

[0056] As shown in Figure 2 The method for generating power consumption data provided by the second embodiment of the present application specifically comprises the following steps:

[0057] S201, obtaining target data affecting power consumption of a simulated appliance.

[0058] Optionally, the target data comprises environmental data and social factor data, the environmental data comprises indoor environmental data and outdoor environmental data, and the indoor environmental data comprises a current state of the simulated appliance.

[0059] Further, the social factor data comprises working hours, holiday hours and social activities, and the outdoor environmental data comprises temperature, illumination, wind speed, humidity, weather, altitude and season.

[0060] Specifically, the indoor environmental data comprises temperature, illumination and the current state of the simulated appliance, etc. The current state of the simulated appliance comprises starting, standby, turning off and a gear in which the simulated appliance is located, etc. Figure 3Fig. 1 is a schematic diagram of a power consumption data generation system. The social factor data includes whether the current time is a weekday, a normal weekend, a large holiday, a large event (such as a sports event or a concert), a family gathering, or a travel, etc. As shown in Fig. 1, the power consumption data generation system can include an environmental data collection device, a social factor updating platform, a power consumption behavior generation platform, an electrical appliance load characteristic simulation device, and a power consumption load data collection device. The target data affecting the simulation of electrical appliance power consumption can be obtained by using the environmental data collection device. Figure 3

[0061] S202, target information is generated, and a Markov random decision process is used to determine whether to enter the next event according to the target data and the current event at the current time. If yes, step 203 is performed, and if no, step 202 is performed.

[0062] Specifically, after receiving the target data each time, based on the current event, the Markov random decision process can be used to determine whether to enter the next event according to the target data. The Markov random decision process belongs to a preset sequential decision model.

[0063] S203, a first target probability is determined from the state transition probability matrix, and an event corresponding to the first target probability is determined as the next event of the current event.

[0064] Specifically, the Markov random decision process is used to query the probability of entering the next event in the state probability matrix, so as to determine the first target probability, and the event corresponding to the first target probability is determined as the next event of the current event.

[0065] S204, a second target probability is determined from the action selection probability matrix according to the target data by using the Markov random decision process, and an action corresponding to the second target probability is determined as the action in the next event.

[0066] S205, electrical appliance action information is output, wherein the electrical appliance action information includes an action and a target electrical appliance involved in the action.

[0067] Specifically, as shown in Fig. 2, the power consumption behavior generation platform can be used to perform steps 202 to 205. Figure 3

[0068] S206, at least one of turning on, turning off, and adjusting a gear of a corresponding simulation electrical appliance is simulated according to the electrical appliance action information, so as to output power consumption load data.

[0069] Specifically, Figure 3 ​​The electrical appliance load characteristic simulation device in the electrical appliance load characteristic simulation device can be understood as an electrical appliance load characteristic simulation device, which can simulate the load characteristics of various household electrical appliances during opening, closing and running to output electrical load data. The electrical load data acquisition device can collect electrical load data from the electrical appliance load characteristic simulation device in real time and record into a database.

[0070] S207, updating the current state of the simulated electrical appliance to the target data.

[0071] Specifically, as shown in Figure 3 The current state of the simulated electrical appliance can be transmitted to the environmental data acquisition device by using the electrical appliance load characteristic simulation device

[0072] The electrical appliance data generation method provided by the embodiment of the present application realizes reasonable speculation of electrical appliance power consumption by using Markov random decision process to randomly decide the electrical appliance behavior under different environments and social influence factors, and realizes automatic generation of electrical load data by generating the electrical load data behind the electrical appliance behavior, thereby providing data support for intelligent energy management.

[0073] Embodiment three

[0074] Figure 4 A flowchart of a household electrical load prediction method is provided for the embodiment three of the present application. The embodiment can be applicable to the case of predicting household electrical load. The method can be executed by a household electrical load prediction device. The household electrical load prediction device can be realized in the form of hardware and / or software. The household electrical load prediction device can be configured in an electronic device. The electronic device can be composed of two or more physical entities, or can be composed of one physical entity.

[0075] As shown in Figure 4 The household electrical load prediction method provided by the embodiment three of the present application specifically includes the following steps:

[0076] S301, obtaining target electrical load data. The target electrical load data includes electrical load data obtained by the electrical appliance data generation method described in the above embodiment.

[0077] In the embodiment, the target electrical load data includes electrical load data obtained by the electrical appliance data generation method described in the above embodiment, and / or user historical actual electrical load data.

[0078] S302, determining household electrical load prediction data of a household in a future period by using the target electrical load data and a preset load prediction model.

[0079] In the embodiment, the future time period includes future minute-level, hour-level and day-level time periods after the current time. The minute-level is, for example, 1 minute, 5 minutes, 15 minutes, etc., the hour-level is, for example, 1 hour, 4 hours, etc., and the day-level is, for example, 1 day (24 hours), etc.

[0080] The household electricity load prediction method provided in the embodiment of the application obtains target electricity load data, the target electricity load data including electricity load data obtained by the electricity data generation method described in the above embodiment, and determines household electricity load prediction data of a future time period of a household by using the target electricity load data and a preset load prediction model. The technical solution of the embodiment of the application accurately predicts the household electricity load by using the preset load prediction model according to the target electricity load data.

[0081] Embodiment Four

[0082] Figure 5 A structural schematic diagram of an electricity data generation device provided in the fourth embodiment of the application is shown in FIG. 4. As shown in FIG. 4, the device includes a data acquisition module 401, an appliance action information determination module 402 and an electricity data output module 403, wherein: Figure 5

[0083] The data acquisition module is configured to acquire target data affecting electricity consumption of a simulated appliance.

[0084] The appliance action information determination module is configured to generate target information, and determine appliance action information according to the target information and the target data by using a preset model, wherein the target information includes event jump probability and action occurrence probability at event occurrence.

[0085] The electricity data output module is configured to output electricity load data by using the simulated appliance and the appliance action information.

[0086] The electricity data generation device provided in the embodiment of the application simulates appliance action by using a preset model, and then generates electricity load data of various types of appliances by using a simulated appliance according to the appliance action, thereby providing valuable data support and reference information for an artificial intelligence system.

[0087] Optionally, the appliance action information determination module includes:

[0088] The appliance action information determination unit is configured to determine next event of a current event and action in the next event by using a preset sequential decision model according to a target matrix and the target data, so as to obtain appliance action information, wherein the target information includes the target matrix, the target matrix includes a state transition probability matrix and an action selection probability matrix, the event jump probability is included in the state transition probability matrix, and action occurrence probability at event occurrence is included in the action selection probability matrix.​

[0089] Optionally, the determining the next event of the current event and the action in the next event according to the target matrix and the target data by using the preset sequential decision model to obtain the appliance action information comprises: determining whether to enter the next event according to the target data and the current event at the current time by using a Markov random decision process; if yes, determining a first target probability from the state transition probability matrix, and determining an event corresponding to the first target probability as the next event of the current event; determining a second target probability from the action selection probability matrix according to the target data by using the Markov random decision process, and determining an action corresponding to the second target probability as the action in the next event; and outputting the appliance action information, wherein the appliance action information comprises the action and a target appliance involved in the action.

[0090] Optionally, the power consumption data output module is specifically configured to simulate at least one of turning on, turning off and adjusting a gear of a corresponding simulation appliance according to the appliance action information, to output the power consumption load data.

[0091] Optionally, the device further comprises:

[0092] an updating module configured to update a current state of the simulation appliance to the target data after the simulation appliance and the appliance action information are used to output the power consumption load data.

[0093] Optionally, the target data comprises environmental data and social factor data, the environmental data comprises indoor environmental data and outdoor environmental data, and the indoor environmental data comprises the current state of the simulation appliance.

[0094] Further, the social factor data comprises working hours, holiday hours and social activities, and the outdoor environmental data comprises temperature, illumination, wind speed, humidity, weather, altitude and season.

[0095] The power consumption data generation device provided in the embodiments of the present application can execute the power consumption data generation method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0096] Embodiment Five

[0097] Figure 6 A structural schematic diagram of a household power consumption load prediction device provided in Embodiment Five of the present application is shown in FIG. 5. As shown in FIG. 5, the device comprises a power consumption load acquisition module 501 and a data prediction module 502, wherein: Figure 6

[0098] ​The electric load acquisition module is configured to acquire target electric load data, wherein the target electric load data comprises electric load data obtained by the electric data generation method described in the above embodiments.

[0099] The data prediction module is configured to determine household electric load prediction data of the household in a future period by using the target electric load data and a preset load prediction model.

[0100] The household electric load prediction device provided by the embodiments of the present application can accurately predict the household electric load by using the preset load prediction model according to the target electric load data.

[0101] Embodiment six

[0102] Figure 7 A structural schematic diagram of an electronic device 60 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0103] As shown in Figure 7 The electronic device 60 includes at least one processor 61, and a memory, such as a read-only memory (ROM) 62, a random access memory (RAM) 63, etc., which are in communication with the at least one processor 61, wherein the memory stores computer programs that can be executed by the at least one processor. The processor 61 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 62 or loaded from the storage unit 68 into the random access memory (RAM) 63. In the RAM 63, various programs and data required for the operation of the electronic device 60 can also be stored. The processor 61, the ROM 62, and the RAM 63 are connected to each other through a bus 64. An input / output (I / O) interface 65 is also connected to the bus 64.

[0104] A plurality of components in the electronic device 60 are connected to the I / O interface 65, including: an input unit 66, such as a keyboard, a mouse, etc.; an output unit 67, such as various types of displays, speakers, etc.; a storage unit 68, such as a magnetic disk, an optical disk, etc.; and a communication unit 69, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 69 allows the electronic device 60 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0105] The processor 61 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 61 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 61 performs various methods and processes described above, such as the electricity consumption data generation method and / or the household electricity load prediction method.

[0106] In some embodiments, the electricity consumption data generation method and / or the household electricity load prediction method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 68. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 60 via the ROM 62 and / or the communication unit 69. When the computer program is loaded onto the RAM 63 and executed by the processor 61, one or more steps of the electricity consumption data generation method and / or the household electricity load prediction method described above can be performed. Alternatively, in other embodiments, the processor 61 can be configured to perform the electricity consumption data generation method and / or the household electricity load prediction method by any other appropriate means, such as by means of firmware.

[0107] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0108] A computer program for implementing the methods of the present application can be written in any combination of one or more programming languages. The computer program can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, causes the functions / operations specified in the flow charts and / or block diagrams to be implemented. The computer program can be executed in whole on the machine, partially on the machine, partially on the machine as a stand-alone software package, and partially on a remote machine or server.

[0109] The computer device provided above can be used to execute the power consumption data generation method and / or the household power consumption load prediction method provided by any of the embodiments above, and has the corresponding functions and advantages.

[0110] Embodiment Seven

[0111] In the context of the present application, the computer-readable storage medium can be a tangible medium, and the computer-executable instructions, when executed by a computer processor, are used to execute the power consumption data generation method and / or the household power consumption load prediction method, the power consumption data generation method comprising:

[0112] obtaining target data affecting the power consumption of the simulated electrical appliance;

[0113] generating target information, and determining electrical appliance action information according to the target information and the target data using a preset model, wherein the target information includes event jump probability and action occurrence probability at the time of event occurrence;

[0114] outputting power consumption load data using the simulated electrical appliance and the electrical appliance action information.

[0115] The household power consumption load prediction method comprises:

[0116] obtaining target power consumption load data, the target power consumption load data including power consumption load data obtained by the power consumption data generation method described in the above embodiments;

[0117] determining household power consumption load prediction data of a future time period of a household using the target power consumption load data and a preset load prediction model.

[0118] In the context of the present application, a computer readable storage medium can be a tangible medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium can be a machine readable signal medium. More specific examples of the machine readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] The computer device provided above can be used to execute the power consumption data generation method and / or the household power consumption load prediction method provided by any of the embodiments above, and has the corresponding functions and advantages.

[0120] It is worth noting that the embodiments of the power consumption data generation device provided above are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy mutual differentiation, and do not limit the protection scope of the present application.

[0121] Note that the above are only the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for generating electricity consumption data, characterized in that, include: Obtain target data affecting the electricity consumption of analog electrical appliances; Generate target information, and use a preset model to determine the electrical appliance action information based on the target information and the target data, wherein the target information includes the event jump probability and the action occurrence probability when the event occurs; Using the simulated electrical appliance and its operating information, power load data is output.

2. The method according to claim 1, characterized in that, The step of determining electrical appliance action information based on the target information and the target data using a preset model includes: Using a pre-defined sequential decision model, the next event of the current event and the action in the next event are determined based on the target matrix and the target data to obtain electrical appliance action information. The target information includes the target matrix, which includes a state transition probability matrix and an action selection probability matrix. The state transition probability matrix includes event jump probabilities, and the action selection probability matrix includes the probability of each action occurring when the event occurs.

3. The method according to claim 2, characterized in that, The process of using a pre-defined sequential decision model to determine the next event and the action in the next event based on the target matrix and the target data, in order to obtain electrical appliance action information, includes: Using a Markov stochastic decision process, based on the target data and the current event at the current time, it is determined whether to proceed to the next event; If so, then determine the first target probability from the state transition probability matrix, and determine the event corresponding to the first target probability as the next event of the current event; Using a Markov stochastic decision process, a second target probability is determined from the action selection probability matrix based on the target data, and the action corresponding to the second target probability is determined as the action in the next event; Output electrical appliance action information, wherein the electrical appliance action information includes the action and the target electrical appliance involved in the action.

4. The method according to claim 1, characterized in that, The step of using the simulated electrical appliance and its operation information to output power load data includes: Based on the electrical appliance action information, the corresponding simulated electrical appliance is simulated to perform at least one of the following actions: turning on, turning off, and adjusting the gear, in order to output electrical load data.

5. The method according to any one of claims 1-4, characterized in that, After outputting the power load data using the simulated electrical appliance and its operating information, the method further includes: Update the current state of the simulated electrical appliance to the target data.

6. A method for predicting household electricity load, characterized in that, include: Obtain target electricity load data, wherein the target electricity load data includes electricity load data obtained by the electricity data generation method according to any one of claims 1-5; The target electricity load data and the preset load prediction model are used to determine the household electricity load prediction data for future periods.

7. An electricity consumption data generation device, characterized in that, include: The data acquisition module is used to acquire target data that affects the electricity consumption of analog electrical appliances; An appliance action information determination module is used to generate target information and determine appliance action information based on the target information and target data using a preset model. The target information includes the event jump probability and the action occurrence probability when the event occurs. The power consumption data output module is used to output power load data using the analog electrical appliance and the appliance's operating information.

8. A household electricity load prediction device, characterized in that, include: The power load acquisition module is used to acquire target power load data, wherein the target power load data includes power load data obtained by the power load data generation method according to any one of claims 1-5; The data prediction module is used to determine the household electricity load prediction data for future periods using the target electricity load data and a preset load prediction model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the electricity data generation method of any one of claims 1-5 and / or implement the household electricity load forecasting method of claim 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the electricity data generation method of any one of claims 1-5 and / or the household electricity load prediction method of claim 6.

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