Energy-saving household management method and system for concentrator

Abstract

The present invention discloses a method and system for energy-saving household management of a concentrator, and relates to the field of smart grid technology. The method includes: establishing a communication connection between a target concentrator and an intelligent power consumption monitoring device, collecting multiple household total power consumption time series of multiple users under a preset period and a set of power consumption data of multiple electrical equipment; predicting the power consumption demand of multiple users, generating multiple household predicted power consumption; analyzing independent waste indicators of electrical equipment, and constructing a list of N electrical power waste indicators of multiple users; judging whether the total power consumption of multiple household predicted power consumption meets the preset energy-saving power supply information, and if not, generating energy-saving household optimization instructions; performing household power supply optimization, generating household recommended power supply decisions; and managing power supply for multiple users according to household recommended power supply decisions. The technical problem of energy waste caused by insufficient precision of household power consumption management in the prior art is solved, and the technical effect of improving the accuracy of power supply is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to an energy-saving household management method and system for a concentrator. Background Art

[0002] In the architecture of smart grids, concentrators play a vital role. The main function of concentrators is to collect data from front-end devices such as smart meters and sensors, and then reliably and efficiently send these data to the central system of the power company for analysis and processing. As the power consumption monitoring point of each user's home, smart meters can record the user's power consumption data in real time, while sensors can monitor the operating status and power consumption of electrical equipment. With the acceleration of urbanization and the continuous growth of electricity demand, the power supply system needs more accurate and efficient management. However, the traditional power supply model often lacks refined management of household electricity consumption, resulting in unreasonable power supply and serious energy waste. Summary of the invention

[0003] The embodiments of the present application provide a method and system for energy-saving household management of a concentrator, which solves the technical problem in the prior art that the household electricity management is not sophisticated enough, resulting in energy waste.

[0004] In view of the above problems, an embodiment of the present application provides an energy-saving household management method and system for a concentrator.

[0005] A first aspect of an embodiment of the present application provides an energy-saving household management method of a concentrator, the method comprising:

[0006] Establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series and multiple electrical equipment power consumption data sets of multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets of the multiple users; based on the multiple household total power consumption time series, predict the power consumption demand of the multiple users at the preset power distribution time node, and generate multiple household predicted power consumption; based on the multiple electrical equipment power consumption data sets, analyze the independent waste indicators of the electrical equipment, and construct the N electrical appliances power waste indicators of the multiple users. An indicator list, wherein N is an integer greater than 1; connecting to the power grid system, receiving the preset energy-saving power supply information for the multiple users at the preset power distribution time node, and determining whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information, and if not, generating an energy-saving household optimization instruction; activating a household optimizer through the energy-saving household optimization instruction, performing household power supply optimization according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumption, and generating a household recommended power supply decision; and performing power supply management for the multiple users according to the household recommended power supply decision.

[0007] A second aspect of the embodiment of the present application provides an energy-saving household management system of a concentrator, the system comprising:

[0008] A data collection module, the data collection module is used to establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series and multiple electrical equipment power consumption data sets for multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets for the multiple users; a prediction module, the prediction module is used to predict the power consumption demand of the multiple users at a preset power distribution time node based on the multiple household total power consumption time series, and generate multiple household predicted power consumption; an analysis module, the analysis module is used to analyze the independent waste indicators of electrical equipment based on the multiple electrical equipment power consumption data sets, and construct the N electrical appliances power waste indicators of the multiple users An indicator list, wherein N is an integer greater than 1; a judgment module, the judgment module is used to connect to the power grid system, receive the preset energy-saving power supply information for the multiple users at the preset power distribution time node, and judge whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information, if not, generate an energy-saving household optimization instruction; an optimization module, the optimization module is used to activate the household optimizer through the energy-saving household optimization instruction, perform household power supply optimization according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumption, and generate a household recommended power supply decision; a power supply management module, the power supply management module is used to manage the power supply to the multiple users according to the household recommended power supply decision.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series and multiple electrical equipment power consumption data sets of multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets of multiple users. Then, based on the multiple household total power consumption time series, predict the power consumption demand of multiple users at the preset power distribution time node, and generate multiple household predicted power consumption. At the same time, analyze the independent waste index of electrical equipment based on the power consumption data sets of multiple electrical equipment, and construct a list of N electrical equipment power consumption waste indexes for multiple users, wherein N is an integer greater than 1. By connecting to the power grid system, receive the preset energy-saving power supply information for multiple users at the preset power distribution time node, and determine whether the total power consumption of multiple household predicted power consumption meets the preset energy-saving power supply information. If not, generate an energy-saving household optimization instruction. The household optimizer is activated by the energy-saving household optimization instruction, and household power supply optimization is performed according to the N electrical equipment power consumption waste index lists and the multiple household predicted power consumption, and household recommended power supply decisions are generated. Finally, power supply management is performed for multiple users according to the household recommended power supply decisions. The invention solves the technical problem of energy waste caused by insufficiently refined household electricity management in the prior art, and achieves the technical effect of improving the accuracy of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic flow chart of an energy-saving household management method for a concentrator provided in an embodiment of the present application.

[0013] Figure 2 A schematic diagram of the structure of an energy-saving household management system of a concentrator provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: data collection module 11 , prediction module 12 , analysis module 13 , judgment module 14 , optimization module 15 , power supply management module 16 . DETAILED DESCRIPTION

[0015] The embodiments of the present application solve the technical problem in the prior art that energy waste is caused by insufficiently refined household electricity management by providing an energy-saving household management method and system for a concentrator.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the embodiment of the present application provides an energy-saving household management method of a concentrator, wherein the method includes:

[0020] Establish a communication connection between the target concentrator and the intelligent electricity consumption monitoring device, collect multiple household total electricity consumption time series and multiple electrical equipment electricity consumption data sets of multiple users under a preset period, wherein the intelligent electricity consumption monitoring device includes multiple smart meters and multiple smart socket sets of the multiple users.

[0021] The smart electricity monitoring equipment includes multiple smart meters and multiple smart socket sets for multiple users, where multiple smart socket sets refer to smart sockets inserted into corresponding sockets of different electrical appliances, and monitor the electricity consumption data of these appliances in real time. By establishing a communication connection between the target concentrator and the smart electricity monitoring equipment, the total household electricity consumption time series data of multiple users and the electricity consumption data set of multiple electrical appliances are collected within a preset period (such as day, week, month, etc.), providing strong support for subsequent data analysis and electricity management. The multiple household total electricity consumption time series of multiple users records the total electricity consumption and electricity consumption trend of each user in a specific time period, and the electricity consumption data set of multiple electrical appliances includes detailed information such as the switch status, power consumption, and power consumption duration of each electrical appliance.

[0022] Based on the multiple household total electricity consumption time series, the electricity consumption demand of the multiple users is predicted at the preset power distribution time node to generate multiple household predicted electricity consumption.

[0023] Based on historical electricity consumption data, time series analysis models (such as ARIMA, LSTM, etc.) are trained to enable the model to predict the user's electricity demand at the preset power distribution time node based on the total electricity consumption time series of each household. At the preset power distribution time node, multiple total electricity consumption time series of each household are input into the trained model to predict the electricity demand of multiple users. The model will output the predicted electricity consumption of each user within the preset power distribution time node, thereby providing strong support for power dispatching and user power management.

[0024] Based on the electricity consumption data sets of the multiple electrical devices, independent waste index analysis of the electrical devices is performed to construct a list of N electrical device electricity consumption waste indicators of the multiple users, where N is an integer greater than 1.

[0025] By analyzing the independent waste indicators of electrical equipment on multiple sets of electrical equipment electricity consumption data, that is, from the collected multiple sets of electrical equipment electricity consumption data, the electricity consumption data of the electrical equipment used by each user is screened out. The electricity consumption of the same type of electrical appliances should not differ much. After the concentrated characteristic value is calculated, if it is greater than the concentrated characteristic value, the degree of difference is calculated as the waste indicator, and then a list of N electrical equipment electricity waste indicators for multiple users is constructed.

[0026] Furthermore, performing independent waste index analysis of electrical equipment based on the plurality of electrical equipment power consumption data sets includes:

[0027] The plurality of electrical equipment power consumption data sets are classified according to the electrical equipment attributes to generate a first electrical equipment attribute power consumption data set, a second electrical equipment attribute power consumption data set, up to the Nth electrical equipment attribute power consumption data set; a multi-user power consumption difference analysis of a single electrical equipment attribute is performed on the first electrical equipment attribute power consumption data set, the second electrical equipment attribute power consumption data set, up to the Nth electrical equipment attribute power consumption data set, to generate a first electrical equipment attribute power consumption difference indicator set, a second electrical equipment attribute power consumption difference indicator set, up to the Nth electrical equipment attribute power consumption difference indicator set; information on a plurality of electricity users of the plurality of users is obtained, and difference compensation is performed on the first electrical equipment attribute power consumption difference indicator set, the second electrical equipment attribute power consumption difference indicator set, up to the Nth electrical equipment attribute power consumption difference indicator set, to generate a list of power waste indicators for the N electrical appliances.

[0028] Preferably, the collected multiple electrical equipment power consumption data sets are classified according to the attributes of the electrical equipment (such as air conditioners, refrigerators, lighting, etc.), the attributes of each electrical equipment are identified and marked through data tags or user input, and the electrical equipment power consumption data with the same attributes are classified into one category to generate a first electrical equipment attribute power consumption data set, a second electrical equipment attribute power consumption data set, and so on. For each electrical equipment attribute power consumption data set, the power consumption difference analysis between multiple users is performed to analyze the differences in power consumption habits, power consumption, power consumption time, etc. of different users under the same electrical equipment attributes, and the differences are quantified through statistical methods (such as mean, median, standard deviation, etc.). These differences are analyzed and corresponding difference indicators are generated, thereby forming a first electrical appliance attribute electricity usage difference indicator set, a second electrical appliance attribute electricity usage difference indicator set, and so on, up to an Nth electrical appliance attribute electricity usage difference indicator set; multiple electricity consumption population information of multiple users is obtained, including the number of family members, age structure, living habits, etc. Due to the influence of electricity consumption population information on electricity consumption differences, difference compensation is performed on the previously generated set of electricity consumption difference indicators of electrical appliance attributes. The difference compensation can be achieved by establishing a machine learning model, which takes the electricity consumption population information as input, adjusts or corrects the difference indicators, and after difference compensation, generates a more accurate and reference-worthy list of N electrical appliance electricity waste indicators.

[0029] Further, performing a multi-user electricity usage difference analysis of a single electrical appliance attribute on the first electrical appliance attribute electricity usage data set, the second electrical appliance attribute electricity usage data set, and up to the Nth electrical appliance attribute electricity usage data set includes:

[0030] Perform a central trend analysis on the first electrical appliance attribute electricity usage data set to generate a first electrical appliance attribute electricity usage central characteristic value; determine whether the electricity usage data belonging to the first user in the first electrical appliance attribute electricity usage data set is greater than the first electrical appliance attribute electricity usage central characteristic value; if so, generate a first electrical appliance attribute electricity usage difference index for the first user based on the electricity usage deviation, and add it to the first electrical appliance attribute electricity usage difference index set; if not, automatically reset the first electrical appliance attribute electricity usage difference index for the first user to zero; and so on, continue to perform multi-user electricity usage difference analysis of a single electrical appliance attribute on the second electrical appliance attribute electricity usage data set until the Nth electrical appliance attribute electricity usage data set, and generate the first electrical appliance attribute electricity usage difference index set, the second electrical appliance attribute electricity usage difference index set, and the Nth electrical appliance attribute electricity usage difference index set.

[0031] Preferably, a central tendency analysis is performed on the electricity consumption data set of the first electrical appliance attribute, and statistical methods such as mean, median or mode are usually used to quantify the overall electricity consumption of the electrical appliance attribute, and a concentrated characteristic value of electricity consumption of the first electrical appliance attribute is generated. The concentrated characteristic value of electricity consumption of the first electrical appliance attribute represents the typical electricity consumption level of the electrical appliance attribute among most users; the electricity consumption data of the first user under the first electrical appliance attribute is extracted, and it is determined whether the electricity consumption data of the first user is greater than the concentrated characteristic value of electricity consumption of the first electrical appliance attribute. If the electricity consumption data of the first user is greater than the concentrated characteristic value, the electricity consumption deviation (i.e., the difference between the actual electricity consumption and the concentrated characteristic value) is calculated, and a power consumption deviation is generated based on the deviation. The first electrical appliance attribute electricity consumption difference index of the first user is added to the first electrical appliance attribute electricity consumption difference index set; if the first user's electricity consumption data is not greater than the concentrated characteristic value, the first electrical appliance attribute electricity consumption difference index of the user is automatically reset to zero, indicating that the user's electricity consumption behavior under the electrical appliance attribute is relatively economical or close to the average level; repeat the above steps, and perform multi-user electricity consumption difference analysis of a single electrical appliance attribute on the second electrical appliance attribute electricity consumption data set until the Nth electrical appliance attribute electricity consumption data set, and for each electrical appliance attribute, generate a corresponding electrical appliance attribute electricity consumption concentrated characteristic value and a set of electricity consumption difference indicators for multiple users.

[0032] Furthermore, performing a central trend analysis on the first electrical appliance attribute power consumption data set includes:

[0033] The first electrical appliance attribute electricity usage data set is classified and divided according to a preset electricity usage deviation threshold to generate multiple clusters of first electrical appliance attribute electricity usage data; multiple clustering proportion coefficients of the multiple clusters of first electrical appliance attribute electricity usage data are calculated; based on the multiple clustering proportion coefficients, M clusters of first electrical appliance attribute electricity usage data whose clustering proportion coefficients are greater than the preset coefficient are extracted, and mean processing is performed to generate the first electrical appliance attribute electricity usage concentrated characteristic value, wherein M is an integer greater than 0.

[0034] Preferably, according to a preset power consumption deviation threshold, the first electrical appliance attribute power consumption data set is classified and divided, and a clustering algorithm (such as K-means, DBSCAN, etc.) is used to divide the power consumption data into multiple clusters, each cluster represents a similar power consumption mode or power consumption habit, and the division of these clusters is based on power consumption deviation to ensure that the data in the same cluster has similar power consumption characteristics; for each cluster, the proportion of the power consumption data contained in it to the entire first electrical appliance attribute power consumption data set is calculated to obtain a clustering ratio coefficient, which reflects the clustering ratio of the cluster in the overall Representativeness in electricity consumption data; set a preset coefficient as the screening criterion, traverse all clusters, find M clusters whose cluster ratio coefficient is greater than the preset coefficient, these clusters represent the main electricity consumption habits, and perform mean processing on the electricity consumption data in these M clusters, that is, calculate the average value of all data in each cluster, and further process the means of the M clusters (such as weighted average, etc.) to obtain the concentrated characteristic value of electricity consumption of the first electrical appliance attribute. The concentrated characteristic value of electricity consumption of the first electrical appliance attribute represents the main electricity consumption level or typical electricity consumption mode of this electrical appliance attribute in most users.

[0035] Connect to the power grid system, receive the preset energy-saving power supply information for the multiple users at the preset power distribution time node, and determine whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information. If not, generate energy-saving household optimization instructions.

[0036] By connecting to the power grid system, preset energy-saving power supply information for multiple users is received from the power grid system at a preset power distribution time node, including the maximum power supply, power limit, energy-saving target, etc. of each user in a specific time period; the household predicted power consumption of each user at the preset power distribution time node is obtained, and the household predicted power consumption of all users is accumulated to obtain the total predicted power consumption; the calculated total power consumption is compared with relevant indicators in the preset energy-saving power supply information (such as maximum power supply, power limit, etc.). If the total power consumption exceeds the preset maximum power supply, it is judged that the energy-saving power supply information is not met, and then energy-saving household optimization instructions are generated, including adjusting the power supply plan of certain users, reducing the power consumption of specific users, etc.

[0037] The household optimizer is activated by the energy-saving household optimization instruction, and household power supply optimization is performed according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumptions to generate household recommended power supply decisions.

[0038] After receiving the energy-saving household optimization instruction, the household optimizer is activated. The household optimizer is a module responsible for analyzing and optimizing power supply decisions. The household optimizer will optimize the household power supply based on the list of N electrical appliance power waste indicators and the multiple household predicted power consumptions, and generate household recommended power supply decisions. The household recommended power supply decisions are used to assist in energy-saving power supply.

[0039] Furthermore, optimizing household power supply according to the N lists of electrical appliance power waste indicators and the plurality of household predicted power consumptions includes:

[0040] Based on the multiple electricity consumption population information of the multiple users, the preset energy-saving power supply information is allocated with respect to power supply quantity, and multiple household standardized power supply information is generated; the multiple household standardized power supply information is compared with the multiple household predicted power consumption, and a set of abnormal users whose deviation is greater than a preset deviation is located, wherein the abnormal user set has an abnormality degree identifier; based on the list of N electrical appliance power waste indicators and the abnormality degree identifier, user traversal optimization is performed on the abnormal user set with the abnormality degree decreasing step by step as an optimization constraint, until a household recommended power supply decision that meets the preset energy-saving power supply information is generated.

[0041] Preferably, the preset energy-saving power supply information is allocated with respect to the power consumption information of multiple users (such as the number of family members, age structure, occupation type, etc.); by analyzing the power consumption information of multiple users, the basic power demand and potential power consumption pattern of each user can be estimated; based on this information, a reasonable power supply is allocated to each user, and multiple household standardized power supply information is generated; the generated multiple household standardized power supply information is compared with multiple household predicted power consumption, the power consumption deviation of each user is calculated, that is, the difference between the predicted power consumption and the standardized power supply information, a preset deviation threshold is set, users whose deviation is greater than the threshold are located, and an abnormal user set is formed, and a set of abnormal users is provided for the abnormal users. Each user in the user set is assigned an abnormality identifier to identify the degree of deviation; based on the list of N electrical appliance power waste indicators and the abnormality identifier, the abnormal user set is traversed and optimized, with the abnormality decreasing step by step as the optimization constraint, which means that the users with the highest abnormality are focused on first, and then gradually expanded to users with lower abnormality. For each abnormal user, its power consumption data, electrical appliance power waste indicators and possible energy-saving potential are analyzed. According to the analysis results, specific energy-saving suggestions or power supply adjustment plans are formulated for the user to reduce power waste and improve power efficiency; the power supply decisions of abnormal users are continuously iterated and adjusted until a household recommended power supply decision that meets the preset energy-saving power supply information is generated.

[0042] Furthermore, taking the step-by-step decrease of abnormality as the optimization constraint, the abnormal user set is subjected to user traversal optimization, including:

[0043] A first abnormal user with the largest abnormality is extracted from the abnormal user set; based on the list of N electrical appliance power waste indicators, P abnormal electrical appliance power waste indicators greater than a preset waste indicator are extracted from the first abnormal user, wherein P is an integer greater than or equal to 1 and less than N; energy-saving optimization is performed on the household predicted power consumption of the first abnormal user based on the P abnormal electrical appliance power waste indicators, and a recommended power supply decision for the first abnormal user is generated; a summary verification is performed on the power supply decision recommended by the first abnormal user and the household predicted power consumption of the remaining users, to determine whether the summary verification result meets the preset energy-saving power supply information; if so, the household recommended power supply decision is generated based on the power supply decision recommended by the first abnormal user and the household predicted power consumption of the remaining users; if not, the second abnormal user with the largest abnormality in the abnormal user set other than the first abnormal user is continuously obtained, and energy-saving optimization is performed until the household recommended power supply decision that meets the preset energy-saving power supply information is generated.

[0044] Preferably, in the abnormal user set, the user with the largest abnormality is extracted as the first abnormal user, and according to the list of N electrical appliance power waste indicators, P abnormal electrical appliance power waste indicators greater than the preset waste indicator are screened out for the first abnormal user, wherein P is an integer greater than or equal to 1 and less than N; based on the screened out P abnormal electrical appliance power waste indicators, energy-saving optimization is performed on the household predicted power consumption of the first abnormal user, such as setting a power consumption upper limit, etc., and according to the optimization result, a recommended power supply decision for the first abnormal user is generated; the recommended power supply decision of the first abnormal user is summarized with the household predicted power consumption of the remaining users, and it is verified whether the summarized result meets the requirements of the preset energy-saving power supply information, including the total power supply, power limit, etc. If the summarized verification result meets the preset energy-saving power supply information, the first abnormal user is directly The recommended power supply decision for the abnormal user is combined with the household predicted power consumption of the remaining users to generate the final household recommended power supply decision. If it is not satisfied, it is necessary to continue to process the abnormal user set, remove the first abnormal user that has been processed from the abnormal user set, extract the remaining user with the largest abnormality as the second abnormal user, repeat the above process, perform energy-saving optimization on the second abnormal user, and verify whether the summary result meets the preset energy-saving power supply information. If not, continue to process the next user with the largest abnormality until all abnormal users are processed or the summary verification result meets the preset energy-saving power supply information; once the summary verification result meets the preset energy-saving power supply information, combine the recommended power supply decisions of all abnormal users with the household predicted power consumption of the remaining users to generate the final household recommended power supply decision.

[0045] Furthermore, it also includes:

[0046] Extract N electrical equipment attributes corresponding to the N electrical equipment electricity waste index lists; identify necessary attributes of the N electrical equipment attributes to generate necessary attribute electrical appliances; and in the process of optimizing household power supply, do not perform energy-saving optimization on the necessary attribute electrical appliances.

[0047] Preferably, from a list of N electrical appliance power waste indicators, electrical appliance attributes corresponding to each indicator are extracted, including the type, power, purpose, frequency of use, etc. of the appliance; necessary attributes of the extracted N electrical appliance attributes are identified, such as some appliances are necessary for safety or health (such as refrigerators, medical equipment, surveillance cameras, etc.), and through a series of preset rules, necessary attribute appliances are identified, that is, those appliances that should not be optimized for energy saving, and the identified necessary attribute appliances are sorted into a list, which includes electrical equipment that should not be optimized for energy saving. In the process of optimizing household power supply, electrical equipment in the list of necessary attribute appliances is first excluded, and only the remaining non-essential attribute appliances are optimized for energy saving, and their power waste indicators are considered to generate a recommended power supply decision, and the power consumption of the necessary attribute appliances and the recommended power supply decision of the non-essential attribute appliances are combined to form a complete household recommended power supply decision to ensure that the decision not only meets the user's power demand, but also achieves the energy saving goal as much as possible.

[0048] Power supply management is performed for the multiple users according to the household-based recommended power supply decision.

[0049] The recommended power supply decision for each household will be notified to each user, and the reasons and purposes behind the decision will be explained so that the user understands why such an adjustment is made. By implementing the above power supply management measures, it can be ensured that power resources are reasonably allocated and efficiently utilized, while meeting the user's electricity needs and achieving energy-saving goals.

[0050] In summary, the embodiments of the present application have at least the following technical effects:

[0051] First, establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series and multiple electrical equipment power consumption data sets of multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets of multiple users. Then, based on the multiple household total power consumption time series, predict the power consumption demand of multiple users at the preset power distribution time node, and generate multiple household predicted power consumption. At the same time, analyze the independent waste index of electrical equipment based on the power consumption data sets of multiple electrical equipment, and construct a list of N electrical equipment power consumption waste indexes for multiple users, wherein N is an integer greater than 1. By connecting to the power grid system, receive the preset energy-saving power supply information for multiple users at the preset power distribution time node, and determine whether the total power consumption of multiple household predicted power consumption meets the preset energy-saving power supply information. If not, generate an energy-saving household optimization instruction. The household optimizer is activated by the energy-saving household optimization instruction, and household power supply optimization is performed according to the N electrical equipment power consumption waste index lists and the multiple household predicted power consumption, and household recommended power supply decisions are generated. Finally, power supply management is performed for multiple users according to the household recommended power supply decisions. The invention solves the technical problem of energy waste caused by insufficiently refined household electricity management in the prior art, and achieves the technical effect of improving the accuracy of power supply.

[0052] Embodiment 2

[0053] Based on the same inventive concept as the energy-saving household management method of a concentrator in the aforementioned embodiment, Figure 2 As shown, the present application provides an energy-saving household management system for a concentrator, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0054] A data collection module 11, the data collection module 11 is used to establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series and multiple electrical equipment power consumption data sets for multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets for the multiple users; a prediction module 12, the prediction module 12 is used to predict the power consumption demand of the multiple users at a preset power distribution time node based on the multiple household total power consumption time series, and generate multiple household predicted power consumption; an analysis module 13, the analysis module 13 is used to analyze the independent waste indicators of electrical equipment based on the multiple electrical equipment power consumption data sets, and construct the N electrical appliances power waste indicators of the multiple users. An indicator list, wherein N is an integer greater than 1; a judgment module 14, the judgment module 14 is used to connect to the power grid system, receive the preset energy-saving power supply information for the multiple users at the preset power distribution time node, and judge whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information, if not, generate an energy-saving household optimization instruction; an optimization module 15, the optimization module 15 is used to activate the household optimizer through the energy-saving household optimization instruction, perform household power supply optimization according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumption, and generate a household recommended power supply decision; a power supply management module 16, the power supply management module 16 is used to manage the power supply to the multiple users according to the household recommended power supply decision.

[0055] Furthermore, the analysis module 13 is used to perform the following method:

[0056] The plurality of electrical equipment power consumption data sets are classified according to the electrical equipment attributes to generate a first electrical equipment attribute power consumption data set, a second electrical equipment attribute power consumption data set, up to the Nth electrical equipment attribute power consumption data set; a multi-user power consumption difference analysis of a single electrical equipment attribute is performed on the first electrical equipment attribute power consumption data set, the second electrical equipment attribute power consumption data set, up to the Nth electrical equipment attribute power consumption data set, to generate a first electrical equipment attribute power consumption difference indicator set, a second electrical equipment attribute power consumption difference indicator set, up to the Nth electrical equipment attribute power consumption difference indicator set; information on a plurality of electricity users of the plurality of users is obtained, and difference compensation is performed on the first electrical equipment attribute power consumption difference indicator set, the second electrical equipment attribute power consumption difference indicator set, up to the Nth electrical equipment attribute power consumption difference indicator set, to generate a list of power waste indicators for the N electrical appliances.

[0057] Furthermore, the analysis module 13 is used to perform the following method:

[0058] Perform a central trend analysis on the first electrical appliance attribute electricity usage data set to generate a first electrical appliance attribute electricity usage central characteristic value; determine whether the electricity usage data belonging to the first user in the first electrical appliance attribute electricity usage data set is greater than the first electrical appliance attribute electricity usage central characteristic value; if so, generate a first electrical appliance attribute electricity usage difference index for the first user based on the electricity usage deviation, and add it to the first electrical appliance attribute electricity usage difference index set; if not, automatically reset the first electrical appliance attribute electricity usage difference index for the first user to zero; and so on, continue to perform multi-user electricity usage difference analysis of a single electrical appliance attribute on the second electrical appliance attribute electricity usage data set until the Nth electrical appliance attribute electricity usage data set, and generate the first electrical appliance attribute electricity usage difference index set, the second electrical appliance attribute electricity usage difference index set, and the Nth electrical appliance attribute electricity usage difference index set.

[0059] Furthermore, the analysis module 13 is used to perform the following method:

[0060] The first electrical appliance attribute electricity usage data set is classified and divided according to a preset electricity usage deviation threshold to generate multiple clusters of first electrical appliance attribute electricity usage data; multiple clustering proportion coefficients of the multiple clusters of first electrical appliance attribute electricity usage data are calculated; based on the multiple clustering proportion coefficients, M clusters of first electrical appliance attribute electricity usage data whose clustering proportion coefficients are greater than the preset coefficient are extracted, and mean processing is performed to generate the first electrical appliance attribute electricity usage concentrated characteristic value, wherein M is an integer greater than 0.

[0061] Furthermore, the optimization module 15 is used to execute the following method:

[0062] Based on the multiple electricity consumption population information of the multiple users, the preset energy-saving power supply information is allocated with respect to power supply quantity, and multiple household standardized power supply information is generated; the multiple household standardized power supply information is compared with the multiple household predicted power consumption, and a set of abnormal users whose deviation is greater than a preset deviation is located, wherein the abnormal user set has an abnormality degree identifier; based on the list of N electrical appliance power waste indicators and the abnormality degree identifier, user traversal optimization is performed on the abnormal user set with the abnormality degree decreasing step by step as an optimization constraint, until a household recommended power supply decision that meets the preset energy-saving power supply information is generated.

[0063] Furthermore, the optimization module 15 is used to execute the following method:

[0064] A first abnormal user with the largest abnormality is extracted from the abnormal user set; based on the list of N electrical appliance power waste indicators, P abnormal electrical appliance power waste indicators greater than a preset waste indicator are extracted from the first abnormal user, wherein P is an integer greater than or equal to 1 and less than N; energy-saving optimization is performed on the household predicted power consumption of the first abnormal user based on the P abnormal electrical appliance power waste indicators, and a recommended power supply decision for the first abnormal user is generated; a summary verification is performed on the power supply decision recommended by the first abnormal user and the household predicted power consumption of the remaining users, to determine whether the summary verification result meets the preset energy-saving power supply information; if so, the household recommended power supply decision is generated based on the power supply decision recommended by the first abnormal user and the household predicted power consumption of the remaining users; if not, the second abnormal user with the largest abnormality in the abnormal user set other than the first abnormal user is continuously obtained, and energy-saving optimization is performed until the household recommended power supply decision that meets the preset energy-saving power supply information is generated.

[0065] Furthermore, the optimization module 15 is used to execute the following method:

[0066] Extract N electrical equipment attributes corresponding to the N electrical equipment electricity waste index lists; identify necessary attributes of the N electrical equipment attributes to generate necessary attribute electrical appliances; and in the process of optimizing household power supply, do not perform energy-saving optimization on the necessary attribute electrical appliances.

[0067] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0069] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A household-based energy-saving management method for a concentrator, characterized in that: The method comprises: Establishing a communication connection between the target concentrator and the intelligent power consumption monitoring device, collecting multiple household total power consumption time series and multiple electrical equipment power consumption data sets of multiple users under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets of the multiple users; Based on the multiple household total electricity consumption time series, predict the electricity consumption demand of the multiple users at the preset power distribution time node to generate multiple household predicted electricity consumption; Perform independent waste index analysis of electrical equipment based on the plurality of electrical equipment power consumption data sets, and construct a list of N electrical equipment power consumption waste indexes of the plurality of users, where N is an integer greater than 1; Connecting to the power grid system, receiving preset energy-saving power supply information for the multiple users at the preset power distribution time node, and determining whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information, and if not, generating an energy-saving household optimization instruction; The household optimizer is activated by the energy-saving household optimization instruction, and household power supply optimization is performed according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumptions, to generate a household recommended power supply decision; Performing power supply management for the multiple users according to the household recommended power supply decision; Performing independent waste index analysis of electrical equipment based on the plurality of electrical equipment power consumption data sets includes: Classifying the plurality of electrical equipment power usage data sets according to electrical equipment attributes to generate a first electrical equipment attribute power usage data set, a second electrical equipment attribute power usage data set, and so on to an Nth electrical equipment attribute power usage data set; Performing a multi-user electricity usage difference analysis of a single electrical appliance attribute on the first electrical appliance attribute electricity usage data set, the second electrical appliance attribute electricity usage data set, and up to the Nth electrical appliance attribute electricity usage data set, to generate a first electrical appliance attribute electricity usage difference index set, a second electrical appliance attribute electricity usage difference index set, and up to the Nth electrical appliance attribute electricity usage difference index set; Acquire multiple electricity consumption population information of the multiple users, perform difference compensation on the first electrical appliance attribute electricity consumption difference index set, the second electrical appliance attribute electricity consumption difference index set, and up to the Nth electrical appliance attribute electricity consumption difference index set, and generate the N electrical appliance electricity waste index list; Optimizing household power supply according to the N lists of power waste indicators of electrical appliances and the plurality of household predicted power consumptions includes: Based on the multiple electricity consumption population information of the multiple users, the preset energy-saving power supply information is allocated with respect to the power supply amount, and multiple household standardized power supply information is generated; Comparing the plurality of household standardized power supply information with the plurality of household predicted power consumption, locating an abnormal user set whose deviation is greater than a preset deviation, wherein the abnormal user set has an abnormality degree identifier; According to the N electrical appliance power waste index list and the abnormality level identifier, the abnormal user set is subjected to user traversal optimization with the abnormality level decreasing step by step as the optimization constraint, until a household recommended power supply decision satisfying the preset energy-saving power supply information is generated; Performing a multi-user electricity usage difference analysis of a single electrical appliance attribute on the first electrical appliance attribute electricity usage data set, the second electrical appliance attribute electricity usage data set, and up to the Nth electrical appliance attribute electricity usage data set, includes: Performing a central trend analysis on the power consumption data set of the first electrical appliance attribute to generate a central characteristic value of power consumption of the first electrical appliance attribute; Determine whether the power consumption data belonging to the first user in the power consumption data set of the first appliance attribute is greater than the power consumption concentration characteristic value of the first appliance attribute; If so, generating a first electrical appliance attribute power usage difference index of the first user according to the power usage deviation, and adding the index to the first electrical appliance attribute power usage difference index set; If not, automatically resetting the power consumption difference indicator of the first electrical appliance attribute of the first user to zero; By analogy, continue to perform multi-user electricity usage difference analysis of a single appliance attribute on the second appliance attribute electricity usage data set until the Nth appliance attribute electricity usage data set, and generate the first appliance attribute electricity usage difference index set, the second appliance attribute electricity usage difference index set until the Nth appliance attribute electricity usage difference index set; Performing a central trend analysis on the first electrical appliance attribute power consumption data set includes: Classify and divide the first electrical appliance attribute power consumption data set according to a preset power consumption deviation threshold value to generate multiple clusters of first electrical appliance attribute power consumption data; Calculating a plurality of clustering ratio coefficients of the plurality of clusters of the first electrical appliance attribute power consumption data; Based on the multiple clustering proportion coefficients, M clusters of first electrical appliance attribute power consumption data with clustering proportion coefficients greater than a preset coefficient are extracted, and mean processing is performed to generate the first electrical appliance attribute power consumption concentrated characteristic value, wherein M is an integer greater than 0.

2. The energy-saving household management method of a concentrator as claimed in claim 1, characterized in that: Taking the step-by-step decrease of abnormality as the optimization constraint, the abnormal user set is subjected to user traversal optimization, including: Extracting a first abnormal user with the largest abnormality from the abnormal user set; According to the list of N electrical appliance power waste indicators, extracting P abnormal electrical appliance power waste indicators of the first abnormal user that are greater than a preset waste indicator, where P is an integer greater than or equal to 1 and less than N; Based on the P abnormal electrical appliance power waste indicators, energy-saving optimization is performed on the household predicted power consumption of the first abnormal user to generate a recommended power supply decision for the first abnormal user; Combine the first abnormal user's recommended power supply decision with the remaining users' predicted power consumption and perform summary verification to determine whether the summary verification result meets the preset energy-saving power supply information; If so, generating the household-by-household recommended power supply decision based on the first abnormal user recommended power supply decision and the household predicted power consumption of the remaining users; If not, continue to obtain the second abnormal user with the largest abnormality in the abnormal user set except the first abnormal user, and perform energy-saving optimization until the household recommended power supply decision that meets the preset energy-saving power supply information is generated.

3. The energy-saving household management method of a concentrator as claimed in claim 2, characterized in that: Also includes: Extracting N electrical equipment attributes corresponding to the N electrical equipment power waste indicator lists; Identify the necessary attributes of the N electrical equipment properties to generate necessary attribute electrical equipment; In the process of optimizing household power supply, energy-saving optimization is not performed on the electrical appliances with the necessary attributes.

4. An energy-saving household management system for a concentrator, characterized in that: The system is used to implement the energy-saving household management method of a concentrator according to any one of claims 1 to 3, and comprises: A data collection module, the data collection module is used to establish a communication connection between the target concentrator and the intelligent power consumption monitoring device, collect multiple household total power consumption time series of multiple users and multiple electrical equipment power consumption data sets under a preset period, wherein the intelligent power consumption monitoring device includes multiple smart meters and multiple smart socket sets of the multiple users; A prediction module, the prediction module is used to predict the power consumption demand of the multiple users at a preset power distribution time node based on the multiple household total power consumption time series, and generate multiple household predicted power consumption; An analysis module, the analysis module is used to perform independent waste index analysis of electrical equipment based on the plurality of electrical equipment power consumption data sets, and construct a list of N electrical equipment power consumption waste indexes of the plurality of users, wherein N is an integer greater than 1; A judgment module, the judgment module is used to connect to the power grid system, receive the preset energy-saving power supply information for the multiple users at the preset power distribution time node, and judge whether the total power consumption of the multiple household predicted power consumption meets the preset energy-saving power supply information, if not, generate an energy-saving household optimization instruction; An optimization module, the optimization module is used to activate the household optimizer through the energy-saving household optimization instruction, perform household power supply optimization according to the N electrical appliance power waste indicator lists and the multiple household predicted power consumptions, and generate a household recommended power supply decision; A power supply management module is used to manage power supply to the multiple users according to the household-recommended power supply decision.

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