Community energy consumption monitoring system based on big data

Abstract

The invention discloses a community energy consumption monitoring system based on big data. The community energy consumption monitoring system comprises a data acquisition module, a user typed data processing module, a module for acquiring various types of monitoring sub-models and an intelligent energy consumption monitoring module. The invention relates to the technical field of community energy data processing, in particular to a community energy consumption monitoring system based on big data, which innovatively introduces a community user typed difference analysis mechanism, performs independent learning and differential identification on energy consumption behavior characteristics of different user groups, and improves the energy consumption monitoring precision. A fuzzy lower approximation matrix is constructed based on class variance, and an adaptive feature weight learning mechanism is introduced to construct an energy consumption feature selection algorithm, so that the feature selection precision and stability are improved, and the performance of an energy consumption monitoring sub-model is enhanced; and an optimization algorithm is improved by adopting a three-stage progressive updating strategy and a multi-vector mean value variation strategy, so that the optimization efficiency and precision of the energy consumption monitoring sub-model are improved, and intelligent monitoring of community energy consumption is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of community energy data processing, in particular to a community energy consumption monitoring system based on big data. BACKGROUND

[0002] The community energy consumption monitoring system based on big data is a comprehensive energy management system that uses big data technology to collect and analyze various energy consumption data in the community in real time. Through centralized processing and intelligent analysis of energy consumption data, it realizes dynamic monitoring of energy consumption in the community, thereby improving energy utilization efficiency, reducing energy consumption cost, and supporting energy-saving management and decision-making of smart communities.

[0003] However, the traditional community energy consumption monitoring system has the technical problem of inaccurate energy consumption monitoring results due to the lack of differentiation between different types of users in the overall energy consumption monitoring of the community. The existing algorithms for energy consumption feature selection cannot distinguish the internal distribution difference of different result categories and cannot adjust the feature weight according to the data characteristics, resulting in poor adaptability to complex data and weak fitting ability, and further causing inaccurate energy consumption feature extraction results, poor performance of energy consumption monitoring models, and low accuracy of output results. The existing super parameter setting of the energy consumption monitoring sub-model is not reasonable, and the individual is easy to fall into local optimum in the super parameter optimization process, resulting in low search precision of the model super parameter, and thus low accuracy of the output results of the monitoring sub-model. SUMMARY

[0004] In view of the above, in order to overcome the defects of the prior art, the present application provides a community energy consumption monitoring system based on big data, aiming at the technical problem that the traditional community energy consumption monitoring system monitors the overall energy consumption of the community without distinguishing the energy use differences of different types of users, resulting in inaccurate energy consumption monitoring results, the present application innovatively introduces a community user type difference analysis mechanism, first divides and selects user type data through a user type data processing module, and constructs and trains an energy consumption monitoring sub-model for each type of user, which can independently learn and differentially identify the energy consumption behavior characteristics of different user groups, improve the energy consumption monitoring accuracy, support energy consumption monitoring of multiple energy types and different scale community scenarios, effectively enhance the universality and scalability of the system, realize community energy consumption classification monitoring, and significantly improve the refinement and intelligence level of intelligent community energy management; In view of the technical problems that the existing energy consumption feature selection algorithm cannot distinguish the internal distribution difference of different result categories and cannot automatically adjust the feature weight according to the data characteristics, resulting in poor adaptability to complex data and weak fitting ability, and further causing inaccurate energy consumption feature extraction results, poor energy consumption monitoring model performance and low output result accuracy, the present application innovatively proposes an energy consumption feature selection algorithm based on class variance, which can accurately capture the internal density difference of different energy consumption categories and automatically adjust the weight according to the data distribution characteristics, improve the discriminant ability of the feature in high density and sparse categories, improve the feature selection accuracy and stability, remove redundant and low contribution features, reduce the input dimension and computational complexity of the model, and at the same time enhance the performance of the energy consumption monitoring model and the accuracy of the output result, realizing intelligent management of community energy consumption; In view of the technical problem that the existing energy consumption monitoring sub-model parameter setting is not reasonable, and the individual is easy to fall into local optimum in the super parameter optimization process, the model has low search precision, and the output result of the monitoring sub-model has low accuracy, the present application innovatively adopts a three-stage progressive updating strategy and a multi-vector mean mutation strategy in the particle swarm optimization algorithm, which widens the search range of the super parameter and avoids premature convergence in the optimization process, ensures the balance between global search ability and local exploration ability in the optimization process, improves the refinement and accuracy of the super parameter optimization, improves the optimization efficiency and accuracy of the energy consumption monitoring sub-model, and finally realizes the classification and intelligent monitoring of community energy consumption.

[0005] The technical scheme adopted by the present application is as follows: the community energy consumption monitoring system based on big data provided by the present application comprises a data acquisition module, a user type data processing module, an acquisition type monitoring sub-model module and an energy consumption intelligent monitoring module.

[0006] The data acquisition module specifically acquires community energy consumption raw data through data acquisition.

[0007] The user typed data processing module, specifically through raw data optimization processing and community user data classification, and based on class variance, constructs a fuzzy lower approximate matrix and introduces an adaptive feature weight learning mechanism to construct an energy consumption feature selection algorithm, then obtains a typed optimal feature set, obtains an optimal feature data set of each type of energy consumption and the corresponding weight;

[0008] The acquisition of each type of monitoring sub-model module is used to construct independent energy consumption monitoring sub-models for different user types, specifically, a unified energy consumption monitoring model is first constructed, based on the unified energy consumption monitoring model, corresponding energy consumption monitoring sub-models are constructed for different user types and trained, then an improved optimization algorithm is used to construct a sub-model hyperparameter optimization algorithm to obtain the optimal hyperparameter combination of each energy consumption monitoring sub-model, finally, the optimal hyperparameter combination is used to update the hyperparameter configuration of each sub-model to obtain the optimal energy consumption monitoring sub-model of each type of user;

[0009] The energy consumption intelligent monitoring module, specifically, inputs the real-time energy consumption data of each type of user into the corresponding optimal energy consumption monitoring sub-model to obtain real-time monitoring results of each type of user energy, and realizes community energy consumption classification monitoring.

[0010] Further, the data acquisition module, specifically, through multi-source data collection from the community energy management platform, obtains community energy consumption raw data; the community energy consumption raw data includes historical energy consumption data and real-time energy consumption data; the historical energy consumption data and real-time energy consumption data both include community user data, energy consumption record data of each user in the community and community environment data; the historical energy consumption data also includes historical energy monitoring results of each user.

[0011] Further, the user typed data processing module, specifically includes the following steps:

[0012] Raw data optimization processing, specifically, data cleaning and data standardization are performed on the raw data to obtain community energy consumption optimization data;

[0013] Community user data classification, specifically, according to the user type in the community user data, the community energy consumption optimization data is divided into each type of user energy consumption data;

[0014] The construction of the energy consumption feature selection algorithm includes the following steps:

[0015] Initial algorithm parameters, specifically, a feature selection decision table is constructed , and the number of iterations is initialized , the maximum number of iterations is set , and the feature weight correlation parameter is initialized wherein U denotes a set of user data samples of each type, denotes a set of user energy consumption features of each type, denotes an energy monitoring result decision variable;

[0016] a class variance of the feature, specifically, a class variance of each energy consumption feature in the sample subset of each type of energy consumption monitoring result is calculated; the formula used is as follows:

[0017] ;

[0018] wherein, denotes a class variance of the kth energy consumption feature in the sample subset of the tth type of energy consumption monitoring result, denotes the sample subset of the tth type of energy consumption monitoring result, denotes the number of samples in the sample subset of the tth type of energy consumption monitoring result, denotes the mean value of all samples of the kth energy consumption feature in the sample subset of the tth type of energy consumption monitoring result, denotes the value of the kth energy consumption feature at the ith sample, denotes the kth energy consumption feature, denotes the ith sample;

[0019] an approximate matrix under the fuzzy feature is constructed, specifically, for each energy consumption feature, any two samples in the sample set are traversed, the fuzzy similarity relation of the two samples in the energy consumption feature is calculated based on the class variance of the feature, then the approximate value of each energy consumption feature under the fuzzy feature is calculated based on the fuzzy similarity relation, and finally the approximate matrix under the fuzzy feature of each energy consumption feature is obtained; the formula used is as follows:

[0020] ;

[0021] ;

[0022] wherein, denotes the fuzzy similarity relation of the sample and in the energy consumption feature , denotes the jth sample, denotes the value of the kth energy consumption feature at , denotes the value of the sample to in the energy consumption feature , denotes the membership degree of the sample to , denotes a minimum value function, This represents the function that takes the maximum value.

[0023] Energy consumption characteristic weight optimization includes the following steps:

[0024] The objective function for weight optimization is designed by constructing an objective function that integrates fuzzy approximation error, decision error, and regularization term; the formula used is as follows:

[0025] ;

[0026] In the formula, This represents the value of the objective function for weight optimization. This represents the energy consumption monitoring result indication matrix. Indicates energy consumption characteristics The fuzzy approximation matrix is ​​given by the element in the i-th row and t-th column. , Indicates the Tth iteration Weight, Represents the decision matrix based on energy consumption characteristics. This represents an n-dimensional vector whose elements are all 1s. Describing the Frobenius norm, This represents the weight of the energy consumption feature in the T-th iteration. and The regularization parameters are used to constrain decision error and weight sparsity, respectively.

[0027] The energy consumption feature weights are calculated by updating the feature weight association parameters using gradient descent, thereby continuously decreasing the objective function value during the iteration process. Based on the updated feature weight association parameters, the updated energy feature weights are then calculated. The formula used is as follows:

[0028] ;

[0029] ;

[0030] In the formula, Indicates the time of the Tth iteration The weighted correlation parameters, Indicates the first During the next iteration The weighted correlation parameters, Represents the learning rate, with a range of values. , This represents the weight optimization objective function value pair. The partial derivatives, No. Second iteration Weight, Indicates the first During the next iteration The weighted correlation parameters, This represents the l-th energy consumption characteristic;

[0031] The weight iteration optimization terminates when the iteration termination condition is met during the iteration process. Specifically, the iterative update of the energy feature weights is stopped, and the energy consumption feature weights of the current iteration stage are output as the optimal energy consumption feature weights.

[0032] The optimal feature subset is selected by sorting the weights of the optimal energy consumption features in descending order and calculating the cumulative weight percentage of the top s energy consumption features. If the cumulative weight percentage is greater than a weight percentage threshold, then the top s energy consumption features are selected to form the optimal energy consumption feature subset. The formula used is as follows:

[0033] ;

[0034] In the formula, Indicates the cumulative weight percentage. express The optimal weights, where m represents the number of energy consumption features;

[0035] To obtain the optimal feature set for each type, the user data of each type is input into the energy consumption feature selection algorithm to obtain the optimal feature dataset and corresponding weights for each type of energy consumption.

[0036] Furthermore, the acquisition of various types of monitoring sub-model modules specifically includes the following steps:

[0037] The energy consumption monitoring model is constructed by building a dynamic time-series feature extraction layer for energy consumption and an output layer for energy consumption monitoring results.

[0038] The construction of the energy consumption dynamic time series feature extraction layer specifically involves weighting the input energy consumption features with their corresponding weights to form a weighted energy consumption feature sequence, and then inputting it into a bidirectional long short-term memory network to obtain the energy consumption dynamic time series features.

[0039] The construction of the energy consumption monitoring result output layer specifically involves inputting the dynamic time-series features of energy consumption into a fully connected neural network and a Softmax classifier, outputting the probability distribution corresponding to each energy consumption state level, and determining the energy monitoring result based on the category corresponding to the highest probability.

[0040] The construction and training of energy consumption monitoring sub-models are specifically based on the energy consumption monitoring model. Energy consumption monitoring sub-models are constructed for different user types, and the energy consumption monitoring training data corresponding to each type of user is input into the corresponding energy consumption monitoring sub-model. The models are trained separately to obtain the trained energy consumption monitoring sub-models for each type of user.

[0041] The sub-model hyperparameter optimization algorithm is constructed by including the following steps:

[0042] The initial population is generated by encoding the hyperparameters of the energy consumption monitoring sub-model into individual position vectors, and randomly initializing and generating the position vectors of N individuals according to the upper and lower boundaries of the hyperparameters to obtain the initial population.

[0043] Calculate the individual fitness value, specifically by calculating the fitness value of individuals in the population; use the performance of the energy consumption monitoring sub-model established based on the individual's location as the individual fitness value;

[0044] The initial update position is determined by two strategies based on the contraction behavior strategy and random conditions; the formula used is as follows:

[0045] ;

[0046] In the formula, It indicates that the i-th individual is in the first position. Initial update position in the next iteration This represents the optimal position of the global individual. Parameters representing individual information interaction. Indicates the first The position of a random individual in the next iteration. Indicates the first The optimal individual position in the next iteration. express Random numbers within a range This indicates the position update control parameter for the i-th individual. It indicates that the i-th individual is in the first position. Position in the next iteration Indicates the number of iterations. Indicates the maximum number of search iterations;

[0047] The dynamic diversity adjustment position is specifically achieved by dynamically adjusting the initial update position through a probability-triggered mechanism; the formula used is as follows:

[0048] ;

[0049] In the formula, It indicates that the i-th individual is in the first position. The position is dynamically updated in each iteration. , and express Random numbers within a range Pc represents the adjustment magnitude constant, and Pc represents the population change control parameter. Indicates the first The optimal individual position in the initial update of the next iteration;

[0050] The local breakthrough update position is specifically achieved by optimizing the position locally through a multi-vector mean mutation strategy; the formula used is as follows:

[0051] ;

[0052] In the formula, It indicates that the i-th individual is in the first position. Position in the next iteration Indicates two firsts The mean of the random individual positions in each iteration. Indicates a first The average position of the random individual and the globally optimal individual in each iteration. Indicates the mutation factor in the early stage of iteration. Indicates the mutation factor in the later stage of iteration;

[0053] The iterative search terminates when, specifically, the fitness values ​​of all updated individuals are recalculated, and the fitness values ​​of all individuals are compared to update the global optimal position of the individual. When any search termination condition is met, the search is terminated and the global individual optimal position is obtained. The global individual optimal position specifically refers to the optimal hyperparameter combination of the energy consumption monitoring sub-model.

[0054] To obtain the optimal hyperparameter combination, the sub-model hyperparameter optimization algorithm is used to search for the hyperparameters of each energy consumption monitoring sub-model and obtain the optimal hyperparameter combination for each energy consumption monitoring sub-model.

[0055] The optimal hyperparameter update of the sub-model is specifically to update the hyperparameter configuration of each energy consumption monitoring sub-model according to the optimal hyperparameter combination of each energy consumption monitoring sub-model, so as to obtain the optimal energy consumption monitoring sub-model for each type of user.

[0056] Furthermore, the intelligent energy consumption monitoring module specifically inputs the real-time energy consumption monitoring data corresponding to each type of user into the corresponding optimal energy consumption monitoring sub-model for each type of user to obtain the real-time energy monitoring results for each type of user, and realizes intelligent monitoring of the community's energy consumption status based on these results.

[0057] The beneficial effects achieved by the present invention using the above solution are as follows:

[0058] (1) In view of the technical problem that the traditional community energy consumption monitoring system monitors the overall energy consumption of the community without distinguishing the energy use differences of different types of users, resulting in inaccurate energy consumption monitoring results, the scheme innovatively introduces a community user type difference analysis mechanism. First, the user type data processing module is used to divide and select the features of the user type data, and the energy consumption monitoring sub-models of each type of user are constructed and trained respectively. The energy consumption behavior characteristics of different user groups can be learned and differentiated independently, which improves the accuracy of energy consumption monitoring. The system supports energy consumption monitoring of multiple energy types and different scale community scenarios, effectively enhances the universality and scalability of the system, realizes classified monitoring of community energy consumption, and significantly improves the refinement and intelligence level of smart community energy management.

[0059] (2) In view of the technical problem that the existing energy consumption feature selection algorithm cannot distinguish the internal distribution difference of different result categories and cannot automatically adjust the feature weight according to the data characteristics, resulting in poor adaptability to complex data and weak fitting ability, and further causing inaccurate energy consumption feature extraction results, poor energy consumption monitoring model performance and low output result precision, the scheme innovatively proposes an energy consumption feature selection algorithm based on class variance construction fuzzy lower approximation matrix and introduction of adaptive feature weight learning mechanism. The algorithm can accurately capture the internal density difference of different energy consumption categories and automatically adjust the weight according to the data distribution characteristics, improve the discriminant ability of features in high-density and sparse categories, improve the feature selection accuracy and stability, remove redundant and low-contribution features, reduce the input dimension and computational complexity of the model, and at the same time enhance the performance of the energy consumption monitoring model and the accuracy of the output results, realizing intelligent management of community energy consumption.

[0060] (3) In view of the technical problem that the existing energy consumption monitoring sub-model hyperparameter setting is not reasonable, and the individual is easy to fall into local optimum in the hyperparameter optimization process, the model has low search precision, and the output result of the monitoring sub-model has low accuracy, the scheme innovatively uses a three-stage progressive update strategy and a multi-vector mean mutation strategy in the particle swarm optimization algorithm, which widens the search range of hyperparameters and avoids premature convergence in the optimization process, ensuring the balance between global search ability and local exploration ability in the optimization process, improving the fineness and accuracy of hyperparameter optimization, and improving the optimization efficiency and accuracy of the energy consumption monitoring sub-model. Finally, the classified and intelligent monitoring of community energy consumption is realized. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The module schematic diagram of the community energy consumption monitoring system based on big data provided by the present application is shown in the figure;

[0062] Figure 2 The flowchart of the user type data processing module is shown in the figure;

[0063] Figure 3 a flowchart for obtaining a monitoring sub-model module of each type;

[0064] Figure 4 a flowchart for constructing an energy consumption feature selection algorithm in the user typed data processing module;

[0065] Figure 5 a flowchart for constructing a sub-model hyperparameter optimization algorithm in the module for obtaining a monitoring sub-model of each type;

[0066] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, which are used together with embodiments of the present application to explain the present application, and do not constitute a limitation of the present application. DETAILED DESCRIPTION

[0067] 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 of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0068] Embodiment one, refer to Figure 1 The community energy consumption monitoring system based on big data provided by the present application comprises a data acquisition module, a user typed data processing module, a module for obtaining monitoring sub-models of each type, and an energy consumption intelligent monitoring module.

[0069] The data acquisition module specifically acquires community energy consumption raw data through data collection and sends the data to the user typed data processing module.

[0070] The user typed data processing module receives the data sent by the data acquisition module, specifically processes the raw data and classifies community user data, and constructs an energy consumption feature selection algorithm based on a fuzzy lower approximation matrix and an adaptive feature weight learning mechanism, then obtains a typed optimal feature set, acquires optimal feature data sets of each type of energy consumption and corresponding weights, and sends the data to the module for obtaining monitoring sub-models of each type and the energy consumption intelligent monitoring module.

[0071] The obtaining each type of monitoring sub-model module receives the data sent by the user type data processing module, and is used for constructing independent energy consumption monitoring sub-models for different user types, specifically, a unified energy consumption monitoring model is first constructed, based on the unified energy consumption monitoring model, corresponding energy consumption monitoring sub-models are constructed for different user types and are trained, then, an improved optimization algorithm is used to construct a sub-model hyperparameter optimization algorithm, to obtain the optimal hyperparameter combination of each energy consumption monitoring sub-model, finally, the optimal hyperparameter combination of each sub-model is updated according to the optimal hyperparameter combination, to obtain the optimal energy consumption monitoring sub-model of each type of user, and the data is sent to the energy consumption intelligent monitoring module;

[0072] The energy consumption intelligent monitoring module receives the data sent by the user type data processing module and the obtaining each type of monitoring sub-model module, inputs the real-time energy consumption data of each type of user into the corresponding optimal energy consumption monitoring sub-model, obtains the real-time monitoring result of the energy of each type of user, and realizes the classified monitoring of community energy consumption.

[0073] By performing the above operation, in view of the technical problem that in the traditional community energy consumption monitoring system, the community is monitored in terms of overall energy consumption, the energy use differences of different types of users are not distinguished, and the energy consumption monitoring result is inaccurate, the community user type difference analysis mechanism is innovatively introduced, the user type data is divided and the features are selected by the user type data processing module, and the energy consumption monitoring sub-models of each type of user are constructed and trained, the energy consumption behavior characteristics of different user groups can be independently learned and differentiated, the energy consumption monitoring precision is improved, the energy consumption monitoring of multiple energy types and different scale community scenes is supported, the universality and scalability of the system are effectively enhanced, the classified monitoring of community energy consumption is realized, and the fine and intelligent level of intelligent community energy management is significantly improved.

[0074] Embodiment two, refer to Figure 1 The embodiment is based on the above-mentioned embodiment, the data acquisition module specifically acquires the community energy consumption raw data by collecting multi-source data from the community energy management platform; the community energy consumption raw data includes historical energy consumption data and real-time energy consumption data; the historical energy consumption data and the real-time energy consumption data both include community user data, energy consumption record data of each user in the community and community environment data;

[0075] The community environment data includes temperature, humidity, wind speed, weather events, ambient light intensity, season, month, holiday and day and night period identifier;

[0076] The community user data includes user type, user quantity, floor area and personnel density;

[0077] The user types include a household type, a business type, and a public facility type;

[0078] The energy consumption record data of each user in the community includes electric power energy consumption record data, water energy consumption record data, gas energy consumption record data, and heat energy consumption record data;

[0079] The historical energy consumption data further includes historical energy monitoring results of each user;

[0080] The energy monitoring results include a normal level, a slight abnormal level, a general abnormal level, and a serious abnormal level.

[0081] Embodiment Three, refer to Figure 1 、 Figure 2 and Figure 4 , based on the above-mentioned embodiments, the user type data processing module specifically includes the following steps:

[0082] The original data optimization processing is specifically data cleaning and data standardization of the original data to obtain community energy consumption optimization data;

[0083] The community energy consumption optimization data includes historical energy consumption optimization data and real-time energy consumption optimization data;

[0084] The data cleaning is used to eliminate errors, missing values, and inconsistencies in the data, and is specifically missing value filling and outlier removal of the original data;

[0085] The missing value filling is specifically to complete the values with missing values by the mean filling method;

[0086] The outlier removal is specifically to detect and remove extreme values and logical outliers in the original data by the IQR method;

[0087] The data standardization is used to eliminate differences in time scale, data format, measurement unit, and value range of multi-source collected data, and specifically includes time alignment, format standardization, unit standardization, and data normalization;

[0088] The time alignment is specifically to map original data with different sampling frequencies, different upload delays, and time stamp deviations to a unified time axis;

[0089] The format standardization is specifically to unify the original data into standard fields and data types;

[0090] The unit standardization is specifically to unify the standard units of the original data;

[0091] The data normalization is specifically Min-Max normalization for all continuous variables, ensuring that the data is within a uniform range, and using one-hot encoding method to convert categorical fields into numerical form that can be recognized by the model;

[0092] Community user data classification is used to differentiate the optimized community energy consumption data according to user types; specifically, according to the user types in the community user data, the community energy consumption optimization data is divided into various types of user energy consumption data;

[0093] The various types of user energy consumption data include household energy consumption data, commercial energy consumption data and public facility energy consumption data;

[0094] The household energy consumption data includes historical household energy consumption data and household energy consumption data;

[0095] The commercial energy consumption data includes historical commercial energy consumption data and commercial energy consumption data;

[0096] The public facility energy consumption data includes historical public facility energy consumption data and public facility energy consumption data;

[0097] An energy consumption feature selection algorithm is constructed, specifically including the following steps:

[0098] Initial algorithm parameters, specifically constructing a feature selection decision table , and initializing the number of iterations , setting the maximum number of iterations , initializing the feature weight correlation parameters , wherein U represents the sample set of each type of user data, represents the energy consumption feature set of each type of user, represents the energy monitoring result decision variable; the formula used is as follows:

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] In the formula, represents the first sample, represents the second sample, represents the nth sample, represents the first energy consumption feature, represents the second energy consumption feature, represents the mth energy consumption feature, represents a normal level, represents a slight abnormal level, represents a general abnormal level, represents a serious abnormal level, represents a sample subset corresponding to the normal level, represents a sample subset corresponding to the slight abnormal level, represents a sample subset corresponding to the general abnormal level, represents a sample subset corresponding to the serious abnormal level, represents a first energy consumption feature weight correlation parameter value, represents a second energy consumption feature weight correlation parameter value, represents an mth energy consumption feature weight correlation parameter value, m represents the number of energy consumption features;

[0105] Calculate the class variance of the feature, which is used to capture the distribution difference of the energy consumption feature under different energy consumption abnormal levels, solve the problem of ignoring the class density difference by using the overall variance in the traditional method, adapt to the heterogeneity of community energy data, and specifically calculate the class variance of each energy consumption feature in the sample subset of each class of energy consumption monitoring result. The formula is as follows:

[0106] ;

[0107] In the formula, represents the class variance of the kth energy consumption feature in the sample subset of the tth energy consumption monitoring result, represents the sample subset of the tth energy consumption monitoring result, represents the number of samples in the sample subset of the tth energy consumption monitoring result, represents the mean value of all samples of the kth energy consumption feature in the sample subset of the tth energy consumption monitoring result, represents the value of the kth energy consumption feature at the ith sample, represents the kth energy consumption feature, represents the ith sample;

[0108] Construct a fuzzy approximation matrix of the feature, which is used to quantify the stability of the description of the energy consumption feature to the energy consumption abnormal level. Specifically, for each energy consumption feature, traverse any two samples in the sample set, calculate the fuzzy similarity relationship of the two samples in the energy consumption feature based on the class variance of the feature, and then calculate the fuzzy approximation value of each energy consumption feature based on the fuzzy similarity relationship. Finally, the fuzzy approximation matrix of each energy consumption feature is obtained.

[0109] The construction of the fuzzy lower approximation matrix is specifically to arrange all the fuzzy lower approximation values of the energy consumption characteristics according to the samples The dimensional arrangement of the abnormal level forms the fuzzy lower approximation matrix corresponding to the characteristics, the number of rows of the matrix is the total number of samples n, and the number of columns of the matrix is the total number of energy consumption abnormal levels 4; the formula used is as follows:

[0110] ;

[0111] ;

[0112] ;

[0113] In the formula, indicates the sample and The fuzzy similarity relationship under the energy consumption characteristics , the value range is , indicates the jth sample, indicates the value of the kth energy consumption characteristic under , indicates the value of the sample under the energy consumption characteristics to , indicates the membership degree of the sample to , indicates the minimum value function, indicates the maximum value function;

[0114] Energy consumption characteristic weight optimization, used to accurately adapt the energy consumption characteristic differences of various users to the characteristics, specifically including the following steps:

[0115] Design a weight optimization objective function, specifically to construct an objective function that integrates fuzzy approximation error, decision error and regularization term; the formula used is as follows:

[0116] ;

[0117] ;

[0118] ;

[0119] In the formula, indicates the weight optimization objective function value, the smaller the value, the more accurate the current characteristic weight in describing the energy consumption monitoring result, indicates the energy consumption monitoring result indication matrix, the dimension is , denotes the element in the i-th row and the t-th column of the energy consumption monitoring result indicator matrix, used to mark the sample ; denotes the real energy consumption monitoring result of the sample ; denotes the fuzzy lower approximation matrix of the energy consumption feature ; denotes the weight of the T-th iteration ; denotes the energy consumption feature decision matrix, with the dimension of , if the energy consumption feature can match the energy consumption monitoring result of the sample , then the element in the i-th row and the k-th column of the energy consumption feature decision matrix is , otherwise ; denotes an n-dimensional vector with all elements being 1, denotes the Frobenius norm, denotes the energy consumption feature weight of the T-th iteration, and denotes the regularization parameter, with the value range of , respectively constraining the decision error and the weight sparsity;

[0120] The condition that the energy consumption feature can match the energy consumption monitoring result of the sample is that, when the energy consumption feature satisfies that the value conforms to the typical distribution of the monitoring result to which the sample belongs , wherein, denotes the standard deviation of the k-th energy consumption feature in the sample subset of the t-th energy consumption monitoring result;

[0121] The energy consumption feature weight is calculated for adaptive optimization of the energy consumption feature weight, specifically, the feature weight correlation parameter is updated by the gradient descent method, so that the weight optimization objective function value is continuously reduced in the iteration process, and based on the updated feature weight correlation parameter, the updated energy feature weight is calculated; the formula is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] In the formula, denotes the weight correlation parameter of in the T-th iteration, denotes the During the next iteration The weighted correlation parameters, Represents the learning rate, with a range of values. , This represents the weight optimization objective function value pair. The partial derivatives, This represents the diagonal matrix of energy consumption feature weights in the T-th iteration. This represents the outer product matrix of the energy consumption feature weights in the T-th iteration. No. Second iteration Weight, Indicates the first During the next iteration The weighted correlation parameters, This represents the l-th energy consumption characteristic. and Represents the regularization parameter;

[0126] The weight iteration optimization terminates when the iteration termination condition is met during the iteration process. Specifically, the iterative update of the energy feature weights is stopped, and the energy consumption feature weights of the current iteration stage are output as the optimal energy consumption feature weights.

[0127] The iteration termination conditions include reaching the maximum number of iterations and convergence of the weight optimization objective function value;

[0128] The maximum number of iterations is specifically defined as if the current number of iterations exceeds a preset maximum number of iterations. If the iteration terminates, then the iteration ends.

[0129] The convergence of the objective function value for weight optimization is specifically defined as follows: if the difference between the objective function values ​​of two adjacent iterations is less than a preset convergence threshold, then the iteration is terminated.

[0130] The optimal feature subset is selected by sorting the weights of the optimal energy consumption features in descending order and calculating the cumulative weight percentage of the top s energy consumption features. If the cumulative weight percentage is greater than a weight percentage threshold, then the top s energy consumption features are selected to form the optimal energy consumption feature subset. The formula used is as follows:

[0131] ;

[0132] In the formula, Indicates the cumulative weight percentage. express The optimal weight;

[0133] To obtain the optimal feature set for each type, the user data of each type is input into the energy consumption feature selection algorithm to obtain the optimal feature dataset and corresponding weights for each type of energy consumption.

[0134] By performing the above operation, in order to solve the technical problems that the existing algorithm suitable for energy consumption feature selection cannot distinguish the internal distribution difference of different result categories and cannot automatically adjust the feature weight according to the data characteristics, resulting in poor adaptability to complex data and weak fitting ability, and further causing inaccurate energy consumption feature extraction results, poor energy consumption monitoring model performance and low output result precision, the scheme innovatively proposes an energy consumption feature selection algorithm based on class variance, fuzzy lower approximation matrix and adaptive feature weight learning mechanism, which can accurately capture the internal density difference of different energy consumption categories and automatically adjust the weight according to the data distribution characteristics, improve the discrimination ability of features in high density and sparse categories, improve the feature selection precision and stability, remove redundant and low contribution features, reduce the input dimension and computational complexity of the model, and at the same time enhance the performance of the energy consumption monitoring model and the accuracy of the output results, realizing the intelligent management of community energy consumption.

[0135] In the fourth embodiment, refer to Figure 1 、 Figure 3 and Figure 5 , based on the above-mentioned embodiments, the acquisition of each type of monitoring sub-model module specifically includes the following steps:

[0136] The energy consumption monitoring model is constructed, specifically by constructing an energy consumption dynamic time series feature extraction layer and constructing an energy consumption monitoring result output layer to obtain the energy consumption monitoring model;

[0137] The energy consumption dynamic time series feature extraction layer is used to extract the dynamic features of energy consumption changing with time, specifically by weighting the input energy consumption features and their corresponding weights to form a weighted energy consumption feature sequence, and inputting it into a bidirectional long short-term memory network to obtain energy consumption dynamic time series features;

[0138] The energy consumption monitoring result output layer is used for energy consumption state recognition and abnormal level determination, specifically by inputting the energy consumption dynamic time series features into a fully connected neural network and a Softmax classifier to output the probability distribution corresponding to each energy consumption state level, and determining the energy monitoring result according to the class corresponding to the maximum probability;

[0139] The energy consumption monitoring sub-model is constructed and trained, specifically based on the energy consumption monitoring model, the energy consumption monitoring sub-models for different user types are constructed respectively, and the energy consumption monitoring training data corresponding to each type of user is input into the corresponding energy consumption monitoring sub-model for model training, to obtain the trained energy consumption monitoring sub-models for each type of user;

[0140] The energy consumption monitoring training data corresponding to each type of user is specifically the historical data of the energy consumption data of each type of user after the energy consumption feature selection algorithm;

[0141] The sub-model training specifically involves using the cross-entropy loss function and iteratively adjusting the model weight matrix and bias vector through the backpropagation algorithm until the error converges.

[0142] The sub-model hyperparameter optimization algorithm is constructed by including the following steps:

[0143] The initial population is generated by encoding the hyperparameters of the energy consumption monitoring sub-model into individual position vectors, and randomly initializing and generating the position vectors of N individuals according to the upper and lower boundaries of the hyperparameters to obtain the initial population.

[0144] The model hyperparameters include the number of LSTM layers, the dimension of hidden states, the number of neurons in the hidden layers, the time step, and the Dropout ratio, and upper and lower boundaries are set for the value of each hyperparameter.

[0145] The fitness value of an individual is calculated to evaluate the performance of the model under different combinations of hyperparameters. Specifically, the fitness value of an individual in the population is calculated; the performance of the energy consumption monitoring sub-model established based on the individual's location is used as the fitness value of the individual.

[0146] The initial update position is used to provide a preliminary optimization position with global exploratory capabilities, addressing the problem of narrow global search range and susceptibility to local optima in traditional optimization algorithms. Specifically, based on the shrinking behavior strategy, two strategies are used to update the individual position according to random conditions; the formulas used are as follows:

[0147] ;

[0148] ;

[0149] In the formula, It indicates that the i-th individual is in the first position. Initial update position in the next iteration This represents the optimal position of an individual globally. Parameters representing individual information interaction, adjusting the intensity of information transmission, and their range of values. , Indicates the first The position of a random individual in the next iteration. Indicates the first The optimal individual position in the next iteration. express Random numbers within a range This indicates the position update control parameter for the i-th individual. It indicates that the i-th individual is in the first position. Position in the next iteration Indicates the number of iterations. Indicates the maximum number of search iterations. This represents the fitness value of the i-th individual. This represents the global optimal fitness value for an individual.

[0150] Dynamic diversity adjustment of the update location is used to address potential issues of population clustering and insufficient diversity at the initial update location. Specifically, it involves dynamically adjusting the initial update location through a probability-triggered mechanism; the formula used is as follows:

[0151] ;

[0152] In the formula, It indicates that the i-th individual is in the first position. The position is dynamically updated in each iteration. , and express Random numbers within a range The value represents the adjustment amplitude constant, with a value of 0.1. Pc represents the population change control parameter, with a value range of [value missing]. , Indicates the first The optimal individual position in the initial update of the next iteration;

[0153] The local breakthrough update position is used to obtain the final update position that balances global optimum and local refinement. It is the initial position in the next iteration. Specifically, it is optimized locally by using a multi-vector mean mutation strategy. The formula used is as follows:

[0154] ;

[0155] ;

[0156] ;

[0157] In the formula, It indicates that the i-th individual is in the first position. Position in the next iteration Indicates two firsts The mean of the random individual positions in each iteration. Indicates a first The average position of the random individual and the globally optimal individual in each iteration. , and Both indicate that a random individual is in the 1st... Position in the next iteration This represents the mutation factor in the early stage of iteration, with a value range of [value missing]. , This represents the mutation factor in the later stages of the iteration, with a value range of [value missing]. ;

[0158] The iterative search is terminated, specifically, the fitness values of the individuals are recalculated for all the updated individuals, and the fitness values of all the individuals are compared to update the global individual optimal position When any of the search termination conditions is met, the search is terminated and the global individual optimal position is obtained, specifically, the optimal hyperparameter combination of the energy consumption monitoring submodel;

[0159] The search termination conditions include when the individual optimal position is higher than the fitness threshold and the maximum number of search iterations is reached;

[0160] The optimal hyperparameter combination is obtained, specifically, the hyperparameters of each energy consumption monitoring submodel are searched by the submodel hyperparameter optimization algorithm to obtain the optimal hyperparameter combination of each energy consumption monitoring submodel;

[0161] The submodel optimal hyperparameter is updated, specifically, the hyperparameter configuration of each energy consumption monitoring submodel is updated according to the optimal hyperparameter combination of each energy consumption monitoring submodel to obtain the optimal energy consumption monitoring submodel of each type of user.

[0162] Further, the energy consumption intelligent monitoring module specifically inputs the real-time energy consumption monitoring data of each type of user into the corresponding optimal energy consumption monitoring submodel of each type of user to obtain real-time monitoring results of each type of user, and realizes intelligent monitoring of the community energy consumption state according to the results.

[0163] The real-time monitoring results of each type of user include real-time monitoring results of household energy, real-time monitoring results of commercial energy, and real-time monitoring results of public facility energy.

[0164] The real-time energy consumption monitoring data of each type of user is specifically the real-time data of the energy consumption data of each type of user selected by the energy consumption feature selection algorithm.

[0165] By performing the above operations, the technical problem of low accuracy of the output results of the monitoring submodel due to the lack of reasonable hyperparameter settings of the existing applicable energy consumption monitoring submodel and the individual easily falling into local optimum in the hyperparameter optimization process, and the low search precision of the model hyperparameters, is solved. The three-stage progressive updating strategy and the multi-vector mean mutation strategy are innovatively used in the particle swarm optimization algorithm to broaden the search range of the hyperparameters and avoid premature convergence in the optimization process, ensuring the balance between global search ability and local exploration ability in the optimization process, improving the precision and accuracy of the hyperparameter optimization, improving the optimization efficiency and precision of the energy consumption monitoring submodel, and finally realizing the classification and intelligent monitoring of community energy consumption.

[0166] Embodiment five, see Figure 1The embodiment is based on the above embodiment, and the energy consumption intelligent monitoring module is specifically configured to input energy consumption monitoring real-time data of each type of user into corresponding energy consumption monitoring sub-models of each type of user respectively, to obtain energy real-time monitoring results of each type of user, and to realize intelligent monitoring of a community energy consumption state according to the results.

[0167] The energy real-time monitoring results of each type of user include household energy real-time monitoring results, commercial energy real-time monitoring results and public facility energy real-time monitoring results.

[0168] The energy consumption monitoring real-time data of each type of user is specifically real-time data of energy consumption data of each type of user obtained through an energy consumption feature selection algorithm.

[0169] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application.

[0170] The above describes the present application and its embodiments, which are not limited, and the embodiments shown in the drawings are only one of the embodiments of the present application, and the actual structure is not limited thereto. In summary, if a person skilled in the art is inspired, without departing from the principles and spirits of the present application, without creative design, similar structure and embodiments of the technical solution should belong to the protection scope of the present application.

Claims

1. A community energy consumption monitoring system based on big data, characterized in that: It includes a data acquisition module, a user-typed data processing module, a module for acquiring various types of monitoring sub-models, and an intelligent energy consumption monitoring module; The data acquisition module specifically obtains raw data on community energy consumption through data collection. The user-typed data processing module specifically optimizes and processes raw data and classifies community user data. It then constructs an approximate matrix under fuzzy conditions based on class variance and introduces an adaptive feature weight learning mechanism to build an energy consumption feature selection algorithm. Finally, it obtains the optimal feature set for each type of energy consumption and its corresponding weights. The energy consumption feature selection algorithm includes initial algorithm parameters, calculation of feature class variance, construction of feature fuzzy approximation matrix, energy consumption feature weight optimization, and selection of optimal feature subset; The module for obtaining monitoring sub-models of various types specifically involves first constructing a unified energy consumption monitoring model, then constructing and training corresponding energy consumption monitoring sub-models for different user types based on the unified energy consumption monitoring model, then constructing a sub-model hyperparameter optimization algorithm through an improved optimization algorithm to obtain the optimal hyperparameter combination for each energy consumption monitoring sub-model, and finally updating the hyperparameter configuration of each sub-model according to each optimal hyperparameter combination to obtain the optimal energy consumption monitoring sub-model for each type of user. The intelligent energy consumption monitoring module specifically inputs real-time energy consumption data of various types of users into the corresponding optimal energy consumption monitoring sub-model to obtain real-time energy monitoring results for each type of user, thereby realizing classified monitoring of community energy consumption.

2. The community energy consumption monitoring system based on big data according to claim 1, characterized in that: The user-typed data processing module specifically includes the following steps: The raw data optimization process specifically involves data cleaning and standardization to obtain optimized community energy consumption data. Community user data is categorized, specifically by dividing community energy consumption optimization data into energy consumption data for each user type based on the user type within the community user data. Construct an energy consumption feature selection algorithm; To obtain the optimal feature set for each type, the user data of each type is input into the energy consumption feature selection algorithm to obtain the optimal feature dataset and corresponding weights for each type of energy consumption.

3. The community energy consumption monitoring system based on big data according to claim 2, characterized in that: The construction of the energy consumption feature selection algorithm specifically includes the following steps: The initial algorithm parameters are specifically used to construct the feature selection decision table. and initialize the number of iterations. Set the maximum number of iterations. Initialize feature weight association parameters Where U represents the sample sets of user data of various types, This represents the energy consumption characteristic set of various user types. Indicates the decision variables based on energy monitoring results; The class variance of a feature is calculated, specifically by calculating the class variance of each energy consumption feature within the sample subset of each type of energy consumption monitoring results; the formula used is as follows: ； In the formula, This represents the class variance of the k-th energy consumption feature in the sample subset of the t-th class of energy consumption monitoring results. This represents a subset of the energy consumption monitoring results for the t-th category. This represents the number of samples in the subset of energy consumption monitoring results for category t. Let represent the mean of all samples in the subset of energy consumption monitoring results for the k-th energy consumption characteristic in the t-th category. This represents the value of the k-th energy consumption feature in the i-th sample. This represents the k-th energy consumption characteristic. This represents the i-th sample; Construct an approximate matrix under feature fuzziness; Optimization of energy consumption characteristic weights; The optimal feature subset is selected by sorting the weights of the optimal energy consumption features in descending order and calculating the cumulative weight percentage of the top s energy consumption features. If the cumulative weight percentage is greater than a weight percentage threshold, then the top s energy consumption features are selected to form the optimal energy consumption feature subset. The formula used is as follows: ； In the formula, Indicates the cumulative weight percentage. express The optimal weight is given by m, which represents the number of energy consumption features.

4. The community energy consumption monitoring system based on big data according to claim 3, characterized in that: The construction of the feature-based fuzzy approximation matrix specifically involves, for each energy consumption feature, iterating through any two samples in the sample set, calculating the fuzzy similarity relationship between the two samples in terms of energy consumption features based on the class variance of the features, and then calculating the fuzzy approximation value of each energy consumption feature based on the fuzzy similarity relationship, ultimately obtaining the fuzzy approximation matrix for each energy consumption feature; the formula used is as follows: ； ； In the formula, Indicates sample and In terms of energy consumption characteristics Fuzzy similarity relationships, Let j represent the j-th sample. This indicates that the k-th energy consumption characteristic is in The value of , Indicates sample In terms of energy consumption characteristics Below The value, Indicates sample right membership degree This represents the function that takes the minimum value. This represents the function that takes the maximum value.

5. The community energy consumption monitoring system based on big data according to claim 3, characterized in that: The optimization of energy consumption feature weights specifically includes the following steps: The objective function for weight optimization is designed by constructing an objective function that integrates fuzzy approximation error, decision error, and regularization term; the formula used is as follows: ； In the formula, This represents the value of the objective function for weight optimization. This represents the energy consumption monitoring result indication matrix. Indicates energy consumption characteristics The fuzzy approximation matrix is ​​given by the element in the i-th row and t-th column. , Indicates the Tth iteration Weight, Represents the decision matrix based on energy consumption characteristics. This represents an n-dimensional vector whose elements are all 1s. Denotes the Frobenius norm. This represents the weight of the energy consumption feature in the T-th iteration. and The regularization parameters are used to constrain decision error and weight sparsity, respectively. The energy consumption feature weights are calculated by updating the feature weight association parameters through gradient descent, so that the value of the weight optimization objective function decreases continuously during the iteration process, and the updated energy feature weights are calculated based on the updated feature weight association parameters. The formula used is as follows: ； ； In the formula, Indicates the time of the Tth iteration The weighted correlation parameters, Indicates the first During the next iteration The weighted correlation parameters, Represents the learning rate, with a range of values. , This represents the weight optimization objective function value pair. The partial derivatives, No. Second iteration Weight, Indicates the first During the next iteration The weighted correlation parameters, This represents the l-th energy consumption characteristic; The weight iteration optimization terminates when the iteration process meets the iteration termination condition. Specifically, if the iteration process meets the iteration termination condition, the iterative update of the energy feature weights is stopped, and the energy consumption feature weights of the current iteration stage are output as the optimal energy consumption feature weights. The iteration termination condition includes reaching the maximum number of iterations and the convergence of the weight optimization objective function value.

6. The community energy consumption monitoring system based on big data according to claim 1, characterized in that: The module for acquiring various types of monitoring sub-models specifically includes the following steps: The energy consumption monitoring model is constructed by building a dynamic time-series feature extraction layer for energy consumption and an output layer for energy consumption monitoring results. The construction of the energy consumption dynamic time series feature extraction layer specifically involves weighting the input energy consumption features with their corresponding weights to form a weighted energy consumption feature sequence, and then inputting it into a bidirectional long short-term memory network to obtain the energy consumption dynamic time series features. The construction of the energy consumption monitoring result output layer specifically involves inputting the dynamic time-series features of energy consumption into a fully connected neural network and a Softmax classifier, outputting the probability distribution corresponding to each energy consumption state level, and determining the energy monitoring result based on the category corresponding to the highest probability. The construction and training of energy consumption monitoring sub-models are specifically based on the energy consumption monitoring model. Energy consumption monitoring sub-models are constructed for different user types, and the energy consumption monitoring training data corresponding to each type of user is input into the corresponding energy consumption monitoring sub-model. The models are trained separately to obtain the trained energy consumption monitoring sub-models for each type of user. Construct a sub-model hyperparameter optimization algorithm; To obtain the optimal hyperparameter combination, the sub-model hyperparameter optimization algorithm is used to search for the hyperparameters of each energy consumption monitoring sub-model and obtain the optimal hyperparameter combination for each energy consumption monitoring sub-model. The optimal hyperparameter update of the sub-model is specifically to update the hyperparameter configuration of each energy consumption monitoring sub-model according to the optimal hyperparameter combination of each energy consumption monitoring sub-model, so as to obtain the optimal energy consumption monitoring sub-model for each type of user.

7. The community energy consumption monitoring system based on big data according to claim 6, characterized in that: The algorithm for constructing the sub-model hyperparameter optimization specifically includes the following steps: The initial population is generated by encoding the hyperparameters of the energy consumption monitoring sub-model into individual position vectors, and randomly initializing and generating the position vectors of N individuals according to the upper and lower boundaries of the hyperparameters to obtain the initial population. Calculate the individual fitness value, specifically by calculating the fitness value of individuals in the population; use the performance of the energy consumption monitoring sub-model established based on the individual's location as the individual fitness value; The initial update position is determined by two strategies based on the contraction behavior strategy and random conditions; the formula used is as follows: ； In the formula, Indicates that the i-th individual is in the i-th position. Initial update position in the next iteration This represents the optimal position of an individual globally. Parameters representing individual information interaction. Indicates the first The position of a random individual in the next iteration. Indicates the first The optimal individual position in the next iteration. express Random numbers within a range This indicates the position update control parameter for the i-th individual. Indicates that the i-th individual is in the i-th position. Position in the next iteration Indicates the number of iterations. Indicates the maximum number of search iterations; The dynamic diversity adjustment position is specifically achieved by dynamically adjusting the initial update position through a probability-triggered mechanism; the formula used is as follows: ； In the formula, Indicates that the i-th individual is in the i-th position. The position is dynamically updated in each iteration. , and express Random numbers within a range Pc represents the adjustment magnitude constant, and Pc represents the population change control parameter. Indicates the first The optimal individual position in the initial update of the next iteration; The local breakthrough update position is specifically achieved by optimizing the position locally through a multi-vector mean mutation strategy; the formula used is as follows: ； In the formula, Indicates that the i-th individual is in the i-th position. Position in the next iteration Indicates two firsts The mean of the random individual positions in each iteration. Indicates a first The average position of the random individual and the globally optimal individual in each iteration. Indicates the mutation factor in the early stage of iteration. Indicates the mutation factor in the later stage of iteration; The iterative search terminates when, specifically, the fitness values ​​of all updated individuals are recalculated, and the fitness values ​​of all individuals are compared to update the global optimal position of the individual. When any search termination condition is met, the search is terminated and the global individual optimal position is obtained. The global individual optimal position specifically refers to the optimal hyperparameter combination of the energy consumption monitoring sub-model.

8. The community energy consumption monitoring system based on big data according to claim 1, characterized in that: Specifically, the intelligent energy consumption monitoring module inputs real-time energy consumption monitoring data corresponding to each type of user into the corresponding optimal energy consumption monitoring sub-model for each type of user to obtain real-time energy monitoring results for each type of user, and realizes intelligent monitoring of the community's energy consumption status based on these results.

9. The community energy consumption monitoring system based on big data according to claim 1, characterized in that: The data acquisition module specifically obtains raw community energy consumption data by collecting data from multiple sources from the community energy management platform. The raw community energy consumption data includes historical energy consumption data and real-time energy consumption data. Both the historical energy consumption data and the real-time energy consumption data include community user data, energy consumption records of each user in the community, and community environmental data. The historical energy consumption data also includes historical energy monitoring results of each user.

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