94 results about "Power user" patented technology

The invention relates to the technical field of electric power, in particular to a line loss abnormity diagnosis method based on electric quantity fluctuation analysis. The method comprises the following steps: acquiring an electricity user and electricity data; calculating a power supply quantity change rate and a power consumption change rate according to the daily power supply quantity of the transformer area and the fluctuation degree of the daily power consumption of the transformer area, and further judging a line loss abnormity type; if the line loss abnormity type is user side abnormity, screening the electricity users, and obtaining a user side abnormity type according to the screened electricity users; if the line loss abnormity type is power supply side abnormity, calculating a power supply quantity multiple, and performing fault attribution to obtain a power supply side abnormity type; and explaining the two exception types through the SHAP so as to generate a line loss exception diagnosis report. In this way, the adaptability and robustness of the diagnosis method in a novel scene containing a distributed power supply and the like can be enhanced, the interpretability of a diagnosis result is further improved, and a clear and credible feature basis is provided for field check.

The invention discloses a non-intrusive power user identification method based on self-supervised comparative learning, and relates to the technical field of non-intrusive power monitoring and user identification, and the method comprises the steps: input data are electricity utilization power time sequence data recorded by equipment such as a household intelligent electric meter; inputting the data into a double-layer contrast learning framework based on clustering guidance, and completing power user identification in combination with an adaptive time-frequency feature enhancement mechanism; the method comprises the following steps: constructing a double-layer contrast learning module, introducing a CEEMDAN algorithm to decompose IMF components of power consumption data of different dates, mapping distribution characteristics of the IMF components into selection probabilities of convolution kernels with different widths by a training network, completing adaptive selection of the convolution kernels by means of a Gumbel-softmax mechanism, removing accidental fluctuation of data through convolution in a time domain, and obtaining a time domain; and finally, fusing features of different dates through an attention module, mapping the features into probability distribution through a full connection layer, and classifying the probability distribution, thereby finally realizing accurate identification of non-intrusive power consumers.

The invention discloses a block chain enabling smart power grid transaction system based on a game theory, and relates to the technical field of regional chains, and the system comprises a strategy management module which is used for making a buying and selling strategy based on the optimal utility through the game theory according to the electric meter storage space of a user, power utilization historical information and transaction record information, the electric equipment of the whole network is dispatched through the block chain; the transaction contract module is used for creating and updating a transaction contract based on the operation strategy formulated by the strategy management module; and the user management module is used for managing the information of the power users and the historical electricity utilization records. According to the invention, an efficient, fair and transparent power transaction mode is constructed by using the security of the block chain technology and the real-time response characteristic of the smart power grid, and the method aims at fully utilizing the distributed power storage units to achieve power utilization peak regulation so as to reduce the energy loss.

The present application belongs to the technical field of electrical equipment state monitoring and fault diagnosis, and particularly relates to an automatic electricity stealing identification method based on data mining technology.The present application constructs an "electric-thermal-acoustic" multi-physical field coupling benchmark physical model through a BP neural network, mines the dynamic correlation law between the current and the temperature, acoustic emission physical quantities through the benchmark physical model, effectively captures the multi-dimensional physical quantity abnormal coupling characteristics caused by electricity stealing behavior, and through the model residual blood sequence output by the benchmark physical model, the deviation degree between the temperature measured value and the acoustic emission measured value of the actual collected power user equipment under the current power utilization state and the predicted value output by the BP neural network can be reflected, so as to strengthen the identification accuracy of electricity stealing behavior.

This invention discloses a method for constructing electricity user profiles, comprising: S1: acquiring long-term electricity consumption data of electricity users, clustering the long-term electricity consumption data to obtain classification clusters, extracting features of the classification clusters, and obtaining user profiles; S2: selecting users from each classification cluster and acquiring short-term electricity consumption data of the selected users to construct an electricity consumption prediction model; S3: acquiring payment data and arrears data of the selected users to construct an arrears prediction model; S4: acquiring electricity consumption behavior data of the selected users to construct an electricity consumption behavior prediction model; S5: refining the user profiles based on the electricity consumption prediction model, the arrears prediction model, and the electricity consumption behavior prediction model. This invention also provides a construction apparatus. This invention can improve the accuracy and real-time performance of user profiles.

The invention discloses a multi-source collaborative power consumer portrait construction method and system, a medium and equipment, and belongs to the field of user power consumption behavior analysis, and the method comprises the steps: collecting multi-source data of each user ammeter, including electric energy metering data, electricity price data and meteorological data; constructing load characteristics, meteorological characteristics and electricity price characteristics of each user based on the multi-source data; wherein the cooling load characteristics in the meteorological characteristics are obtained by measuring the morphological similarity of a daily load curve and a daily temperature curve and performing recognition in combination with a clustering algorithm; integrating the load, weather and electricity price characteristics of each user to obtain corresponding combined characteristics; and clustering the combined features through a clustering algorithm to obtain a plurality of user groups, and allocating power consumption behavior tags to the user groups in combination with service rules to complete user portrait construction. By implementing the method and the device, the technical problems of inaccurate and incomplete power consumer portraits caused by single data dimension and lack of multi-source collaborative analysis in the prior art can be solved.

The present application relates to the technical field of intelligent management, in particular to a power user account intelligent management method based on data analysis. The method comprises: obtaining weight coefficients corresponding to each dimension based on the local density estimation value of each power user under each dimension and the feature distance between power users; weighting the feature distance between power users under the corresponding dimension based on the weight coefficients corresponding to each dimension to obtain the metric distance between different power users, performing density peak value clustering on all power users based on the metric distance between different power users, and classifying and managing the power user accounts based on the clustering results. The present application can improve the effect of managing power user accounts.

The present application relates to a kind of suitable for multiple types of mass power user data completion method, comprising the following steps: step 1, the power consumption data of multiple types of power users is collected;Step 2, the power consumption data of single different user is decomposed into the sum of trend term and periodic term;Step 3, periodic term verification is carried out;Step 4, the power consumption data of single user collected in step 1 and the periodic term, trend term time series vector after decomposition in step 2 are carried out standard delay transformation, obtain the hankel form tensor;Step 5, establish time series smoothing constraint and periodic term constraint;Step 6, establish comprehensive tensor completion objective function;Step 7, solve the comprehensive tensor completion objective function obtained in step 6 and output complete data by anti-MDT technology.The present application can be applied to one-dimensional and multidimensional data scene, and the completion effect is good.

The invention discloses an enterprise electricity user grouping and label evolution method, relates to the technical field of artificial intelligence of a power system, and realizes accurate grouping, dynamic label updating and behavior trajectory tracking of enterprise electricity users by constructing a multi-level feature extraction system, improving an incremental clustering algorithm and establishing a label evolution and traceability graph. The method can effectively solve the problems of insufficient feature fusion, low clustering efficiency, tag staticization and the like in the traditional technology, provides a more accurate user portrait tool for a power grid enterprise, supports key power utilization enterprise identification, energy storage potential evaluation, demand response scheduling and other core services, and improves the user experience. The method plays an important technical support role in improving the load side management level of a power system, promoting new energy consumption and assisting the construction of a novel power system.

The application discloses a power metering data combination value mining algorithm, system and device, and the method comprises the following steps: scanning power distribution power metering activity information of an original database, converting according to user demand, and obtaining a target data set; designing a pruning strategy to screen the target data set, and judging a strong correlation high utility item set based on a target query to obtain a strong correlation high utility item set with the target query; then, projection merging is performed to obtain a power metering item set with a combination value. The system comprises a data acquisition module, a data conversion module, a data screening module, a candidate judgment module and a projection merging module. The device comprises a memory and a processor for executing the power metering data combination value mining algorithm. By using the application, the maximum profit of the supplier can be realized, and the power distribution combination that satisfies the customer demand of the power user can be provided. The application can be widely applied to the technical field of data mining.

The invention provides a power grid operation risk assessment, regulation and control method and system. The method comprises the following steps: clustering operation data of each power user in a target area into a plurality of user groups; constructing a risk assessment index set based on the common risk feature and the personalized risk feature of each user group; inputting the risk assessment index set into an index weight adjustment model to obtain an index weight of each risk assessment index; based on the risk assessment index set of each user group, the index weight of each risk assessment index in the risk assessment index set and the corresponding operation data, calculating a power grid operation risk assessment result of the target area; and on the basis of a power grid operation risk assessment result, executing load interruption or load limitation on a user load control terminal in the target area, and / or executing power supply line switching through a power distribution automation system in the target area. The calculated power grid operation risk assessment result is closer to the actual risk condition, and safe and stable operation of the power grid is facilitated.

The invention discloses a new energy transaction settlement deviation compensation method, device and equipment and a computer program, and the method comprises the steps: determining transaction settlement decision variables of a new energy generator, an electricity selling company and a power user, and determining a settlement deviation variable; constructing a power generator objective function, a power selling company objective function and a user objective function, and determining compensation coefficient sensitivity indexes of the new energy power generator, the power selling company and the power user; constructing a deviation compensation optimization model based on game equilibrium, and constructing a compensation coefficient dynamic adjustment model; and solving the deviation compensation optimization model through a particle swarm algorithm fusing a chaos particle swarm and Nash equilibrium guidance to obtain an optimal solution of a transaction settlement decision variable, determining an optimal compensation coefficient according to the optimal solution and a compensation coefficient dynamic adjustment model, and further performing new energy transaction settlement deviation compensation. The accuracy, fairness and real-time performance of new energy transaction settlement deviation compensation are improved, and the method can be applied to the technical field of electricity markets.

The application discloses a kind of devices and methods for assisting individual power users to participate in power market quotation, device includes data input module: the type of power market that individual power user participates in is input, the transaction period under the type of participation, the power that each transaction period participates in;Data storage module is arranged by device operator, in networked state, daily update operator issued market information and user information by each device;Data analysis module: for the certain power market of certain time scale of individual power user, carry out the analysis of suggested quotation based on the historical transaction information of power market and the analysis of suggested quotation based on the historical transaction result of individual power user;Data output module: the result obtained by data analysis module, the visualized suggested quotation output for user reference, weaken the hindrance of individual power user participating in power market, promote its enthusiasm of participating in power market transaction, promote demand side resource to participate in power market, guarantee the safe and stable operation of power system.

The invention discloses a demand response strategy optimization method and a related device. The method comprises the following steps: generating an uncertain scene set for constructing new energy output; obtaining a load curve of the power consumer in a preset time period; determining a response load and a load directrix according to the load curve; determining comprehensive responsivity according to the load curve and the load directrix; constructing a first-stage model according to the uncertain scene set and the start-stop cost of the thermal power generating unit; solving the first-stage model according to the response load and the comprehensive responsivity to obtain a target operation cost; constructing a second-stage model according to the target operation cost, the response load and the comprehensive responsivity; and solving the first stage model and the second stage model to obtain a target demand response strategy. It can be seen that the optimized target demand response strategy is obtained by constructing the two-stage model and solving, the target demand response strategy can enable the user to dynamically adjust the response behavior, and the coping capacity of the virtual power plant to the new energy fluctuation is enhanced.

The invention relates to a data processing method specially suitable for the purpose of business prediction, in particular to an electric charge pre-collection balance prediction method based on a combined model. The prediction method comprises the following steps: acquiring historical electric charge pre-collection balance time sequence data, meteorological monitoring data, economic statistical index data and user classification data of a power user; clustering and grouping are carried out based on the power utilization characteristic difference of each province through a province clustering module, and a differentiated modeling strategy is established; dividing the historical data into training data and test data through a sliding window according to a time sequence; an independent SARIMA model and an independent LSTM (Long Short Term Memory) model are respectively constructed for each province; and fusing the predicted value of the combined model and outputting a prediction result. According to the method, the advantages of the SARIMA model and the LSTM model are fused, and the defect of insufficient fitting of sudden fluctuation and complex nonlinear modes is effectively overcome.

1. Name of the product in this design: Flexible Control Terminal for Power Load. 2. Application of this design: The power load flexible control terminal refers to a hardware device installed on the user side for collecting, predicting and analyzing, testing, controlling and providing services for power users, load aggregators, virtual power plants, etc. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention relates to an adjustable capacity optimization method and system for a data center participating in a peak shaving auxiliary service market, and the method comprises the steps: obtaining a data center computing power task plan, power load prediction data, power grid peak shaving auxiliary service demand information and external environment information, and carrying out the preprocessing; according to the preprocessed data, constructing a computing power-electric power cooperative regulation and control cost model; with maximization of computing power user satisfaction and minimization of data center operation cost as targets, according to the computing power-electric power cooperative regulation and control cost model, constructing a double-target optimization model of the adjustable capacity of the data center participating in the peak shaving auxiliary service market; solving the dual-objective optimization model by adopting a multi-objective particle swarm optimization algorithm to obtain an optimal load scheduling scheme; and obtaining the optimal adjustable capacity according to the optimal load scheduling scheme. According to the invention, the efficiency and economy of the data center participating in the peak regulation auxiliary service can be improved.

The application discloses a kind of mass power user clustering algorithm based on data-physical feature joint driving, comprising the following steps: S1, single-user daily typical load curve clustering;S2, standardization user daily typical load data, calculate the standard value of user daily typical load;S3, multi-user daily typical load curve clustering;S4, automatic iteration, find the clustering number that satisfies contour coefficient and industry concentration standard;S5, output the user clustering result corresponding to the best clustering number.The application is based on the mass user historical power consumption data accumulated in power consumption information acquisition system, excavates and grasps the production characteristics and power demand of each industry and user, not only can improve the load prediction accuracy and dispatching management level of power grid company, but also can provide accurate data support and decision basis for electricity price setting, economic dispatching, demand response, etc.

The invention relates to the technical field of electric power informatization, in particular to a deep mining and structured intelligent auditing method and system oriented to high and low voltage electric power user address data and a computer readable storage medium. Address standardization is achieved through multi-source data cleaning and duplicate removal, electric power exclusive unified coding formulation and three-level restoration of regular fuzzy matching, alias mapping conversion and hierarchical membership completion; an address feature-voltage class rule base is established, and association between addresses and voltage classes is established through forward and reverse verification; intelligent monitoring and closed-loop optimization are realized by combining scenarized hierarchical verification and business system coupling. The method improves the qualified rate of the address data, reduces the labor cost, and achieves the collaborative optimization of data management and business application.

The invention discloses a user electric energy meter missort identification method and device based on electric power big data and a medium, and belongs to the technical field of electric power information. The method comprises the following steps: determining a user set of electric energy meters in the same meter box based on power user information in a power master station database; obtaining active power of each user in the user set at a plurality of different time points every day in n days before and after meter replacement, and daily power consumption of each user in n days before and after meter replacement; calculating an active power correlation coefficient between each user and each user according to the active power of each user before meter changing and the active power of each user after meter changing; calculating an electricity consumption fluctuation coefficient of each user before and after meter replacement according to the daily electricity consumption; and identifying the electric energy meter missort pair of the user set according to the active power correlation coefficient and the electric quantity fluctuation coefficient. According to the method, the electric energy meter missort pair can be accurately identified by analyzing the electric power big data, the operation and maintenance cost is reduced, and the identification efficiency of the electric energy meter missort is improved.

The invention relates to an intelligent recommendation method and device based on load prediction and multi-agent cooperation and computer equipment. The method comprises the following steps: inputting power consumption data of a power consumer into a data analysis model to obtain analyzed power consumption data, and inputting the analyzed power consumption data into a load prediction model to obtain a load prediction result; constructing a plurality of agents representing different power roles through an agent role division model, and controlling the plurality of agents to perform information interaction and cooperation through a cooperation mechanism model to obtain optimized push information; and determining a user portrait of the power user through a user portrait construction model, and determining personalized push information matched with the power user from the optimized push information through a recommendation model according to the load prediction result and the user portrait. By adopting the method, the accuracy of power information pushing can be improved.

The invention discloses an electric power material purchase recommendation method and device, and the method comprises the steps: obtaining and extracting material information and material browsing data in power user browsing information, and forming a material browsing vector; a plurality of material browsing vectors are fused to obtain user browsing vectors, association degrees of different user browsing vectors are given, user similarity coefficients are given, the user browsing vectors are fused, to-be-recommended material information is analyzed through a pre-constructed double-tower model, and purchase scores corresponding to the to-be-recommended material information are given; and sorting the to-be-recommended material information according to a preset filtering function in combination with the purchase score, and completing purchase recommendation of the electric power materials. According to the method, the browsing behaviors of the power users and the browsed to-be-recommended material information are analyzed by adopting the double-tower model, and the similarity condition between the power users is analyzed in combination with the Pearson coefficient, so that accurate purchase suggestions are provided for purchase of the power materials, and the purchase recommendation accuracy of the power materials is improved.

The application discloses a power user portrait method considering privacy protection and demand response, and mainly comprises the following steps: firstly, collecting historical load data and historical temperature and humidity data of a building, and extracting load characteristics of the building; secondly, determining the optimal number of user portraits by minimizing the overlap degree; thirdly, a smart scheduling terminal in the building performs local calculation and introduces zero error, and the calculation result is used as an initial value of a distributed privacy protection AAC algorithm; finally, according to the initial value of the distributed privacy protection AAC algorithm, the user parameter value is updated, and a typical user portrait and a typical user portrait type to which the building belongs are obtained through convergence. The application can analyze the user portrait according to the user electricity habit without collecting specific load data of the user, and the user portrait can be used in a demand response system, so that the user with different electricity habits can formulate a targeted electricity strategy, the response capability of the building load is better explored, and the user privacy is protected.

The invention, which relates to the technical field of the power grid, discloses an electric power measurement data synchronization return and supplement optimization method comprising the following steps: step 1, data calling; 2, establishing an electricity consumption model; step 3, data analysis; according to the electric power measurement data synchronous return and supplement optimization method, eight electricity consumption crowds are set, an electricity consumption model is established for each crowd, eight crowd electricity consumption models of the electric power users are established, and electric quantity change judgment is carried out through family composition of the electric power users. And the prediction accuracy of the electric quantity change of the power consumer is improved.

The application discloses a power user service demand prediction method based on big data analysis, comprising the following steps: collecting power user data, and pre-processing the power user data; analyzing the pre-processed data by using a wavelet transform clustering algorithm to divide power user categories; combining the power user categories and historical power consumption data to establish a power demand prediction model; predicting the power user service demand by using the power demand prediction model, and visualizing the prediction result; by combining the clustering algorithm and the neural network, the application can process massive data, accurately predict the power user service demand, visualize the prediction result, and realize fine management of the power user.

The invention provides a user electricity price portrait construction method and system based on feature clustering, and relates to the field of user feature analysis, and the method comprises the steps: obtaining electricity consumption data and electricity price data of each power user in a power user set, extracting the electricity price feature of each power user from the electricity consumption data and the electricity price data, and forming a feature data set; on the basis of the preprocessed feature data set, grouping a power consumer set by using a K-means clustering algorithm; and taking the clustering center of each group as a typical user of the group, carrying out portrait construction on each group according to feature data of the typical user, obtaining a multi-dimensional information label of each group, and taking the multi-dimensional information label as an electricity price portrait of the power user in the group. According to the method, the multi-dimensional feature information is combined, the user electricity price portrait is generated, and market-oriented user electricity consumption transaction behaviors can be accurately analyzed.

The application provides a power user load feature recognition classification evaluation method, comprising: identifying start interval and power superposition time through analysis of start-stop sequence change identification, grouping the start interval and power superposition time according to equipment type and power level to obtain equipment start-stop time combination; evaluating start time difference and power superposition time difference of different users with the same production process in the same industry according to the equipment start-stop time combination, and identifying user electricity behavior similarity; identifying industrial user electricity load data through subdivision of user category label, evaluating load prediction accuracy and electricity mode stability according to the industrial user electricity load data to obtain electricity load feature; updating equipment start-stop time record database according to the electricity load feature, and obtaining final electricity load data recognition classification evaluation result through analysis of the electricity load feature.

The application discloses a kind of based on multi-scale decomposition and Hampel filter power user adjustable load feature extraction method, system, storage medium and computing device.The method is first based on Fourier transform to the frequency spectrum analysis of load data, identify the main frequency component therein to extract periodic component, and based on variational mode decomposition to the trend component obtained after the residual signal decomposition of stripping periodic component;Robust denoising is carried out to periodic component based on dynamic window Hampel filter;Combining the production scale factor provided by trend component and the adjustable load vector obtained after periodic component denoising, the adjustable load multidimensional feature vector reflecting user demand response potential is constructed.The different scale components obtained by the decomposition of load data in time domain and frequency domain are processed and information is fused in cooperation, to provide high reliability feature input for demand response potential evaluation.

The application discloses a demand response scheduling method and device based on high-proportion renewable energy and energy storage, relates to the technical field of energy optimization scheduling, and comprises the following steps: step 1, historical target power load data of a target area, daily output optimization influencing factors of each generator unit, daily charge / discharge optimization influencing factors of energy storage equipment and historical meteorological data are acquired; step 2, based on the data acquired in step 1, a target function optimization model is constructed based on the total power generation cost of a power system in the target area, and an optimal solution of the target function is acquired; and step 3, based on the optimal solution, a source-network-load-storage interactive scheduling scheme is output; by taking the total power generation cost of the power system in the target area as the lowest target to solve the target function, the optimal daily output of the unit and the optimal energy storage can be obtained, a more accurate and efficient daily power consumption configuration scheme is provided for power users, the wind and light self-sufficiency and self-consumption rate is improved, and the power consumption cost of the power users is reduced.

The application relates to a power user clustering method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring time series data of historical power consumption of a power user; adopting a minimum-maximum normalization mode to map the time series data of the historical power consumption to a preset value range; extracting historical power consumption features of the power user below a preset dimension from the normalized time series data of the historical power consumption; clustering by using a Gaussian mixture model according to the historical power consumption features of the power user below the preset dimension, and performing convergence processing on the clustering result; and classifying the power user according to the clustering result after the convergence processing. The method can take into account both high-dimensional data dimension reduction and time series characteristics, and improve the clustering accuracy of power user clustering.