171 results about "Power user" patented technology

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention relates to an important user-oriented power failure event digital management method, which comprises the following steps of S1, acquiring basic user data, and constructing a power user knowledge graph; s2, training a user importance scoring model, performing user automatic grading by applying a clustering algorithm, and constructing a user digital twinborn model; s3, constructing an equipment data acquisition network; s4, according to the equipment data acquired by the equipment data acquisition network, identifying a potential fault based on the time sequence prediction model; and S5, constructing a power failure propagation model based on a graph neural network, predicting a fault propagation path according to a potential fault identified by a time sequence prediction model, and combining a user digital twinning model to realize digital twinning of the real-time state of the power grid, and visually displaying the health state of the power grid. And S6, generating risk early warning in a customized manner for different levels of important users according to the predicted fault spreading path. The intelligent level of power failure management is improved, and the power supply reliability of important users is remarkably improved.

The invention discloses an electric power big data privacy protection method and system based on federated learning, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a historical session record set of an electric power user, carrying out the standardization preprocessing of the historical session record set, and obtaining a standardized session data set, extracting a time sequence feature set from the standardized session data set, calling a pre-configured federal learning framework to carry out privacy protection processing on the time sequence feature set, generating a privacy enhancement feature set, generating a power data privacy protection result based on the privacy enhancement feature set, and sending the power data privacy protection result to the server. And feeding back the power data privacy protection result to the power service system. According to the method, the privacy security in the data use process is guaranteed, the utilization value of the power big data in the unstructured session scene is improved, and the problem of adaptability of a traditional power privacy protection technology in unstructured session data processing is effectively solved.

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.

Techniques for limiting or reducing storage accessibility “hiccups” in active-active clustered systems that perform large writes. The techniques can include executing, by a surviving, failover, or rebooted node of an active-active clustered system, a specialized recovery protocol that includes treating each large write request from a host computer as a plurality of small write requests while execution of the specialized recovery protocol is in progress, draining all dedicated sub-ubers for a primary and secondary node of the active-active clustered system, and, having completed execution of the specialized recovery protocol, resuming normal treatment of large write requests from the host computer. In this way, the need and complexity of managing large writes and maintaining their corresponding sub-uber information during recovery from a forced reboot, crash, or disaster involving the primary or secondary node can be avoided, and storage accessibility hiccups due to performing the large writes can be limited or reduced.

The present invention discloses a method for detecting anomalies of electric power and electricity based on big data, which relates to the technical field of anomaly detection of electric power and electricity. Aiming at the problem of high false alarms and missed alarms caused by dynamic changes in the electricity consumption behavior of power users, the method provides online concept drift detection, model adaptive updating, group anomaly determination and multi-scale fusion recognition. Potential pattern changes are discovered through sliding windows and thresholds, and a list of suspected anomalies is generated. Short-term models are corrected using incremental training and long-term models are periodically updated. Subsequently, multi-scale error metrics are fused into multi-scale anomaly scores, and key anomalies and routine anomalies are determined in combination with group deviation rates. Finally, adaptive adjustments are made through threshold correction indicators and the manual annotation results are fed back to the model library, so as to achieve accurate distinction and continuous learning between normal behavior changes and real anomalies. Through this solution, the false alarm and missed alarm rates can be significantly reduced, and the reliability and refinement of power dispatching and monitoring can be improved.

The invention belongs to the technical field of energy management, and discloses a user-side-oriented source-load-storage integrated comprehensive energy management system, and the hardware architecture comprises a sensing layer, a communication layer and a platform layer. On-site facilities of a source side, a load side and a storage side in the hardware architecture are accessed to the integrated energy management system based on the data collection and calculation unit, and data analysis and display are performed through the server; the software function architecture comprises a data layer, a service layer, an application layer and a display layer; the software function architecture supports the realization of the functions of stable power utilization, flexible scheduling and intelligent management of the source-load-storage integrated comprehensive energy management system; the system further comprises a terminal sensing module, a data collection and calculation unit and an optimization control strategy of the comprehensive energy management system. By adopting the source-load-storage integrated comprehensive energy management system facing the user side, the power consumption cost of the power consumer is reduced, the monitoring, analysis and management capabilities of the source-load-storage comprehensive energy are improved, and the distributed new energy consumption is promoted.

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 cloud-side cooperation-based power consumer power consumption data abnormity monitoring method and system, and relates to the technical field of cloud-side cooperation, and the method comprises the steps: obtaining the position of equipment through a GPS, constructing a basic portrait of a power consumer, optimizing the deployment of edge equipment, and achieving the real-time collection of basic power consumption data and environment data. And then de-noising the data and extracting features, identifying an equipment type by using a prediction model, constructing an interference model in combination with a timestamp, environmental data and the equipment type, correcting a basic power utilization function matrix to obtain a standard matrix, and further realizing anomaly monitoring and early warning through deviation comparison. The system iteratively optimizes an interference model through regional convergence data to form an acquisition-analysis-correction-monitoring closed loop. According to the method, the monitoring real-time performance is improved through a cloud edge collaborative architecture, the cost is reduced by utilizing a lightweight model and space optimization deployment, and the problems of high delay, poor adaptability and the like of traditional centralized monitoring are solved.

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 invention relates to the technical field of data security protection, in particular to a security and privacy protection method for data service of a regional power marketing management information platform, which comprises the following steps of: inputting original data of a power user into a trusted execution environment, adding dynamic noise to the data in the trusted execution environment and generating irreversible anonymous variation data; generating a dynamic access strategy based on the consensus verification result of each computing node, wherein the dynamic access strategy comprises a data access permission threshold value and an abnormal access consensus mark; and segmenting the anonymous variation data into a plurality of dynamic fragments, adding a unique recombination verification code to each fragment, and transmitting the fragments to a target terminal through an independent communication link. According to the invention, the risk of cross-regional joint node collusion tampering or cheating access strategies is effectively resisted; and furthermore, an abnormal access consensus result is used for driving an authority threshold index to decay, so that fine-grained and dynamic access control is realized.

The invention discloses a method and a system for identifying, early warning and disposing potential safety hazards of power utilization of power consumers. The method comprises the following steps: acquiring multi-source power data; according to the multi-source electric power data, based on a hidden danger recognition model, spatio-temporal data association and a device-line-user three-level positioning mechanism, performing recognition, early warning, anomaly backtracking and positioning on the hidden danger of electricity utilization of the user; and inputting potential safety hazard identification early warning and positioning related information into the power utilization safety risk disposal large model to generate a disposal strategy. According to the invention, through adoption of a method of processing data through multi-source data fusion and a three-level positioning mechanism, the data treatment capability and the hidden danger disposal capability are enhanced, the risk identification and classification accuracy is improved, and the early warning response time is shortened.

The invention discloses a power consumer group intelligent identification method, system and device based on a PSO-KMeans algorithm, and a medium, and belongs to the technical field of power big data analysis, and the method comprises the steps: collecting original load data of power consumers, carrying out the preprocessing of the original load data, obtaining standardized load data, extracting multi-dimensional features, and constructing a weighted feature matrix based on an entropy weight method; performing coarse clustering by utilizing spectral clustering to generate an initial clustering center set, taking the initial clustering center set as an initial particle position of an improved adaptive inertia weight PSO algorithm, optimizing a K-Means clustering center, and outputting a global optimal clustering center; and performing clustering by taking the result as a K-Means initial center to obtain a final user group, and identifying abnormal power utilization suspected users in combination with a contour coefficient and a local outlier factor. According to the method, high-quality and high-stability user grouping and accurate anomaly detection are realized, and efficient technical support is provided for power user management and demand side response.

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.

The present application relates to the technical field of power dispatch, in particular to a non-uniform power supply power data acquisition calculation method, comprising: each non-uniform power supply concentrator collecting active power sampling data of each electric energy meter in real time, processing and storing to obtain non-uniform power supply real-time active power data; each non-uniform power supply concentrator transmitting non-uniform power supply active power data to the power information acquisition system master station of the management information district in real time through the boundary firewall between the wireless public network and the power user power information acquisition system; and accumulating and calculating the non-uniform power supply power data of each region and the non-uniform power supply power data directly acquired by the dispatching data network to obtain real-time data of the total non-uniform power supply power of the dispatching range. The non-uniform power supply power is acquired by the power information acquisition and regulation and control cloud interaction mode, the safe and stable operation of the equipment is preferentially ensured, and the real-time and stability of the active power data transmission are effectively ensured.

The invention relates to a power market-oriented power selling data analysis method and system, and the method comprises the steps: analyzing the historical purchase data and actual power consumption of a plurality of power users in the power duration, obtaining the power purchase deviation inertial characteristics corresponding to a user group, and enabling the power purchase deviation inertial characteristics to reflect the power purchase strategy of the power users. And then predicting the electricity consumption predicted quantity of the power consumer in the next time period by adopting an electricity consumption variable quantity prediction model based on the long-short-term memory network, carrying out differencing on the electricity consumption predicted quantity and the agreed quantity in the next time period, if the difference value can be matched with the electricity purchase deviation inertial characteristic, indicating that the difference value is an inertial difference value based on a user strategy, and if the difference value is not matched with the electricity purchase deviation inertial characteristic, indicating that the difference value is not an inertia difference value based on a user strategy. And if not, it is indicated that the power utilization plan is changed, and the predicted power utilization amount can be corrected by using the result, so that the predicted power utilization amount is more accurate.

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.

1. Name of the designed product: power user information display. 2. Use of the designed product: the designed product is used for power balance display and power related message notification. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

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 present invention relates to the field of data processing technology, and specifically refers to a method, device, equipment, and computer-readable storage medium for completing a low-rank matrix of electricity consumption of electricity users in electricity spot transactions, comprising: obtaining required time series data and decomposing it; obtaining meteorological data, converting it into a label set, and defining a label sequence; defining a disturbance term and a regularization term based on the fact that a subsequence of residual terms under similar labels is close to a representative vector after quantization of the subsequence; defining a smoothing trend regularization term based on the fact that the fluctuation between adjacent moments of the smoothing trend term is small; establishing an objective function, updating the objective function using a stochastic gradient descent method, iterating to a preset number of iterations, obtaining a completed data matrix, comparing it with the original matrix, and solving the missing value of electricity using a mean method. When data is missing, the present invention updates the data, reduces data sparsity, improves the accuracy of data completion, improves the settlement efficiency of spot transactions in the electricity market, and ensures the data integrity and reliability of load aggregators participating in spot transactions.

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 invention provides an off-grid operation control method of an optical storage and charging micro-grid and a computer program product, and relates to the field of data processing. The off-grid operation control method of the optical storage and charging micro-grid comprises the following steps: acquiring operation state data of the optical storage and charging micro-grid; simulating and predicting a power balance state after the optical storage and charging micro-grid is switched to off-grid operation based on the operation state data by using micro-grid digital twin bodies pre-configured in the edge side controller; and formulating a load regulation and control strategy according to the power balance state, and executing the load regulation and control strategy after obtaining an off-grid instruction. By using the scheme of the invention, the optical storage and charging micro-grid dynamically adjusts the load adjustment strategy after the off-grid according to the operation state data, the pertinence is stronger, the equipment impact caused by directly cutting off the load is avoided, and the satisfaction degree of the electricity user is improved.

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.