Power load real-time data acquisition and analysis system and method

Abstract

The invention relates to the technical field of power load analysis methods, in particular to a power load real-time data collection and analysis system and method, and the method comprises the steps: collecting power load real-time data through an Internet of Things terminal disposed at a user side, and uploading the power load real-time data through a preset communication protocol; receiving the uploaded power load real-time data, performing cleaning and standardization processing on the power load real-time data, and storing the data after aggregation according to a preset dimension; carrying out load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data; applying load analysis and prediction data to strategy support before electric power spot transaction; performing post-transaction analysis and strategy optimization based on market clearing and settlement results; performing multi-terminal visual display and interface output on the whole process data, the analysis result and the transaction view; according to the invention, analysis and real-time safety early warning can be carried out on power load characteristics, and the timeliness and accuracy of early warning are improved.

Description

Technical Field

[0001] This invention relates to the field of power load analysis methods, and in particular to a real-time power load data acquisition and analysis system and method. Background Technology

[0002] Against the backdrop of energy structure transformation and deepening reforms in the electricity market, virtual power plants, as a new form of aggregating distributed energy resources to participate in system regulation and market transactions, rely on core technologies for precise sensing, intelligent analysis, and collaborative control of massive, heterogeneous, and flexible resources. In particular, in-depth characteristic analysis of the aggregated user's electricity load and the implementation of effective real-time safety early warnings are crucial foundations for ensuring stable system operation, unlocking resource value, and mitigating market risks.

[0003] Traditional power load analysis and management methods have significant limitations. At the data level, they often rely on manual meter reading or low-frequency data collection, resulting in coarse data granularity and poor real-time performance, making it difficult to depict users' detailed electricity consumption behavior and rapid load fluctuations. At the safety control level, existing technologies typically use threshold alarms, but threshold settings are static and rigid, alarm information is isolated and scattered, and there is a lack of proactive prediction of abnormal operating conditions such as equipment overload, parameter exceeding limits, and communication interruption, as well as the timeliness and accuracy of early warnings. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time power load data acquisition and analysis system and method, which can analyze power load characteristics and provide real-time safety early warnings, thereby improving the timeliness and accuracy of early warnings.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for real-time data acquisition and analysis of power load, comprising:

[0006] Real-time power load data is collected by IoT terminals deployed on the user side and uploaded via a preset communication protocol;

[0007] Receive uploaded real-time power load data, clean and standardize the real-time power load data, and store it after aggregating it according to preset dimensions;

[0008] Based on historical and real-time aggregated data, load characteristic analysis and real-time safety early warning are performed;

[0009] Apply load analysis and forecasting data to support strategies before electricity spot trading;

[0010] Based on market clearing and settlement results, conduct post-trade analysis and strategy optimization;

[0011] The entire process data, analysis results, and transaction views are visualized and output via multiple platforms and interfaces.

[0012] Among the steps, collecting real-time power load data through IoT terminals deployed on the user side and uploading it via a preset communication protocol,

[0013] The real-time power load data includes active power, three-phase current, voltage, and equipment temperature.

[0014] Among these steps, real-time power load data is collected through IoT terminals deployed on the user side and uploaded via a preset communication protocol.

[0015] The preset communication protocol is either MQTT or HTTPS, and the data transmission process implements integrity verification and communication status monitoring.

[0016] The specific steps involved in receiving uploaded real-time power load data, cleaning and standardizing the data, and then aggregating and storing it according to preset dimensions include:

[0017] The received raw real-time power load data is cleaned to remove outliers and invalid records;

[0018] The real-time power load data after cleaning is standardized by aligning timestamps and unifying units.

[0019] Standardized data is aggregated and calculated according to resource type, user category, and time granularity to generate an aggregated data view and store it in a time series database.

[0020] The specific steps for load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data include:

[0021] Typical daily, weekly, and monthly load curves are plotted based on aggregated data to identify peak and trough periods and trends in load.

[0022] Set multi-level safety thresholds for power, current, and temperature, and monitor and trigger early warnings in real time for exceeding the limits;

[0023] Based on historical electricity consumption behavior, user electricity consumption profiles are constructed to provide load forecasting and energy efficiency assessment suggestions.

[0024] The specific steps for applying load analysis and forecasting data to support pre-trade strategies in electricity spot trading include:

[0025] Based on users' historical electricity consumption and real-time load curves, predict and summarize the electricity purchase demand for future years, months, intra-month periods, and green electricity.

[0026] It integrates and visualizes data on clearing prices, supply-demand ratios, and system load conditions in the medium- and long-term electricity market and the spot market.

[0027] By integrating real-time load, weather, market boundary, and historical price data, and using machine learning algorithms to train models, future time-of-use and regional clearing prices are predicted.

[0028] By combining load forecasts, electricity price forecasts, and medium- to long-term contract positions, we can generate or simulate and recommend day-ahead reporting curves and spot trading strategies for different risk levels, and estimate various costs and benefits.

[0029] Secondly, the present invention also provides a real-time power load data acquisition and analysis system, including a data acquisition and transmission module, a data processing and aggregation module, a load analysis and early warning module, a transaction decision support module, a transaction review and optimization module, and a visualization output module; the data acquisition and transmission module, the data processing and aggregation module, the load analysis and early warning module, the transaction decision support module, the transaction review and optimization module, and the visualization output module are connected in sequence;

[0030] The data acquisition and transmission module is used to collect real-time power load data through IoT terminals deployed on the user side and upload it through a preset communication protocol;

[0031] The data processing and aggregation module is used to receive the uploaded real-time power load data, clean and standardize the real-time power load data, and store it after aggregation according to preset dimensions.

[0032] The load analysis and early warning module is used to perform load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data;

[0033] The transaction decision support module is used to apply load analysis and forecast data to pre-trade strategy support for electricity spot transactions.

[0034] The transaction review and optimization module is used to perform post-trade analysis and strategy optimization based on market clearing and settlement results;

[0035] The visualization output module is used to visualize and output the entire process data, analysis results, and transaction views across multiple platforms.

[0036] This invention discloses a real-time power load data acquisition and analysis system and method. Through multi-dimensional cleaning, aggregation, and in-depth analysis of real-time data, it not only achieves accurate profiling of user load characteristics, electricity consumption habits, and trends, but also establishes a proactive safety early warning system based on multi-level thresholds. This system can analyze power load characteristics and provide real-time safety warnings, improving the timeliness and accuracy of warnings. This allows operators to more precisely understand and manage aggregated resources, identify potential equipment hazards in advance, and significantly improve the safety and reliability of asset operation. This invention deeply integrates real-time load data, forecasting information, and market mechanisms. Through automated post-transaction review and profit / loss attribution analysis, this invention can quickly assess the effectiveness of strategies, locate the sources of risk, and, combined with automatic settlement verification, form a full-cycle risk management system covering pre-, during, and post-transaction periods, effectively reducing market transaction risks and improving economic efficiency. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0038] Figure 1 This is a flowchart of the overall method for real-time data acquisition and analysis of power load according to the present invention.

[0039] Figure 2 This is a flowchart of step S2 of the real-time data acquisition and analysis method for power load of the present invention.

[0040] Figure 3 This is a flowchart of step S3 of the real-time data acquisition and analysis method for power load of the present invention.

[0041] Figure 4 This is a flowchart of step S4 of the real-time data acquisition and analysis method for power load of the present invention.

[0042] Figure 5 This is a flowchart of step S5 of the real-time data acquisition and analysis method for power load of the present invention.

[0043] Figure 6 This is a schematic diagram of the structure of a real-time power load data acquisition and analysis system according to the present invention.

[0044] 101-Data Acquisition and Transmission Module, 102-Data Processing and Aggregation Module, 103-Load Analysis and Early Warning Module, 104-Transaction Decision Support Module, 105-Transaction Review and Optimization Module, 106-Visualization Output Module. Detailed Implementation

[0045] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0046] Please see Figures 1-5 This invention provides a method for real-time data acquisition and analysis of power load, comprising:

[0047] S1 collects real-time power load data through IoT terminals deployed on the user side and uploads it via a preset communication protocol;

[0048] In this step, the real-time power load data includes active power, three-phase current, voltage, and equipment temperature. The preset communication protocol is either MQTT or HTTPS, and integrity verification and communication status monitoring are implemented during data transmission.

[0049] In this embodiment, intelligent measurement terminals and sensors deployed on diverse resource sides such as users and energy storage power stations are used to collect key operating data such as active power, three-phase current and voltage, and equipment temperature in real time. Reliable communication protocols such as MQTT and HTTPS are used to stably upload encrypted real-time data streams to the data access layer of the virtual power plant aggregation platform, and the status of the communication link is continuously monitored and anomaly alarms are issued to ensure the real-time performance and reliability of data collection.

[0050] S2 receives the uploaded real-time power load data, cleans and standardizes the real-time power load data, and stores it after aggregating it according to preset dimensions.

[0051] The specific steps include:

[0052] S21 cleans the received raw real-time power load data, removing outliers and invalid records;

[0053] S22 performs standardized processing on the real-time power load data after cleaning, including timestamp alignment and unit unification.

[0054] S23 aggregates and calculates standardized data according to resource type, user category, and time granularity, generates an aggregated data view, and stores it in a time-series database.

[0055] In this implementation, the raw streaming data received by the platform is cleaned to remove outliers such as jumps and missing values, and standardized preprocessing is performed, including timestamp synchronization and data unit unification. The cleaned data is then written into a high-performance time-series database. Simultaneously, based on resource type, user partition, and different time granularities such as 15 minutes, hours, and days, online aggregation calculations are performed on the data to generate standardized aggregation curves and statistical data sets, providing a unified, high-quality data foundation for upper-level analytical applications.

[0056] S3 performs load characteristic analysis and real-time safety warnings based on historical and real-time aggregated data;

[0057] The specific steps include:

[0058] S31 uses aggregated data to plot typical daily, weekly, and monthly load curves, identifying peak and trough periods and trends in load.

[0059] The S32 sets multi-level safety thresholds for power, current, and temperature, and monitors and triggers warnings in real time for over-limit states;

[0060] S33 constructs user electricity consumption profiles based on historical electricity consumption behavior, and provides load forecasting and energy efficiency assessment suggestions.

[0061] In this implementation, in-depth load characteristic analysis is performed based on historical and real-time aggregated data, including plotting typical daily / weekly / monthly load curves, identifying peak and off-peak periods, predicting load trends, and constructing user electricity consumption behavior profiles. In parallel, multi-level safety thresholds for power, current, and temperature are set to monitor abnormal operating conditions such as equipment overload and exceeding limits in real time at the millisecond level. Early warning notifications are automatically triggered through multiple channels, including the platform interface, SMS, and email, generating closed-loop processing work orders to ensure asset operational safety.

[0062] S4 applies load analysis and forecast data to support strategies before electricity spot trading;

[0063] The specific steps include:

[0064] S41 predicts and summarizes the electricity purchase demand for future years, months, intra-month periods, and green electricity based on users' historical electricity consumption and real-time load curves.

[0065] S42 integrates and visualizes clearing prices, supply-demand ratios, and system load data in the medium- and long-term electricity market and the spot market.

[0066] S43 integrates real-time load, weather, market boundary and historical price data, and uses machine learning algorithms to train models to predict future time-of-use and regional clearing prices;

[0067] S44 combines load forecasting, electricity price forecasting results, and medium- to long-term contract positions to generate or simulate recommended day-ahead reporting curves and spot trading strategies for different risk levels, and estimates various costs and benefits.

[0068] In this implementation, based on aggregated historical electricity consumption curves and real-time load data, the system intelligently predicts and continuously updates total electricity demand for different periods, including future years, months, intra-month periods, and green electricity, providing a quantitative basis for transaction planning and enabling refined electricity demand forecasting. By integrating and visualizing key market information released by the trading center, such as day-ahead / real-time spot market and medium-to-long-term market clearing prices, supply-demand ratios, and system loads, the system assists traders in grasping market dynamics and achieving multi-dimensional market insights. By integrating multi-source data such as real-time load, weather forecasts, network congestion, unit maintenance, and historical clearing prices, the system utilizes machine learning algorithms to construct and continuously train an electricity price forecast model, outputting time-of-use electricity price forecast curves for future trading days or regions, achieving intelligent electricity price forecasting through data fusion. By combining load forecasts, electricity price forecasts, and existing medium-to-long-term contract holding costs, the system simulates or intelligently recommends day-ahead market order curves and provides spot trading combination strategies with high, medium, and low risk levels, estimating the expected costs, returns, and risk exposures of each strategy, thereby achieving strategy simulation and optimization.

[0069] S5 performs post-trade analysis and strategy optimization based on market clearing and settlement results;

[0070] The specific steps include:

[0071] S51: Compare the actual declaration curve, the system recommended strategy and the actual market clearing settlement results to conduct multi-dimensional deviation analysis and visualize the profit and loss composition review.

[0072] S52: Based on the review conclusions, optimize the load forecasting model, electricity price forecasting algorithm, and trading strategy generation logic;

[0073] S53: Receives daily clearing time-of-use results and monthly settlement statements released by the market, automatically verifies them against aggregated user actual electricity consumption data, identifies electricity consumption deviations, and assists in analyzing the causes.

[0074] In this implementation, by comparing actual declared data, system-recommended strategies, and actual settlement results, a visualized review analysis is conducted from multiple dimensions such as electricity volume, electricity price, and cost. This accurately identifies the sources of profit and loss, quantifies the effectiveness of trading strategies, and enables transaction review and refined profit and loss analysis. By automatically receiving and managing daily clearing time-of-use results and monthly settlement statements released by the market, and by automatically comparing the aggregated and verified actual user electricity consumption data with the settlement statements in batches, deviations in settlement electricity volume can be quickly located and analyzed, greatly improving the efficiency and accuracy of settlement verification and enabling automated verification support for market settlement. By continuously feeding back and optimizing the load forecasting model, electricity price forecasting algorithm, and trading strategy generation logic based on historical transaction review conclusions and profit and loss analysis reports, the overall decision-making intelligence level and economic benefits of the system are improved, enabling continuous iteration of the strategy model.

[0075] S6 provides multi-terminal visualization and interface output of the entire process data, analysis results, and transaction views.

[0076] In this implementation, the data, analysis results, early warning information, and transaction views generated throughout the entire process of steps S1-S5 are displayed in an integrated and graphical manner across multiple terminals, including the platform's comprehensive monitoring dashboard, operation management dashboard, and user-side mobile app. Simultaneously, data services are provided to the enterprise's internal data platform and external regulatory or operational platforms through standardized API interfaces; and customized energy consumption analysis reports, control instruction confirmations, settlement invoices, and market participation revenue summaries are generated for agent users.

[0077] This invention provides a real-time power load data acquisition and analysis method. Through multi-dimensional cleaning, aggregation, and in-depth analysis of real-time data, it not only achieves accurate profiling of user load characteristics, electricity consumption habits, and trends, but also establishes a proactive safety early warning system based on multi-level thresholds. This system can analyze power load characteristics and provide real-time safety warnings, improving the timeliness and accuracy of warnings. This allows operators to more precisely understand and manage aggregated resources, identify potential equipment hazards in advance, and significantly improve the safety and reliability of asset operation. This invention deeply integrates real-time load data, forecasting information, and market mechanisms. Through automated post-transaction review and profit / loss attribution analysis, this invention can quickly assess the effectiveness of strategies, locate risk sources, and, combined with automatic settlement verification, form a full-cycle risk management system covering pre-, during, and post-transaction periods, effectively reducing market transaction risks and improving economic efficiency.

[0078] Secondly, please refer to Figure 6 The present invention also provides a real-time power load data acquisition and analysis system, including a data acquisition and transmission module 101, a data processing and aggregation module 102, a load analysis and early warning module 103, a transaction decision support module 104, a transaction review and optimization module 105, and a visualization output module 106; the data acquisition and transmission module 101, the data processing and aggregation module 102, the load analysis and early warning module 103, the transaction decision support module 104, the transaction review and optimization module 105, and the visualization output module 106 are connected in sequence;

[0079] The data acquisition and transmission module 101 is used to collect real-time power load data through an IoT terminal deployed on the user side and upload it through a preset communication protocol.

[0080] The data processing and aggregation module 102 is used to receive the uploaded real-time power load data, clean and standardize the real-time power load data, and store it after aggregation according to preset dimensions.

[0081] The load analysis and early warning module 103 is used to perform load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data;

[0082] The transaction decision support module 104 is used to apply load analysis and forecast data to pre-trade strategy support for electricity spot transactions.

[0083] The transaction review and optimization module 105 is used to perform post-transaction analysis and strategy optimization based on market clearing and settlement results;

[0084] The visualization output module 106 is used to visualize and output the entire process data, analysis results, and transaction views across multiple terminals.

[0085] In this embodiment, the data acquisition and transmission module 101 collects key operational data such as active power, three-phase current and voltage, and equipment temperature in real time through intelligent measurement terminals and sensors deployed on diverse resource sides such as users and energy storage power stations; it uses reliable communication protocols such as MQTT and HTTPS to stably upload encrypted real-time data streams. The data processing and aggregation module 102 cleans the received raw real-time power load data, removing outliers and invalid records; it performs timestamp alignment and unit unification standardization processing on the cleaned real-time power load data; and it aggregates and calculates the standardized data according to resource type, user category, and time granularity to generate an aggregated data view and stores it in a time-series database.

[0086] The load analysis and early warning module 103 draws typical daily, weekly, and monthly load curves based on aggregated data, identifies peak and valley periods and trends of load; sets multi-level safety thresholds for power, current, and temperature, monitors over-limit states in real time and triggers early warnings; and constructs user electricity consumption profiles based on historical electricity consumption behavior, providing load forecasting and energy efficiency assessment suggestions.

[0087] The transaction decision support module 104 predicts and summarizes future annual, monthly, intra-month, and green electricity purchase demands based on users' historical electricity consumption and real-time load curves; it integrates and visualizes the clearing prices, supply-demand ratios, and system load data of the medium- and long-term electricity market and the spot market; it integrates real-time load, meteorological, market boundary, and historical price data, uses machine learning algorithms to train models, and predicts future time-of-use and regional clearing prices; it combines load forecasting, electricity price forecasting results, and medium- and long-term contract positions to generate or simulate recommended day-ahead declaration curves and spot trading strategies with different risk levels, and estimates various costs and benefits. The transaction review and optimization module 105 performs multi-dimensional deviation analysis and visualizes profit and loss composition review by comparing actual declaration curves, system recommended strategies, and actual market clearing settlement results; based on the review conclusions, it optimizes the load forecasting model, electricity price forecasting algorithm, and transaction strategy generation logic; it receives daily clearing time-of-use results and monthly settlement statements released by the market, automatically verifies them with aggregated user actual electricity consumption data, identifies electricity deviations, and assists in analyzing the causes. The visualization output module 106 displays all data, analysis results, early warning information, and transaction views generated by the entire system in an integrated and graphical manner through multiple terminals such as the platform's comprehensive monitoring dashboard, operation management screen, and user-side mobile app.

[0088] The present invention discloses a real-time power load data acquisition and analysis system. Through multi-dimensional cleaning, aggregation and in-depth analysis of real-time data, it not only achieves accurate profiling of user load characteristics, electricity consumption habits and trends, but also establishes an active safety early warning system based on multi-level thresholds. It can analyze power load characteristics and provide real-time safety early warnings, thereby improving the timeliness and accuracy of early warnings.

[0089] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A method for real-time data acquisition and analysis of power load, characterized in that, include: Real-time power load data is collected by IoT terminals deployed on the user side and uploaded via a preset communication protocol; Receive uploaded real-time power load data, clean and standardize the real-time power load data, and store it after aggregating it according to preset dimensions; Based on historical and real-time aggregated data, load characteristic analysis and real-time safety early warning are performed; Apply load analysis and forecasting data to support strategies before electricity spot trading; Based on market clearing and settlement results, conduct post-trade analysis and strategy optimization; The entire process data, analysis results, and transaction views are visualized and output via multiple platforms and interfaces.

2. The method for real-time data acquisition and analysis of power load as described in claim 1, characterized in that, In the step of collecting real-time power load data through IoT terminals deployed on the user side and uploading it via a preset communication protocol, The real-time power load data includes active power, three-phase current, voltage, and equipment temperature.

3. The method for real-time data acquisition and analysis of power load as described in claim 2, characterized in that, The steps involve collecting real-time power load data through IoT terminals deployed on the user side and uploading it via a preset communication protocol. The preset communication protocol is either MQTT or HTTPS, and the data transmission process implements integrity verification and communication status monitoring.

4. The method for real-time data acquisition and analysis of power load as described in claim 3, characterized in that, The specific steps for receiving uploaded real-time power load data, cleaning and standardizing the real-time power load data, and then storing it after aggregation according to preset dimensions include: The received raw real-time power load data is cleaned to remove outliers and invalid records; The real-time power load data after cleaning is standardized by aligning timestamps and unifying units. Standardized data is aggregated and calculated according to resource type, user category, and time granularity to generate an aggregated data view and store it in a time series database.

5. The method for real-time data acquisition and analysis of power load as described in claim 4, characterized in that, The specific steps for load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data include: Typical daily, weekly, and monthly load curves are plotted based on aggregated data to identify peak and trough periods and trends in load. Set multi-level safety thresholds for power, current, and temperature, and monitor and trigger early warnings in real time for exceeding the limits; Based on historical electricity consumption behavior, user electricity consumption profiles are constructed to provide load forecasting and energy efficiency assessment suggestions.

6. The method for real-time data acquisition and analysis of power load as described in claim 5, characterized in that, The specific steps for applying load analysis and forecasting data to support strategies before electricity spot trading include: Based on users' historical electricity consumption and real-time load curves, predict and summarize the electricity purchase demand for future years, months, intra-month periods, and green electricity. It integrates and visualizes data on clearing prices, supply-demand ratios, and system load conditions in the medium- and long-term electricity market and the spot market. By integrating real-time load, weather, market boundary, and historical price data, and using machine learning algorithms to train models, future time-of-use and regional clearing prices are predicted. By combining load forecasts, electricity price forecasts, and medium- to long-term contract positions, we can generate or simulate and recommend day-ahead reporting curves and spot trading strategies for different risk levels, and estimate various costs and benefits.

7. A real-time power load data acquisition and analysis system, employing the real-time power load data acquisition and analysis method as described in any one of claims 1-6, characterized in that, It includes a data acquisition and transmission module, a data processing and aggregation module, a load analysis and early warning module, a transaction decision support module, a transaction review and optimization module, and a visualization output module; the data acquisition and transmission module, the data processing and aggregation module, the load analysis and early warning module, the transaction decision support module, the transaction review and optimization module, and the visualization output module are connected in sequence; The data acquisition and transmission module is used to collect real-time power load data through IoT terminals deployed on the user side and upload it through a preset communication protocol; The data processing and aggregation module is used to receive the uploaded real-time power load data, clean and standardize the real-time power load data, and store it after aggregation according to preset dimensions. The load analysis and early warning module is used to perform load characteristic analysis and real-time safety early warning based on historical and real-time aggregated data; The transaction decision support module is used to apply load analysis and forecast data to pre-trade strategy support for electricity spot transactions. The transaction review and optimization module is used to perform post-trade analysis and strategy optimization based on market clearing and settlement results; The visualization output module is used to visualize and output the entire process data, analysis results, and transaction views across multiple platforms.

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